Amy B. Guo, Deniz Akpinaroglu, Christina A. Stephens, Michael Grabe, Colin A. Smith, Mark J. S. Kelly, Tanja Kortemme
Deep learning–guided design of dynamic proteins Journal Article
In: Science, vol. 388, no. 6749, pp. eadr7094, 2025.
@article{doi:10.1126/science.adr7094,
title = {Deep learning\textendashguided design of dynamic proteins},
author = {Amy B. Guo and Deniz Akpinaroglu and Christina A. Stephens and Michael Grabe and Colin A. Smith and Mark J. S. Kelly and Tanja Kortemme},
url = {https://www.science.org/doi/abs/10.1126/science.adr7094},
doi = {10.1126/science.adr7094},
year = {2025},
date = {2025-01-01},
journal = {Science},
volume = {388},
number = {6749},
pages = {eadr7094},
abstract = {Deep learning has advanced the design of static protein structures, but the controlled conformational changes that are hallmarks of natural signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep learning\textendashguided approach for de novo design of dynamic changes between intradomain geometries of proteins, similar to switch mechanisms prevalent in nature, with atomic-level precision. We solve four structures that validate the designed conformations, demonstrate modulation of the conformational landscape by orthosteric ligands and allosteric mutations, and show that physics-based simulations are in agreement with deep-learning predictions and experimental data. Our approach demonstrates that new modes of motion can now be realized through de novo design and provides a framework for constructing biology-inspired, tunable, and controllable protein signaling behavior de novo. Many signaling proteins and enzymes respond to binding of a small molecule or ion by shifting dynamics to favor one structural conformation over another. This behavior is key to biological function, but engineering these properties in designed proteins is very challenging. Guo et al. developed a computational approach to designing such dynamic proteins that can sense and respond to binding of a calcium ion. Starting with a static protein that binds a calcium ion, the authors identified potential alternate conformations and used AlphaFold2 predictions to identify sequences that were compatible with both structures. Validation with molecular dynamics simulations and nuclear magnetic resonance experiments confirmed a dynamic, multistate design that could be shifted into a single conformation upon ion binding.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Deep learning has advanced the design of static protein structures, but the controlled conformational changes that are hallmarks of natural signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep learning–guided approach for de novo design of dynamic changes between intradomain geometries of proteins, similar to switch mechanisms prevalent in nature, with atomic-level precision. We solve four structures that validate the designed conformations, demonstrate modulation of the conformational landscape by orthosteric ligands and allosteric mutations, and show that physics-based simulations are in agreement with deep-learning predictions and experimental data. Our approach demonstrates that new modes of motion can now be realized through de novo design and provides a framework for constructing biology-inspired, tunable, and controllable protein signaling behavior de novo. Many signaling proteins and enzymes respond to binding of a small molecule or ion by shifting dynamics to favor one structural conformation over another. This behavior is key to biological function, but engineering these properties in designed proteins is very challenging. Guo et al. developed a computational approach to designing such dynamic proteins that can sense and respond to binding of a calcium ion. Starting with a static protein that binds a calcium ion, the authors identified potential alternate conformations and used AlphaFold2 predictions to identify sequences that were compatible with both structures. Validation with molecular dynamics simulations and nuclear magnetic resonance experiments confirmed a dynamic, multistate design that could be shifted into a single conformation upon ion binding.
Nabiha R. Syed, Nafisa B. Masud, Colin A. Smith
Analysis of chi angle distributions in free amino acids via multiplet fitting of proton scalar couplings Journal Article
In: Magnetic Resonance, vol. 5, no. 2, pp. 103–120, 2024.
@article{mr-2024-7,
title = {Analysis of chi angle distributions in free amino acids via multiplet fitting of proton scalar couplings},
author = {Nabiha R. Syed and Nafisa B. Masud and Colin A. Smith},
url = {https://mr.copernicus.org/articles/5/103/2024/},
doi = {10.5194/mr-5-103-2024},
year = {2024},
date = {2024-08-19},
urldate = {2024-08-19},
journal = {Magnetic Resonance},
volume = {5},
number = {2},
pages = {103\textendash120},
abstract = {Scalar couplings are a fundamental aspect of nuclear magnetic resonance (NMR) experiments and provide rich information about electron-mediated interactions between nuclei. 3J couplings are particularly useful for determining molecular structure through the Karplus relationship, a mathematical formula used for calculating 3J coupling constants from dihedral angles. In small molecules, scalar couplings are often determined through analysis of one-dimensional proton spectra. Larger proteins have typically required specialized multidimensional pulse programs designed to overcome spectral crowding and multiplet complexity. Here, we present a generalized framework for fitting scalar couplings with arbitrarily complex multiplet patterns using a weak-coupling model. The method is implemented in FitNMR and applicable to one-dimensional, two-dimensional, and three-dimensional NMR spectra. To gain insight into the proton\textendashproton coupling patterns present in protein side chains, we analyze a set of free amino acid one-dimensional spectra. We show that the weak-coupling assumption is largely sufficient for fitting the majority of resonances, although there are notable exceptions. To enable structural interpretation of all couplings, we extend generalized and self-consistent Karplus equation parameterizations to all χ angles. An enhanced model of side-chain motion incorporating rotamer statistics from the Protein Data Bank (PDB) is developed. Even without stereospecific assignments of the beta hydrogens, we find that two couplings are sufficient to exclude a single-rotamer model for all amino acids except proline. While most free amino acids show rotameric populations consistent with crystal structure statistics, beta-branched valine and isoleucine deviate substantially.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Scalar couplings are a fundamental aspect of nuclear magnetic resonance (NMR) experiments and provide rich information about electron-mediated interactions between nuclei. 3J couplings are particularly useful for determining molecular structure through the Karplus relationship, a mathematical formula used for calculating 3J coupling constants from dihedral angles. In small molecules, scalar couplings are often determined through analysis of one-dimensional proton spectra. Larger proteins have typically required specialized multidimensional pulse programs designed to overcome spectral crowding and multiplet complexity. Here, we present a generalized framework for fitting scalar couplings with arbitrarily complex multiplet patterns using a weak-coupling model. The method is implemented in FitNMR and applicable to one-dimensional, two-dimensional, and three-dimensional NMR spectra. To gain insight into the proton–proton coupling patterns present in protein side chains, we analyze a set of free amino acid one-dimensional spectra. We show that the weak-coupling assumption is largely sufficient for fitting the majority of resonances, although there are notable exceptions. To enable structural interpretation of all couplings, we extend generalized and self-consistent Karplus equation parameterizations to all χ angles. An enhanced model of side-chain motion incorporating rotamer statistics from the Protein Data Bank (PDB) is developed. Even without stereospecific assignments of the beta hydrogens, we find that two couplings are sufficient to exclude a single-rotamer model for all amino acids except proline. While most free amino acids show rotameric populations consistent with crystal structure statistics, beta-branched valine and isoleucine deviate substantially.
Joshua A. Dudley, Sojeong Park, Oliver Cho, Nicholas G. M. Wells, Meagan E. MacDonald, Katerina M. Blejec, Emmanuel Fetene, Eric Zanderigo, Scott Houliston, Jennifer C. Liddle, Chad M. Dashnaw, T. Michael Sabo, Bryan F. Shaw, Jeremy L. Balsbaugh, Gabriel J. Rocklin, Colin A. Smith
Heat-induced structural and chemical changes to a computationally designed miniprotein Journal Article
In: Protein Sci, vol. 33, no. 6, pp. e4991, 2024.
@article{Dudley:2024aa,
title = {Heat-induced structural and chemical changes to a computationally designed miniprotein},
author = {Joshua A. Dudley and Sojeong Park and Oliver Cho and Nicholas G. M. Wells and Meagan E. MacDonald and Katerina M. Blejec and Emmanuel Fetene and Eric Zanderigo and Scott Houliston and Jennifer C. Liddle and Chad M. Dashnaw and T. Michael Sabo and Bryan F. Shaw and Jeremy L. Balsbaugh and Gabriel J. Rocklin and Colin A. Smith},
doi = {https://doi.org/10.1002/pro.4991},
year = {2024},
date = {2024-03-24},
urldate = {2024-03-24},
journal = {Protein Sci},
volume = {33},
number = {6},
pages = {e4991},
publisher = {John Wiley \& Sons, Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Meagan E. MacDonald, Nicholas G. M. Wells, Bakar A. Hassan, Joshua A. Dudley, Kylie J. Walters, Dmitry M. Korzhnev, James M. Aramini, Colin A. Smith
Effects of Xylanase A double mutation on substrate specificity and structural dynamics Journal Article
In: J Struct Biol, vol. 216, no. 2, pp. 108082, 2024.
@article{MacDonald:2024aa,
title = {Effects of Xylanase A double mutation on substrate specificity and structural dynamics},
author = {Meagan E. MacDonald and Nicholas G. M. Wells and Bakar A. Hassan and Joshua A. Dudley and Kylie J. Walters and Dmitry M. Korzhnev and James M. Aramini and Colin A. Smith},
url = {https://www.sciencedirect.com/science/article/pii/S1047847724000224},
doi = {https://doi.org/10.1016/j.jsb.2024.108082},
year = {2024},
date = {2024-03-02},
urldate = {2024-03-02},
journal = {J Struct Biol},
volume = {216},
number = {2},
pages = {108082},
abstract = {While protein activity is traditionally studied with a major focus on the active site, the activity of enzymes has been hypothesized to be linked to the flexibility of adjacent regions, warranting more exploration into how the dynamics in these regions affects catalytic turnover. One such enzyme is Xylanase A (XylA), which cleaves hemicellulose xylan polymers by hydrolysis at internal β-1,4-xylosidic linkages. It contains a ``thumb''region whose flexibility has been suggested to affect the activity. The double mutation D11F/R122D was previously found to affect activity and potentially bias the thumb region to a more open conformation. We find that the D11F/R122D double mutation shows substrate-dependent effects, increasing activity on the non-native substrate ONPX2 but decreasing activity on its native xylan substrate. To characterize how the double mutant causes these kinetics changes, nuclear magnetic resonance (NMR) and molecular dynamics (MD) simulations were used to probe structural and flexibility changes. NMR chemical shift perturbations revealed structural changes in the double mutant relative to the wild-type, specifically in the thumb and fingers regions. Increased slow-timescale dynamics in the fingers region was observed as intermediate-exchange line broadening. Lipari-Szabo order parameters show negligible changes in flexibility in the thumb region in the presence of the double mutation. To help understand if there is increased energetic accessibility to the open state upon mutation, alchemical free energy simulations were employed that indicated thumb opening is more favorable in the double mutant. These studies aid in further characterizing how flexibility in adjacent regions affects the function of XylA.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
While protein activity is traditionally studied with a major focus on the active site, the activity of enzymes has been hypothesized to be linked to the flexibility of adjacent regions, warranting more exploration into how the dynamics in these regions affects catalytic turnover. One such enzyme is Xylanase A (XylA), which cleaves hemicellulose xylan polymers by hydrolysis at internal β-1,4-xylosidic linkages. It contains a ``thumb''region whose flexibility has been suggested to affect the activity. The double mutation D11F/R122D was previously found to affect activity and potentially bias the thumb region to a more open conformation. We find that the D11F/R122D double mutation shows substrate-dependent effects, increasing activity on the non-native substrate ONPX2 but decreasing activity on its native xylan substrate. To characterize how the double mutant causes these kinetics changes, nuclear magnetic resonance (NMR) and molecular dynamics (MD) simulations were used to probe structural and flexibility changes. NMR chemical shift perturbations revealed structural changes in the double mutant relative to the wild-type, specifically in the thumb and fingers regions. Increased slow-timescale dynamics in the fingers region was observed as intermediate-exchange line broadening. Lipari-Szabo order parameters show negligible changes in flexibility in the thumb region in the presence of the double mutation. To help understand if there is increased energetic accessibility to the open state upon mutation, alchemical free energy simulations were employed that indicated thumb opening is more favorable in the double mutant. These studies aid in further characterizing how flexibility in adjacent regions affects the function of XylA.
Nicholas G. M. Wells, Colin A. Smith
Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta Journal Article
In: Proteins, vol. 91, no. 7, pp. 920-932, 2023.
@article{Wells:2023,
title = {Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta},
author = {Nicholas G. M. Wells and Colin A. Smith},
doi = {10.1002/prot.26477},
year = {2023},
date = {2023-02-09},
urldate = {2023-02-09},
journal = {Proteins},
volume = {91},
number = {7},
pages = {920-932},
abstract = {Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.
Emma R. Hostetter, Jeffrey R. Keyes, Ivy Poon, Justin P. Nguyen, Jacob M. Nite, Carlos A. Jimenez Hoyos, Colin A. Smith
Prediction of Fluorophore Brightness in Designed Mini Fluorescence Activating Proteins Journal Article
In: Journal of Chemical Theory and Computation, 2022.
@article{Hostetter:2022,
title = {Prediction of Fluorophore Brightness in Designed Mini Fluorescence Activating Proteins},
author = {Emma R. Hostetter and Jeffrey R. Keyes and Ivy Poon and Justin P. Nguyen and Jacob M. Nite and Carlos A. Jimenez Hoyos and Colin A. Smith},
url = {https://doi.org/10.1021/acs.jctc.1c00748},
doi = {10.1021/acs.jctc.1c00748},
year = {2022},
date = {2022-04-13},
urldate = {2022-04-13},
journal = {Journal of Chemical Theory and Computation},
publisher = {American Chemical Society},
abstract = {The de novo computational design of proteins with predefined three-dimensional structure is becoming much more routine due to advancements both in force fields and algorithms. However, creating designs with functions beyond folding is more challenging. In that regard, the recent design of small beta barrel proteins that activate the fluorescence of an exogenous small molecule chromophore (DFHBI) is noteworthy. These proteins, termed mini fluorescence activating proteins (mFAPs), have been shown to increase the brightness of the chromophore more than 100-fold upon binding to the designed ligand pocket. The design process created a large library of variants with different brightness levels but gave no rational explanation for why one variant was brighter than another. Here, we use quantum mechanics and molecular dynamics simulations to investigate how molecular flexibility in the ground and excited states influences brightness. We show that the ability of the protein to resist dihedral angle rotation of the chromophore is critical for predicting brightness. Our simulations suggest that the mFAP/DFHBI complex has a rough energy landscape, requiring extensive ground-state sampling to achieve converged predictions of excited-state kinetics. While computationally demanding, this roughness suggests that mFAP protein function can be enhanced by reshaping the energy landscape toward conformations that better resist DFHBI bond rotation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The de novo computational design of proteins with predefined three-dimensional structure is becoming much more routine due to advancements both in force fields and algorithms. However, creating designs with functions beyond folding is more challenging. In that regard, the recent design of small beta barrel proteins that activate the fluorescence of an exogenous small molecule chromophore (DFHBI) is noteworthy. These proteins, termed mini fluorescence activating proteins (mFAPs), have been shown to increase the brightness of the chromophore more than 100-fold upon binding to the designed ligand pocket. The design process created a large library of variants with different brightness levels but gave no rational explanation for why one variant was brighter than another. Here, we use quantum mechanics and molecular dynamics simulations to investigate how molecular flexibility in the ground and excited states influences brightness. We show that the ability of the protein to resist dihedral angle rotation of the chromophore is critical for predicting brightness. Our simulations suggest that the mFAP/DFHBI complex has a rough energy landscape, requiring extensive ground-state sampling to achieve converged predictions of excited-state kinetics. While computationally demanding, this roughness suggests that mFAP protein function can be enhanced by reshaping the energy landscape toward conformations that better resist DFHBI bond rotation.
Nicholas G M Wells, Grant A Tillinghast, Alison L O'Neil, Colin A Smith
Free Energy Calculations of ALS-Causing SOD1 Mutants Reveal Common Perturbations to Stability and Dynamics along the Maturation Pathway Journal Article
In: Protein Sci, 2021.
@article{SOD1:2021,
title = {Free Energy Calculations of ALS-Causing SOD1 Mutants Reveal Common Perturbations to Stability and Dynamics along the Maturation Pathway},
author = {Nicholas G M Wells and Grant A Tillinghast and Alison L O'Neil and Colin A Smith},
doi = {10.1002/pro.4132},
year = {2021},
date = {2021-01-01},
journal = {Protein Sci},
abstract = {With over 150 heritable mutations identified as disease-causative, superoxide dismutase 1 (SOD1) has been a main target of amyotrophic lateral sclerosis (ALS) research and therapeutic efforts. However, recent evidence has suggested that neither loss of function nor protein aggregation is responsible for promoting neurotoxicity. Furthermore, there is no clear pattern to the nature or the location of these mutations that could suggest a molecular mechanism behind SOD1-linked ALS. Here, we utilize reliable and accurate computational techniques to predict the perturbations of 10 such mutations on the free energy changes of SOD1 as it matures from apo monomer to metallated dimer. We find that the free energy perturbations caused by these mutations strongly depend on maturational progress, indicating the need for state-specific therapeutic targeting. We also find that many mutations exhibit similar patterns of perturbation to native and non-native maturation, indicating strong thermodynamic coupling between the dynamics at various sites of maturation within SOD1. These results suggest the presence of an allosteric network in SOD1 which is vulnerable to disruption by these mutations. Analysis of these perturbations may contribute to uncovering a unifying molecular mechanism which explains SOD1-linked ALS and help to guide future therapeutic efforts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With over 150 heritable mutations identified as disease-causative, superoxide dismutase 1 (SOD1) has been a main target of amyotrophic lateral sclerosis (ALS) research and therapeutic efforts. However, recent evidence has suggested that neither loss of function nor protein aggregation is responsible for promoting neurotoxicity. Furthermore, there is no clear pattern to the nature or the location of these mutations that could suggest a molecular mechanism behind SOD1-linked ALS. Here, we utilize reliable and accurate computational techniques to predict the perturbations of 10 such mutations on the free energy changes of SOD1 as it matures from apo monomer to metallated dimer. We find that the free energy perturbations caused by these mutations strongly depend on maturational progress, indicating the need for state-specific therapeutic targeting. We also find that many mutations exhibit similar patterns of perturbation to native and non-native maturation, indicating strong thermodynamic coupling between the dynamics at various sites of maturation within SOD1. These results suggest the presence of an allosteric network in SOD1 which is vulnerable to disruption by these mutations. Analysis of these perturbations may contribute to uncovering a unifying molecular mechanism which explains SOD1-linked ALS and help to guide future therapeutic efforts.
Joshua A Dudley, Sojeong Park, Meagan E MacDonald, Emanual Fetene, Colin A Smith
Resolving overlapped signals with automated FitNMR analytical peak modeling Journal Article
In: J Magn Reson, vol. 318, pp. 106773, 2020.
@article{Dudley:2020,
title = {Resolving overlapped signals with automated FitNMR analytical peak modeling},
author = {Joshua A Dudley and Sojeong Park and Meagan E MacDonald and Emanual Fetene and Colin A Smith},
doi = {10.1016/j.jmr.2020.106773},
year = {2020},
date = {2020-09-01},
journal = {J Magn Reson},
volume = {318},
pages = {106773},
abstract = {Nuclear magnetic resonance (NMR) is a valuable tool for determining the structures of molecules and probing their dynamics. A longstanding problem facing both small-molecule and macromolecular NMR is overlapped signals in crowded spectra. To address this, we have developed a method that extracts peak features by fitting analytically derived models of NMR lineshapes. The approach takes into account the effects of truncation, apodization, and the resulting artifacts, while avoiding systematic errors that have affected other models. Even severely overlapped peaks, beyond the point of coalescence, can be distinguished in both simulated and experimental data. We show that the method can measure unresolved backbone scalar couplings directly from a 2D proton-nitrogen spectrum of a de novo designed mini protein. The algorithm is implemented in the FitNMR open-source R package and can be used to analyze nearly any type of single or multidimensional data from small molecules or biomolecules.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nuclear magnetic resonance (NMR) is a valuable tool for determining the structures of molecules and probing their dynamics. A longstanding problem facing both small-molecule and macromolecular NMR is overlapped signals in crowded spectra. To address this, we have developed a method that extracts peak features by fitting analytically derived models of NMR lineshapes. The approach takes into account the effects of truncation, apodization, and the resulting artifacts, while avoiding systematic errors that have affected other models. Even severely overlapped peaks, beyond the point of coalescence, can be distinguished in both simulated and experimental data. We show that the method can measure unresolved backbone scalar couplings directly from a 2D proton-nitrogen spectrum of a de novo designed mini protein. The algorithm is implemented in the FitNMR open-source R package and can be used to analyze nearly any type of single or multidimensional data from small molecules or biomolecules.
Julia Koehler Leman, Brian D Weitzner, Douglas P Renfrew, Steven M Lewis, Rocco Moretti, Andrew M Watkins, Vikram Khipple Mulligan, Sergey Lyskov, Jared Adolf-Bryfogle, Jason W Labonte, Justyna Krys, Christopher Bystroff, William Schief, Dominik Gront, Ora Schueler-Furman, David Baker, Philip Bradley, Roland Dunbrack, Tanja Kortemme, Andrew Leaver-Fay, Charlie E M Strauss, Jens Meiler, Brian Kuhlman, Jeffrey J Gray, Richard Bonneau
Better together: Elements of successful scientific software development in a distributed collaborative community Journal Article
In: PLoS Comput Biol, vol. 16, no. 5, pp. e1007507, 2020.
@article{Koehler-Leman:2020,
title = {Better together: Elements of successful scientific software development in a distributed collaborative community},
author = {Julia Koehler Leman and Brian D Weitzner and Douglas P Renfrew and Steven M Lewis and Rocco Moretti and Andrew M Watkins and Vikram Khipple Mulligan and Sergey Lyskov and Jared Adolf-Bryfogle and Jason W Labonte and Justyna Krys and Christopher Bystroff and William Schief and Dominik Gront and Ora Schueler-Furman and David Baker and Philip Bradley and Roland Dunbrack and Tanja Kortemme and Andrew Leaver-Fay and Charlie E M Strauss and Jens Meiler and Brian Kuhlman and Jeffrey J Gray and Richard Bonneau},
doi = {10.1371/journal.pcbi.1007507},
year = {2020},
date = {2020-05-01},
journal = {PLoS Comput Biol},
volume = {16},
number = {5},
pages = {e1007507},
abstract = {Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.
Colin A Smith, Adam Mazur, Ashok K Rout, Stefan Becker, Donghan Lee, Bert L de Groot, Christian Griesinger
Enhancing NMR derived ensembles with kinetics on multiple timescales Journal Article
In: J Biomol NMR, vol. 74, no. 1, pp. 27–43, 2020.
@article{Smith:2020,
title = {Enhancing NMR derived ensembles with kinetics on multiple timescales},
author = {Colin A Smith and Adam Mazur and Ashok K Rout and Stefan Becker and Donghan Lee and Bert L de Groot and Christian Griesinger},
doi = {10.1007/s10858-019-00288-8},
year = {2020},
date = {2020-01-01},
journal = {J Biomol NMR},
volume = {74},
number = {1},
pages = {27--43},
abstract = {Nuclear magnetic resonance (NMR) has the unique advantage of elucidating the structure and dynamics of biomolecules in solution at physiological temperatures, where they are in constant movement on timescales from picoseconds to milliseconds. Such motions have been shown to be critical for enzyme catalysis, allosteric regulation, and molecular recognition. With NMR being particularly sensitive to these timescales, detailed information about the kinetics can be acquired. However, nearly all methods of NMR-based biomolecular structure determination neglect kinetics, which introduces a large approximation to the underlying physics, limiting both structural resolution and the ability to accurately determine molecular flexibility. Here we present the Kinetic Ensemble approach that uses a hierarchy of interconversion rates between a set of ensemble members to rigorously calculate Nuclear Overhauser Effect (NOE) intensities. It can be used to simultaneously refine both temporal and structural coordinates. By generalizing ideas from the extended model free approach, the method can analyze the amplitudes and kinetics of motions anywhere along the backbone or side chains. Furthermore, analysis of a large set of crystal structures suggests that NOE data contains a surprising amount of high-resolution information that is better modeled using our approach. The Kinetic Ensemble approach provides the means to unify numerous types of experiments under a single quantitative framework and more fully characterize and exploit kinetically distinct protein states. While we apply the approach here to the protein ubiquitin and cross validate it with previously derived datasets, the approach can be applied to any protein for which NOE data is available.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nuclear magnetic resonance (NMR) has the unique advantage of elucidating the structure and dynamics of biomolecules in solution at physiological temperatures, where they are in constant movement on timescales from picoseconds to milliseconds. Such motions have been shown to be critical for enzyme catalysis, allosteric regulation, and molecular recognition. With NMR being particularly sensitive to these timescales, detailed information about the kinetics can be acquired. However, nearly all methods of NMR-based biomolecular structure determination neglect kinetics, which introduces a large approximation to the underlying physics, limiting both structural resolution and the ability to accurately determine molecular flexibility. Here we present the Kinetic Ensemble approach that uses a hierarchy of interconversion rates between a set of ensemble members to rigorously calculate Nuclear Overhauser Effect (NOE) intensities. It can be used to simultaneously refine both temporal and structural coordinates. By generalizing ideas from the extended model free approach, the method can analyze the amplitudes and kinetics of motions anywhere along the backbone or side chains. Furthermore, analysis of a large set of crystal structures suggests that NOE data contains a surprising amount of high-resolution information that is better modeled using our approach. The Kinetic Ensemble approach provides the means to unify numerous types of experiments under a single quantitative framework and more fully characterize and exploit kinetically distinct protein states. While we apply the approach here to the protein ubiquitin and cross validate it with previously derived datasets, the approach can be applied to any protein for which NOE data is available.
Petra Rovó, Colin A Smith, Diego Gauto, Bert L de Groot, Paul Schanda, Rasmus Linser
Mechanistic Insights into Microsecond Time-Scale Motion of Solid Proteins Using Complementary (15)N and (1)H Relaxation Dispersion Techniques Journal Article
In: J Am Chem Soc, vol. 141, no. 2, pp. 858–869, 2019.
@article{Rovo:2019:J-Am-Chem-Soc:30620186,
title = {Mechanistic Insights into Microsecond Time-Scale Motion of Solid Proteins Using Complementary (15)N and (1)H Relaxation Dispersion Techniques},
author = {Petra Rov\'{o} and Colin A Smith and Diego Gauto and Bert L de Groot and Paul Schanda and Rasmus Linser},
doi = {10.1021/jacs.8b09258},
year = {2019},
date = {2019-01-01},
journal = {J Am Chem Soc},
volume = {141},
number = {2},
pages = {858--869},
abstract = {NMR relaxation dispersion methods provide a holistic way to observe microsecond time-scale protein backbone motion both in solution and in the solid state. Different nuclei ((1)H and (15)N) and different relaxation dispersion techniques (Bloch-McConnell and near-rotary-resonance) give complementary information about the amplitudes and time scales of the conformational dynamics and provide comprehensive insights into the mechanistic details of the structural rearrangements. In this paper, we exemplify the benefits of the combination of various solution- and solid-state relaxation dispersion methods on a microcrystalline protein (α-spectrin SH3 domain), for which we are able to identify and model the functionally relevant conformational rearrangements around the ligand recognition loop occurring on multiple microsecond time scales. The observed loop motions suggest that the SH3 domain exists in a binding-competent conformation in dynamic equilibrium with a sterically impaired ground-state conformation both in solution and in crystalline form. This inherent plasticity between the interconverting macrostates is compatible with a conformational-preselection model and provides new insights into the recognition mechanisms of SH3 domains.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
NMR relaxation dispersion methods provide a holistic way to observe microsecond time-scale protein backbone motion both in solution and in the solid state. Different nuclei ((1)H and (15)N) and different relaxation dispersion techniques (Bloch-McConnell and near-rotary-resonance) give complementary information about the amplitudes and time scales of the conformational dynamics and provide comprehensive insights into the mechanistic details of the structural rearrangements. In this paper, we exemplify the benefits of the combination of various solution- and solid-state relaxation dispersion methods on a microcrystalline protein (α-spectrin SH3 domain), for which we are able to identify and model the functionally relevant conformational rearrangements around the ligand recognition loop occurring on multiple microsecond time scales. The observed loop motions suggest that the SH3 domain exists in a binding-competent conformation in dynamic equilibrium with a sterically impaired ground-state conformation both in solution and in crystalline form. This inherent plasticity between the interconverting macrostates is compatible with a conformational-preselection model and provides new insights into the recognition mechanisms of SH3 domains.
D Ban, C A Smith, B L de Groot, C Griesinger, D Lee
Recent advances in measuring the kinetics of biomolecules by NMR relaxation dispersion spectroscopy Journal Article
In: Arch Biochem Biophys, vol. 628, pp. 81-91, 2017.
@article{Ban:2017:Arch-Biochem-Biophys:28576576,
title = {Recent advances in measuring the kinetics of biomolecules by NMR relaxation dispersion spectroscopy},
author = {D Ban and C A Smith and B L de Groot and C Griesinger and D Lee},
url = {http://www.hubmed.org/display.cgi?uids=28576576},
doi = {10.1016/j.abb.2017.05.016},
year = {2017},
date = {2017-01-01},
journal = {Arch Biochem Biophys},
volume = {628},
pages = {81-91},
abstract = {Protein function can be modulated or dictated by the amplitude and timescale of biomolecular motion, therefore it is imperative to study protein dynamics. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technique capable of studying timescales of motion that range from those faster than molecular reorientation on the picosecond timescale to those that occur in real-time. Across this entire regime, NMR observables can report on the amplitude of atomic motion, and the kinetics of atomic motion can be ascertained with a wide variety of experimental techniques from real-time to milliseconds and several nanoseconds to picoseconds. Still a four orders of magnitude window between several nanoseconds and tens of microseconds has remained elusive. Here, we highlight new relaxation dispersion NMR techniques that serve to cover this "hidden-time" window up to hundreds of nanoseconds that achieve atomic resolution while studying the molecule under physiological conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Protein function can be modulated or dictated by the amplitude and timescale of biomolecular motion, therefore it is imperative to study protein dynamics. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technique capable of studying timescales of motion that range from those faster than molecular reorientation on the picosecond timescale to those that occur in real-time. Across this entire regime, NMR observables can report on the amplitude of atomic motion, and the kinetics of atomic motion can be ascertained with a wide variety of experimental techniques from real-time to milliseconds and several nanoseconds to picoseconds. Still a four orders of magnitude window between several nanoseconds and tens of microseconds has remained elusive. Here, we highlight new relaxation dispersion NMR techniques that serve to cover this "hidden-time" window up to hundreds of nanoseconds that achieve atomic resolution while studying the molecule under physiological conditions.
C A Smith, D Ban, S Pratihar, K Giller, M Paulat, S Becker, C Griesinger, D Lee, B L de Groot
Allosteric switch regulates protein-protein binding through collective motion Journal Article
In: Proc Natl Acad Sci U S A, vol. 113, no. 12, pp. 3269-3274, 2016.
@article{Smith:2016:Proc-Natl-Acad-Sci-U-S-A:26961002,
title = {Allosteric switch regulates protein-protein binding through collective motion},
author = {C A Smith and D Ban and S Pratihar and K Giller and M Paulat and S Becker and C Griesinger and D Lee and B L de Groot},
doi = {10.1073/pnas.1519609113},
year = {2016},
date = {2016-03-01},
journal = {Proc Natl Acad Sci U S A},
volume = {113},
number = {12},
pages = {3269-3274},
abstract = {Many biological processes depend on allosteric communication between different parts of a protein, but the role of internal protein motion in propagating signals through the structure remains largely unknown. Through an experimental and computational analysis of the ground state dynamics in ubiquitin, we identify a collective global motion that is specifically linked to a conformational switch distant from the binding interface. This allosteric coupling is also present in crystal structures and is found to facilitate multispecificity, particularly binding to the ubiquitin-specific protease (USP) family of deubiquitinases. The collective motion that enables this allosteric communication does not affect binding through localized changes but, instead, depends on expansion and contraction of the entire protein domain. The characterization of these collective motions represents a promising avenue for finding and manipulating allosteric networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Many biological processes depend on allosteric communication between different parts of a protein, but the role of internal protein motion in propagating signals through the structure remains largely unknown. Through an experimental and computational analysis of the ground state dynamics in ubiquitin, we identify a collective global motion that is specifically linked to a conformational switch distant from the binding interface. This allosteric coupling is also present in crystal structures and is found to facilitate multispecificity, particularly binding to the ubiquitin-specific protease (USP) family of deubiquitinases. The collective motion that enables this allosteric communication does not affect binding through localized changes but, instead, depends on expansion and contraction of the entire protein domain. The characterization of these collective motions represents a promising avenue for finding and manipulating allosteric networks.
C A Smith, D Ban, S Pratihar, K Giller, C Schwiegk, B L de Groot, S Becker, C Griesinger, D Lee
Population Shuffling of Protein Conformations Journal Article
In: Angew Chem Int Ed Engl, vol. 54, no. 1, pp. 207-10, 2015.
@article{Smith:2014:Angew-Chem-Int-Ed-Engl:25377083,
title = {Population Shuffling of Protein Conformations},
author = {C A Smith and D Ban and S Pratihar and K Giller and C Schwiegk and B L de Groot and S Becker and C Griesinger and D Lee},
doi = {10.1002/anie.201408890},
year = {2015},
date = {2015-01-01},
journal = {Angew Chem Int Ed Engl},
volume = {54},
number = {1},
pages = {207-10},
abstract = {Motions play a vital role in the functions of many proteins. Discrete conformational transitions to excited states, happening on timescales of hundreds of microseconds, have been extensively characterized. On the other hand, the dynamics of the ground state are widely unexplored. Newly developed high-power relaxation dispersion experiments allow the detection of motions up to a one-digit microsecond timescale. These experiments showed that side chains in the hydrophobic core as well as at protein-protein interaction surfaces of both ubiquitin and the third immunoglobulin binding domain of protein G move on the microsecond timescale. Both proteins exhibit plasticity to this microsecond motion through redistribution of the populations of their side-chain rotamers, which interconvert on the picosecond to nanosecond timescale, making it likely that this "population shuffling" process is a general mechanism.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Motions play a vital role in the functions of many proteins. Discrete conformational transitions to excited states, happening on timescales of hundreds of microseconds, have been extensively characterized. On the other hand, the dynamics of the ground state are widely unexplored. Newly developed high-power relaxation dispersion experiments allow the detection of motions up to a one-digit microsecond timescale. These experiments showed that side chains in the hydrophobic core as well as at protein-protein interaction surfaces of both ubiquitin and the third immunoglobulin binding domain of protein G move on the microsecond timescale. Both proteins exhibit plasticity to this microsecond motion through redistribution of the populations of their side-chain rotamers, which interconvert on the picosecond to nanosecond timescale, making it likely that this "population shuffling" process is a general mechanism.
T M Sabo, C A Smith, D Ban, A Mazur, D Lee, C Griesinger
ORIUM: optimized RDC-based Iterative and Unified Model-free analysis Journal Article
In: J Biomol NMR, vol. 58, no. 4, pp. 287-301, 2014.
@article{Sabo:2014:J-Biomol-NMR:24013952,
title = {ORIUM: optimized RDC-based Iterative and Unified Model-free analysis},
author = {T M Sabo and C A Smith and D Ban and A Mazur and D Lee and C Griesinger},
doi = {10.1007/s10858-013-9775-1},
year = {2014},
date = {2014-04-01},
journal = {J Biomol NMR},
volume = {58},
number = {4},
pages = {287-301},
abstract = {Residual dipolar couplings (RDCs) are NMR parameters that provide both structural and dynamic information concerning inter-nuclear vectors, such as N-H(N) and Cα-Hα bonds within the protein backbone. Two approaches for extracting this information from RDCs are the model free analysis (MFA) (Meiler et al. in J Am Chem Soc 123:6098-6107, 2001; Peti et al. in J Am Chem Soc 124:5822-5833, 2002) and the direct interpretation of dipolar couplings (DIDCs) (Tolman in J Am Chem Soc 124:12020-12030, 2002). Both methods have been incorporated into iterative schemes, namely the self-consistent RDC based MFA (SCRM) (Lakomek et al. in J Biomol NMR 41:139-155, 2008) and iterative DIDC (Yao et al. in J Phys Chem B 112:6045-6056, 2008), with the goal of removing the influence of structural noise in the MFA and DIDC formulations. Here, we report a new iterative procedure entitled Optimized RDC-based Iterative and Unified Model-free analysis (ORIUM). ORIUM unifies theoretical concepts developed in the MFA, SCRM, and DIDC methods to construct a computationally less demanding approach to determine these structural and dynamic parameters. In all schemes, dynamic averaging reduces the actual magnitude of the alignment tensors complicating the determination of the absolute values for the generalized order parameters. To readdress this scaling issue that has been previously investigated (Lakomek et al. in J Biomol NMR 41:139-155, 2008; Salmon et al. in Angew Chem Int Edit 48:4154-4157, 2009), a new method is presented using only RDC data to establish a lower bound on protein motion, bypassing the requirement of Lipari-Szabo order parameters. ORIUM and the new scaling procedure are applied to the proteins ubiquitin and the third immunoglobulin domain of protein G (GB3). Our results indicate good agreement with the SCRM and iterative DIDC approaches and signify the general applicability of ORIUM and the proposed scaling for the extraction of inter-nuclear vector structural and dynamic content.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Residual dipolar couplings (RDCs) are NMR parameters that provide both structural and dynamic information concerning inter-nuclear vectors, such as N-H(N) and Cα-Hα bonds within the protein backbone. Two approaches for extracting this information from RDCs are the model free analysis (MFA) (Meiler et al. in J Am Chem Soc 123:6098-6107, 2001; Peti et al. in J Am Chem Soc 124:5822-5833, 2002) and the direct interpretation of dipolar couplings (DIDCs) (Tolman in J Am Chem Soc 124:12020-12030, 2002). Both methods have been incorporated into iterative schemes, namely the self-consistent RDC based MFA (SCRM) (Lakomek et al. in J Biomol NMR 41:139-155, 2008) and iterative DIDC (Yao et al. in J Phys Chem B 112:6045-6056, 2008), with the goal of removing the influence of structural noise in the MFA and DIDC formulations. Here, we report a new iterative procedure entitled Optimized RDC-based Iterative and Unified Model-free analysis (ORIUM). ORIUM unifies theoretical concepts developed in the MFA, SCRM, and DIDC methods to construct a computationally less demanding approach to determine these structural and dynamic parameters. In all schemes, dynamic averaging reduces the actual magnitude of the alignment tensors complicating the determination of the absolute values for the generalized order parameters. To readdress this scaling issue that has been previously investigated (Lakomek et al. in J Biomol NMR 41:139-155, 2008; Salmon et al. in Angew Chem Int Edit 48:4154-4157, 2009), a new method is presented using only RDC data to establish a lower bound on protein motion, bypassing the requirement of Lipari-Szabo order parameters. ORIUM and the new scaling procedure are applied to the proteins ubiquitin and the third immunoglobulin domain of protein G (GB3). Our results indicate good agreement with the SCRM and iterative DIDC approaches and signify the general applicability of ORIUM and the proposed scaling for the extraction of inter-nuclear vector structural and dynamic content.
C A Smith, C A Shi, M K Chroust, T E Bliska, M J Kelly, M P Jacobson, T Kortemme
Design of a phosphorylatable PDZ domain with peptide-specific affinity changes Journal Article
In: Structure, vol. 21, no. 1, pp. 54-64, 2013.
@article{Smith:2013:Structure:23159126,
title = {Design of a phosphorylatable PDZ domain with peptide-specific affinity changes},
author = {C A Smith and C A Shi and M K Chroust and T E Bliska and M J Kelly and M P Jacobson and T Kortemme},
doi = {10.1016/j.str.2012.10.007},
year = {2013},
date = {2013-01-01},
journal = {Structure},
volume = {21},
number = {1},
pages = {54-64},
abstract = {Phosphorylation is one of the most common posttranslational modifications controlling cellular protein activity. Here, we describe a combined computational and experimental strategy to design new phosphorylation sites into globular proteins to regulate their functions. We target a peptide recognition protein, the Erbin PDZ domain, to be phosphorylated by cAMP-dependent protein kinase. Comparing the five successful designs to the unsuccessful cases, we find a trade-off between protein stability and the ability to be modified by phosphorylation. In two designs, Erbin's peptide binding function is modified by phosphorylation, where the presence of the phosphate group destabilizes peptide binding. One of these showed an additional switch in specificity by†introducing favorable interactions between†a designed arginine in the peptide and phosphoserine on the PDZ domain. Because of the diversity of PDZ domains, this opens avenues for the design of related phosphoswitchable domains to create†a repertoire of regulatable interaction parts for synthetic biology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Phosphorylation is one of the most common posttranslational modifications controlling cellular protein activity. Here, we describe a combined computational and experimental strategy to design new phosphorylation sites into globular proteins to regulate their functions. We target a peptide recognition protein, the Erbin PDZ domain, to be phosphorylated by cAMP-dependent protein kinase. Comparing the five successful designs to the unsuccessful cases, we find a trade-off between protein stability and the ability to be modified by phosphorylation. In two designs, Erbin's peptide binding function is modified by phosphorylation, where the presence of the phosphate group destabilizes peptide binding. One of these showed an additional switch in specificity by†introducing favorable interactions between†a designed arginine in the peptide and phosphoserine on the PDZ domain. Because of the diversity of PDZ domains, this opens avenues for the design of related phosphoswitchable domains to create†a repertoire of regulatable interaction parts for synthetic biology.
N Ollikainen, C A Smith, J S Fraser, T Kortemme
Flexible backbone sampling methods to model and design protein alternative conformations Journal Article
In: Methods Enzymol, vol. 523, pp. 61-85, 2013.
@article{Ollikainen:2013:Methods-Enzymol:23422426,
title = {Flexible backbone sampling methods to model and design protein alternative conformations},
author = {N Ollikainen and C A Smith and J S Fraser and T Kortemme},
doi = {10.1016/B978-0-12-394292-0.00004-7},
year = {2013},
date = {2013-01-01},
journal = {Methods Enzymol},
volume = {523},
pages = {61-85},
abstract = {Sampling alternative conformations is key to understanding how proteins work and engineering them for new functions. However, accurately characterizing and modeling protein conformational ensembles remain experimentally and computationally challenging. These challenges must be met before protein conformational heterogeneity can be exploited in protein engineering and design. Here, as a stepping stone, we describe methods to detect alternative conformations in proteins and strategies to model these near-native conformational changes based on backrub-type Monte Carlo moves in Rosetta. We illustrate how Rosetta simulations that apply backrub moves improve modeling of point mutant side-chain conformations, native side-chain conformational heterogeneity, functional conformational changes, tolerated sequence space, protein interaction specificity, and amino acid covariation across protein-protein interfaces. We include relevant Rosetta command lines and RosettaScripts to encourage the application of these types of simulations to other systems. Our work highlights that critical scoring and sampling improvements will be necessary to approximate conformational landscapes. Challenges for the future development of these methods include modeling conformational changes that propagate away from designed mutation sites and modulating backbone flexibility to predictively design functionally important conformational heterogeneity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sampling alternative conformations is key to understanding how proteins work and engineering them for new functions. However, accurately characterizing and modeling protein conformational ensembles remain experimentally and computationally challenging. These challenges must be met before protein conformational heterogeneity can be exploited in protein engineering and design. Here, as a stepping stone, we describe methods to detect alternative conformations in proteins and strategies to model these near-native conformational changes based on backrub-type Monte Carlo moves in Rosetta. We illustrate how Rosetta simulations that apply backrub moves improve modeling of point mutant side-chain conformations, native side-chain conformational heterogeneity, functional conformational changes, tolerated sequence space, protein interaction specificity, and amino acid covariation across protein-protein interfaces. We include relevant Rosetta command lines and RosettaScripts to encourage the application of these types of simulations to other systems. Our work highlights that critical scoring and sampling improvements will be necessary to approximate conformational landscapes. Challenges for the future development of these methods include modeling conformational changes that propagate away from designed mutation sites and modulating backbone flexibility to predictively design functionally important conformational heterogeneity.
C A Smith, T Kortemme
Predicting the Tolerated Sequences for Proteins and Protein Interfaces Using RosettaBackrub Flexible Backbone Design Journal Article
In: PLoS One, vol. 6, no. 7, 2011.
@article{Smith:2011:PLoS-One:21789164,
title = {Predicting the Tolerated Sequences for Proteins and Protein Interfaces Using RosettaBackrub Flexible Backbone Design},
author = {C A Smith and T Kortemme},
doi = {10.1371/journal.pone.0020451},
year = {2011},
date = {2011-01-01},
journal = {PLoS One},
volume = {6},
number = {7},
abstract = {Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s) are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface), interactions between and within parts of the structure (e.g. domains) can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s) are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface), interactions between and within parts of the structure (e.g. domains) can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.
Andrew Leaver-Fay, Michael Tyka, Steven M Lewis, Oliver F Lange, James Thompson, Ron Jacak, Kristian W Kaufmann, Douglas P Renfrew, Colin A Smith, Will Sheffler, Ian W Davis, Seth Cooper, Adrien Treuille, Daniel J Mandell, Florian Richter, Yih-En Andrew Ban, Sarel J Fleishman, Jacob E Corn, David E Kim, Sergey Lyskov, Monica Berrondo, Stuart Mentzer, Zoran Popovic, James J Havranek, John Karanicolas, Rhiju Das, Jens Meiler, Tanja Kortemme, Jeffrey J Gray, Brian Kuhlman, David Baker, Philip Bradley
ROSETTA3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules Journal Article
In: Methods Enzymol, vol. 487, pp. 545-574, 2011.
@article{Leaver-Fay:2011,
title = {ROSETTA3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules},
author = {Andrew Leaver-Fay and Michael Tyka and Steven M Lewis and Oliver F Lange and James Thompson and Ron Jacak and Kristian W Kaufmann and Douglas P Renfrew and Colin A Smith and Will Sheffler and Ian W Davis and Seth Cooper and Adrien Treuille and Daniel J Mandell and Florian Richter and Yih-En Andrew Ban and Sarel J Fleishman and Jacob E Corn and David E Kim and Sergey Lyskov and Monica Berrondo and Stuart Mentzer and Zoran Popovic and James J Havranek and John Karanicolas and Rhiju Das and Jens Meiler and Tanja Kortemme and Jeffrey J Gray and Brian Kuhlman and David Baker and Philip Bradley},
doi = {10.1016/B978-0-12-381270-4.00019-6},
year = {2011},
date = {2011-01-01},
journal = {Methods Enzymol},
volume = {487},
pages = {545-574},
abstract = {We have recently completed a full rearchitecturing of the ROSETTA molecular
modeling program, generalizing and expanding its existing functionality. The
new architecture enables the rapid prototyping of novel protocols by providing
easy-to-use interfaces to powerful tools for molecular modeling. The source
code of this rearchitecturing has been released as ROSETTA3 and is freely available
for academic use. At the time of its release, it contained 470,000 lines of
code. Counting currently unpublished protocols at the time of this writing, the
source includes 1,285,000 lines. Its rapid growth is a testament to its ease of
use. This chapter describes the requirements for our new architecture, justifies
the design decisions, sketches out central classes, and highlights a few of the
common tasks that the new software can perform.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We have recently completed a full rearchitecturing of the ROSETTA molecular
modeling program, generalizing and expanding its existing functionality. The
new architecture enables the rapid prototyping of novel protocols by providing
easy-to-use interfaces to powerful tools for molecular modeling. The source
code of this rearchitecturing has been released as ROSETTA3 and is freely available
for academic use. At the time of its release, it contained 470,000 lines of
code. Counting currently unpublished protocols at the time of this writing, the
source includes 1,285,000 lines. Its rapid growth is a testament to its ease of
use. This chapter describes the requirements for our new architecture, justifies
the design decisions, sketches out central classes, and highlights a few of the
common tasks that the new software can perform.
modeling program, generalizing and expanding its existing functionality. The
new architecture enables the rapid prototyping of novel protocols by providing
easy-to-use interfaces to powerful tools for molecular modeling. The source
code of this rearchitecturing has been released as ROSETTA3 and is freely available
for academic use. At the time of its release, it contained 470,000 lines of
code. Counting currently unpublished protocols at the time of this writing, the
source includes 1,285,000 lines. Its rapid growth is a testament to its ease of
use. This chapter describes the requirements for our new architecture, justifies
the design decisions, sketches out central classes, and highlights a few of the
common tasks that the new software can perform.
C A Smith, T Kortemme
Structure-Based Prediction of the Peptide Sequence Space Recognized by Natural and Synthetic PDZ Domains Journal Article
In: J Mol Biol, vol. 402, no. 2, pp. 460-474, 2010.
@article{Smith:2010:J-Mol-Biol:20654621,
title = {Structure-Based Prediction of the Peptide Sequence Space Recognized by Natural and Synthetic PDZ Domains},
author = {C A Smith and T Kortemme},
doi = {10.1016/j.jmb.2010.07.032},
year = {2010},
date = {2010-09-01},
journal = {J Mol Biol},
volume = {402},
number = {2},
pages = {460-474},
abstract = {Protein-protein recognition, frequently mediated by members of large families of interaction domains, is one of the cornerstones of biological function. Here, we present a computational, structure-based method to predict the sequence space of peptides recognized by PDZ domains, one of the largest families of recognition proteins. As a test set, we use the considerable amount of recent phage display data that describe the peptide recognition preferences for 169 naturally occurring and engineered PDZ domains. For both wild-type PDZ domains and single point mutants, we find that 70-80% of the most frequently observed amino acids by phage display are predicted within the top five ranked amino acids. Phage display frequently identified recognition preferences for amino acids different from those present in the original crystal structure. Notably, in about half of these cases, our algorithm correctly captures these preferences, indicating that it can predict mutations that increase binding affinity relative to the starting structure. We also find that we can computationally recapitulate specificity changes upon mutation, a key test for successful forward design of protein-protein interface specificity. Across all evaluated data sets, we find that incorporation backbone sampling improves accuracy substantially, irrespective of using a crystal or NMR structure as the starting conformation. Finally, we report successful prediction of several amino acid specificity changes from blind tests in the DREAM4 peptide recognition domain specificity prediction challenge. Because the foundational methods developed here are structure based, these results suggest that they can be more generally applied to specificity prediction and redesign of other protein-protein interfaces that have structural information but lack phage display data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Protein-protein recognition, frequently mediated by members of large families of interaction domains, is one of the cornerstones of biological function. Here, we present a computational, structure-based method to predict the sequence space of peptides recognized by PDZ domains, one of the largest families of recognition proteins. As a test set, we use the considerable amount of recent phage display data that describe the peptide recognition preferences for 169 naturally occurring and engineered PDZ domains. For both wild-type PDZ domains and single point mutants, we find that 70-80% of the most frequently observed amino acids by phage display are predicted within the top five ranked amino acids. Phage display frequently identified recognition preferences for amino acids different from those present in the original crystal structure. Notably, in about half of these cases, our algorithm correctly captures these preferences, indicating that it can predict mutations that increase binding affinity relative to the starting structure. We also find that we can computationally recapitulate specificity changes upon mutation, a key test for successful forward design of protein-protein interface specificity. Across all evaluated data sets, we find that incorporation backbone sampling improves accuracy substantially, irrespective of using a crystal or NMR structure as the starting conformation. Finally, we report successful prediction of several amino acid specificity changes from blind tests in the DREAM4 peptide recognition domain specificity prediction challenge. Because the foundational methods developed here are structure based, these results suggest that they can be more generally applied to specificity prediction and redesign of other protein-protein interfaces that have structural information but lack phage display data.
F Lauck, C A Smith, G F Friedland, E L Humphris, T Kortemme
RosettaBackrub--a web server for flexible backbone protein structure modeling and design Journal Article
In: Nucleic Acids Res, vol. 38 Suppl, pp. 569-575, 2010.
@article{Lauck:2010:Nucleic-Acids-Res:20462859,
title = {RosettaBackrub--a web server for flexible backbone protein structure modeling and design},
author = {F Lauck and C A Smith and G F Friedland and E L Humphris and T Kortemme},
doi = {10.1093/nar/gkq369},
year = {2010},
date = {2010-07-01},
journal = {Nucleic Acids Res},
volume = {38 Suppl},
pages = {569-575},
abstract = {The RosettaBackrub server (http://kortemmelab.ucsf.edu/backrub) implements the Backrub method, derived from observations of alternative conformations in high-resolution protein crystal structures, for flexible backbone protein modeling. Backrub modeling is applied to three related applications using the Rosetta program for structure prediction and design: (I) modeling of structures of point mutations, (II) generating protein conformational ensembles and designing sequences consistent with these conformations and (III) predicting tolerated sequences at protein-protein interfaces. The three protocols have been validated on experimental data. Starting from a user-provided single input protein structure in PDB format, the server generates near-native conformational ensembles. The predicted conformations and sequences can be used for different applications, such as to guide mutagenesis experiments, for ensemble-docking approaches or to generate sequence libraries for protein design.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The RosettaBackrub server (http://kortemmelab.ucsf.edu/backrub) implements the Backrub method, derived from observations of alternative conformations in high-resolution protein crystal structures, for flexible backbone protein modeling. Backrub modeling is applied to three related applications using the Rosetta program for structure prediction and design: (I) modeling of structures of point mutations, (II) generating protein conformational ensembles and designing sequences consistent with these conformations and (III) predicting tolerated sequences at protein-protein interfaces. The three protocols have been validated on experimental data. Starting from a user-provided single input protein structure in PDB format, the server generates near-native conformational ensembles. The predicted conformations and sequences can be used for different applications, such as to guide mutagenesis experiments, for ensemble-docking approaches or to generate sequence libraries for protein design.
G D Friedland, A J Linares, C A Smith, T Kortemme
A simple model of backbone flexibility improves modeling of side-chain conformational variability Journal Article
In: J Mol Biol, vol. 380, no. 4, pp. 757-774, 2008.
@article{Friedland:2008:J-Mol-Biol:18547586,
title = {A simple model of backbone flexibility improves modeling of side-chain conformational variability},
author = {G D Friedland and A J Linares and C A Smith and T Kortemme},
doi = {10.1016/j.jmb.2008.05.006},
year = {2008},
date = {2008-07-01},
journal = {J Mol Biol},
volume = {380},
number = {4},
pages = {757-774},
abstract = {The considerable flexibility of side-chains in folded proteins is important for protein stability and function, and may have a role in mediating allosteric interactions. While sampling side-chain degrees of freedom has been an integral part of several successful computational protein design methods, the predictions of these approaches have not been directly compared to experimental measurements of side-chain motional amplitudes. In addition, protein design methods frequently keep the backbone fixed, an approximation that may substantially limit the ability to accurately model side-chain flexibility. Here, we describe a Monte Carlo approach to modeling side-chain conformational variability and validate our method against a large dataset of methyl relaxation order parameters derived from nuclear magnetic resonance (NMR) experiments (17 proteins and a total of 530 data points). We also evaluate a model of backbone flexibility based on Backrub motions, a type of conformational change frequently observed in ultra-high-resolution X-ray structures that accounts for correlated side-chain backbone movements. The fixed-backbone model performs reasonably well with an overall rmsd between computed and predicted side-chain order parameters of 0.26. Notably, including backbone flexibility leads to significant improvements in modeling side-chain order parameters for ten of the 17 proteins in the set. Greater accuracy of the flexible backbone model results from both increases and decreases in side-chain flexibility relative to the fixed-backbone model. This simple flexible-backbone model should be useful for a variety of protein design applications, including improved modeling of protein-protein interactions, design of proteins with desired flexibility or rigidity, and prediction of correlated motions within proteins.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The considerable flexibility of side-chains in folded proteins is important for protein stability and function, and may have a role in mediating allosteric interactions. While sampling side-chain degrees of freedom has been an integral part of several successful computational protein design methods, the predictions of these approaches have not been directly compared to experimental measurements of side-chain motional amplitudes. In addition, protein design methods frequently keep the backbone fixed, an approximation that may substantially limit the ability to accurately model side-chain flexibility. Here, we describe a Monte Carlo approach to modeling side-chain conformational variability and validate our method against a large dataset of methyl relaxation order parameters derived from nuclear magnetic resonance (NMR) experiments (17 proteins and a total of 530 data points). We also evaluate a model of backbone flexibility based on Backrub motions, a type of conformational change frequently observed in ultra-high-resolution X-ray structures that accounts for correlated side-chain backbone movements. The fixed-backbone model performs reasonably well with an overall rmsd between computed and predicted side-chain order parameters of 0.26. Notably, including backbone flexibility leads to significant improvements in modeling side-chain order parameters for ten of the 17 proteins in the set. Greater accuracy of the flexible backbone model results from both increases and decreases in side-chain flexibility relative to the fixed-backbone model. This simple flexible-backbone model should be useful for a variety of protein design applications, including improved modeling of protein-protein interactions, design of proteins with desired flexibility or rigidity, and prediction of correlated motions within proteins.
C A Smith, T Kortemme
Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction Journal Article
In: J Mol Biol, vol. 380, no. 4, pp. 742-756, 2008.
@article{Smith:2008:J-Mol-Biol:18547585,
title = {Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction},
author = {C A Smith and T Kortemme},
doi = {10.1016/j.jmb.2008.05.023},
year = {2008},
date = {2008-07-01},
journal = {J Mol Biol},
volume = {380},
number = {4},
pages = {742-756},
abstract = {Incorporation of effective backbone sampling into protein simulation and design is an important step in increasing the accuracy of computational protein modeling. Recent analysis of high-resolution crystal structures has suggested a new model, termed backrub, to describe localized, hinge-like alternative backbone and side-chain conformations observed in the crystal lattice. The model involves internal backbone rotations about axes between C-alpha atoms. Based on this observation, we have implemented a backrub-inspired sampling method in the Rosetta structure prediction and design program. We evaluate this model of backbone flexibility using three different tests. First, we show that Rosetta backrub simulations recapitulate the correlation between backbone and side-chain conformations in the high-resolution crystal structures upon which the model was based. As a second test of backrub sampling, we show that backbone flexibility improves the accuracy of predicting point-mutant side-chain conformations over fixed backbone rotameric sampling alone. Finally, we show that backrub sampling of triosephosphate isomerase loop 6 can capture the millisecond/microsecond oscillation between the open and closed states observed in solution. Our results suggest that backrub sampling captures a sizable fraction of localized conformational changes that occur in natural proteins. Application of this simple model of backbone motions may significantly improve both protein design and atomistic simulations of localized protein flexibility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Incorporation of effective backbone sampling into protein simulation and design is an important step in increasing the accuracy of computational protein modeling. Recent analysis of high-resolution crystal structures has suggested a new model, termed backrub, to describe localized, hinge-like alternative backbone and side-chain conformations observed in the crystal lattice. The model involves internal backbone rotations about axes between C-alpha atoms. Based on this observation, we have implemented a backrub-inspired sampling method in the Rosetta structure prediction and design program. We evaluate this model of backbone flexibility using three different tests. First, we show that Rosetta backrub simulations recapitulate the correlation between backbone and side-chain conformations in the high-resolution crystal structures upon which the model was based. As a second test of backrub sampling, we show that backbone flexibility improves the accuracy of predicting point-mutant side-chain conformations over fixed backbone rotameric sampling alone. Finally, we show that backrub sampling of triosephosphate isomerase loop 6 can capture the millisecond/microsecond oscillation between the open and closed states observed in solution. Our results suggest that backrub sampling captures a sizable fraction of localized conformational changes that occur in natural proteins. Application of this simple model of backbone motions may significantly improve both protein design and atomistic simulations of localized protein flexibility.
George Nicola, Colin A Smith, Ruben Abagyan
New method for the assessment of all drug-like pockets across a structural genome Journal Article
In: J Comput Biol, vol. 15, no. 3, pp. 231-240, 2008.
@article{Nicola:2008,
title = {New method for the assessment of all drug-like pockets across a structural genome},
author = {George Nicola and Colin A Smith and Ruben Abagyan},
doi = {10.1089/cmb.2007.0178},
year = {2008},
date = {2008-04-01},
journal = {J Comput Biol},
volume = {15},
number = {3},
pages = {231-240},
publisher = {MARY ANN LIEBERT INC},
address = {140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA},
abstract = {With the increasing wealth of structural information available for human pathogens, it is now becoming possible to leverage that information to aid in rational selection of targets for inhibitor discovery. We present a methodology for assessing the drugability of all small-molecule binding pockets in a pathogen. Our approach incorporates accurate pocket identification, sequence conservation with a similar organism, sequence conservation with the host, and structure resolution. This novel method is applied to 21 structures from the malarial parasite Plasmodium falciparum. Based on our survey of the structural genome, we selected enoyl-acyl carrier protein reductase (ENR) as a promising candidate for virtual screening based inhibitor discovery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the increasing wealth of structural information available for human pathogens, it is now becoming possible to leverage that information to aid in rational selection of targets for inhibitor discovery. We present a methodology for assessing the drugability of all small-molecule binding pockets in a pathogen. Our approach incorporates accurate pocket identification, sequence conservation with a similar organism, sequence conservation with the host, and structure resolution. This novel method is applied to 21 structures from the malarial parasite Plasmodium falciparum. Based on our survey of the structural genome, we selected enoyl-acyl carrier protein reductase (ENR) as a promising candidate for virtual screening based inhibitor discovery.
Grace O'Maille, Eden P Go, Linh Hoang, Elizabeth J Want, Colin Smith, Paul O'Maille, Anders Nordstrom, Hirotoshi Morita, Chuan Qin, Wilasinee Uritboonthai, Junefredo Apon, Richard Moore, James Garrett, Gary Siuzdak
Metabolomics relative quantitation with mass spectrometry using chemical derivatization and isotope labeling Journal Article
In: Spectr-Int J, vol. 22, no. 5, pp. 327-343, 2008.
@article{OMaille:2008,
title = {Metabolomics relative quantitation with mass spectrometry using chemical derivatization and isotope labeling},
author = {Grace O'Maille and Eden P Go and Linh Hoang and Elizabeth J Want and Colin Smith and Paul O'Maille and Anders Nordstrom and Hirotoshi Morita and Chuan Qin and Wilasinee Uritboonthai and Junefredo Apon and Richard Moore and James Garrett and Gary Siuzdak},
doi = {10.3233/SPE-2008-0361},
year = {2008},
date = {2008-01-01},
journal = {Spectr-Int J},
volume = {22},
number = {5},
pages = {327-343},
publisher = {IOS PRESS},
address = {NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS},
abstract = {Comprehensive detection and quantitation of metabolites from a biological source constitute the major challenges of current metabolomics research. Two chemical derivatization methodologies, butylation and amination, were applied to human serum for ionization enhancement of a broad spectrum of metabolite classes, including steroids and amino acids. LC-ESI-MS analysis of the derivatized serum samples provided a significant signal elevation across the total ion chromatogram to over a 100-fold increase in ionization efficiency. It was also demonstrated that derivatization combined with isotopically labeled reagents facilitated the relative quantitation of derivatized metabolites from individual as well as pooled samples.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Comprehensive detection and quantitation of metabolites from a biological source constitute the major challenges of current metabolomics research. Two chemical derivatization methodologies, butylation and amination, were applied to human serum for ionization enhancement of a broad spectrum of metabolite classes, including steroids and amino acids. LC-ESI-MS analysis of the derivatized serum samples provided a significant signal elevation across the total ion chromatogram to over a 100-fold increase in ionization efficiency. It was also demonstrated that derivatization combined with isotopically labeled reagents facilitated the relative quantitation of derivatized metabolites from individual as well as pooled samples.
George Nicola, Colin A Smith, Edinson Lucumi, Mack R Kuo, Luchezar Karagyozov, David A Fidock, James C Sacchettin, Ruben Abagyan
Discovery of novel inhibitors targeting enoyl-acyl carrier protein reductase in Plasmodium falciparum by structure-based virtual screening Journal Article
In: Biochem Biophys Res Commun, vol. 358, no. 3, pp. 686-691, 2007.
@article{Nicola:2007,
title = {Discovery of novel inhibitors targeting enoyl-acyl carrier protein reductase in Plasmodium falciparum by structure-based virtual screening},
author = {George Nicola and Colin A Smith and Edinson Lucumi and Mack R Kuo and Luchezar Karagyozov and David A Fidock and James C Sacchettin and Ruben Abagyan},
doi = {10.1016/j.bbrc.2007.04.113},
year = {2007},
date = {2007-07-01},
journal = {Biochem Biophys Res Commun},
volume = {358},
number = {3},
pages = {686-691},
publisher = {ACADEMIC PRESS INC ELSEVIER SCIENCE},
address = {525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA},
abstract = {There is a dire need for novel therapeutics to treat the virulent malarial parasite, Plasmodium falciparum. Recently, the X-ray crystal structure of enoyl-acyl carrier protein reductase (ENR) in complex with triclosan has been determined and provides an opportunity for the rational design of novel inhibitors targeting the active site of ENR. Here, we report the discovery of several compounds by virtual screening and their experimental validation as high potency PfENR inhibitors. (c) 2007 Elsevier Inc. All rights reserved.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
There is a dire need for novel therapeutics to treat the virulent malarial parasite, Plasmodium falciparum. Recently, the X-ray crystal structure of enoyl-acyl carrier protein reductase (ENR) in complex with triclosan has been determined and provides an opportunity for the rational design of novel inhibitors targeting the active site of ENR. Here, we report the discovery of several compounds by virtual screening and their experimental validation as high potency PfENR inhibitors. (c) 2007 Elsevier Inc. All rights reserved.
Elizabeth J Want, Colin A Smith, Chuan Qin, K C VanHorne, Gary Siuzdak
Phospholipid capture combined with non-linear chromatographic correction for improved serum metabolite profiling Journal Article
In: Metabolomics, vol. 2, no. 3, pp. 145-154, 2006.
@article{Want:2006b,
title = {Phospholipid capture combined with non-linear chromatographic correction for improved serum metabolite profiling},
author = {Elizabeth J Want and Colin A Smith and Chuan Qin and K C VanHorne and Gary Siuzdak},
doi = {10.1007/s11306-006-0028-0},
year = {2006},
date = {2006-09-01},
journal = {Metabolomics},
volume = {2},
number = {3},
pages = {145-154},
publisher = {SPRINGER},
address = {233 SPRING STREET, NEW YORK, NY 10013 USA},
abstract = {Serum analysis with LC/MS can yield thousands of potential metabolites. However, in metabolomics, biomarkers of interest will often be of low abundance, and ionization suppression from high abundance endogenous metabolites such as phospholipids may prevent the detection of these metabolites. Here a cerium-modified column and methyl-tert-butyl-ether (MTBE) liquid-liquid extraction were employed to remove phospholipids from serum in order to obtain a more comprehensive metabolite profile. XCMS, an in-house developed data analysis software platform, showed that the intensity of existing endogenous metabolites increased, and that new metabolites were observed. This application of phospholipid capture in combination with XCMS non-linear data processing has enormous potential in metabolite profiling, for biomarker detection and quantitation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Serum analysis with LC/MS can yield thousands of potential metabolites. However, in metabolomics, biomarkers of interest will often be of low abundance, and ionization suppression from high abundance endogenous metabolites such as phospholipids may prevent the detection of these metabolites. Here a cerium-modified column and methyl-tert-butyl-ether (MTBE) liquid-liquid extraction were employed to remove phospholipids from serum in order to obtain a more comprehensive metabolite profile. XCMS, an in-house developed data analysis software platform, showed that the intensity of existing endogenous metabolites increased, and that new metabolites were observed. This application of phospholipid capture in combination with XCMS non-linear data processing has enormous potential in metabolite profiling, for biomarker detection and quantitation.
EJ Want, G O'Maille, CA Smith, TR Brandon, W Uritboonthai, C Qin, SA Trauger, G Siuzdak
Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry Journal Article
In: Anal Chem, vol. 78, no. 3, pp. 743-752, 2006.
@article{Want:2006a,
title = {Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry},
author = {EJ Want and G O'Maille and CA Smith and TR Brandon and W Uritboonthai and C Qin and SA Trauger and G Siuzdak},
doi = {DOI 10.1021/ac051312t},
year = {2006},
date = {2006-02-01},
journal = {Anal Chem},
volume = {78},
number = {3},
pages = {743-752},
publisher = {AMER CHEMICAL SOC},
address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA},
abstract = {The aim of metabolite profiling is to monitor all metabolites within a biological sample for applications in basic biochemical research as well as pharmacokinetic studies and biomarker discovery. Here, novel data analysis software, XCMS, was used to monitor all metabolite features detected from an array of serum extraction methods, with application to metabolite profiling using electrospray liquid chromatography/mass spectrometry (ESI-LC/MS). The XCMS software enabled the comparison of methods with regard to reproducibility, the number and type of metabolite features detected, and the similarity of these features between different extraction methods. Extraction efficiency with regard to metabolite feature hydrophobicity was examined through the generation of unique feature density distribution plots, displaying feature distribution along chromatographic time. Hierarchical clustering was performed to highlight similarities in the metabolite features observed between the extraction methods. Protein extraction efficiency was determined using the Bradford assay, and the residual proteins were identified using nano-LC/MS/MS. Additionally, the identification of four of the most intensely ionized serum metabolites using FTMS and tandem mass spectrometry was reported. The extraction methods, ranging from organic solvents and acids to heat denaturation, varied widely in both protein removal efficiency and the number of mass spectral features detected. Methanol protein precipitation followed by centrifugation was found to be the most effective, straightforward, and reproducible approach, resulting in serum extracts containing over 2000 detected metabolite features and less than 2% residual protein. Interestingly, the combination of all approaches produced over 10 000 unique metabolite features, a number that is indicative of the complexity of the human metabolome and the potential of metabolomics in biomarker discovery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The aim of metabolite profiling is to monitor all metabolites within a biological sample for applications in basic biochemical research as well as pharmacokinetic studies and biomarker discovery. Here, novel data analysis software, XCMS, was used to monitor all metabolite features detected from an array of serum extraction methods, with application to metabolite profiling using electrospray liquid chromatography/mass spectrometry (ESI-LC/MS). The XCMS software enabled the comparison of methods with regard to reproducibility, the number and type of metabolite features detected, and the similarity of these features between different extraction methods. Extraction efficiency with regard to metabolite feature hydrophobicity was examined through the generation of unique feature density distribution plots, displaying feature distribution along chromatographic time. Hierarchical clustering was performed to highlight similarities in the metabolite features observed between the extraction methods. Protein extraction efficiency was determined using the Bradford assay, and the residual proteins were identified using nano-LC/MS/MS. Additionally, the identification of four of the most intensely ionized serum metabolites using FTMS and tandem mass spectrometry was reported. The extraction methods, ranging from organic solvents and acids to heat denaturation, varied widely in both protein removal efficiency and the number of mass spectral features detected. Methanol protein precipitation followed by centrifugation was found to be the most effective, straightforward, and reproducible approach, resulting in serum extracts containing over 2000 detected metabolite features and less than 2% residual protein. Interestingly, the combination of all approaches produced over 10 000 unique metabolite features, a number that is indicative of the complexity of the human metabolome and the potential of metabolomics in biomarker discovery.
C A Smith, E J Want, G O'Maille, R Abagyan, G Siuzdak
XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification Journal Article
In: Anal Chem, vol. 78, no. 3, pp. 779-787, 2006.
@article{Smith:2006:Anal-Chem:16448051,
title = {XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification},
author = {C A Smith and E J Want and G O'Maille and R Abagyan and G Siuzdak},
doi = {10.1021/ac051437y},
year = {2006},
date = {2006-02-01},
journal = {Anal Chem},
volume = {78},
number = {3},
pages = {779-787},
abstract = {Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
CA Smith, G O'Maille, EJ Want, C Qin, SA Trauger, TR Brandon, DE Custodio, R Abagyan, G Siuzdak
METLIN - A metabolite mass spectral database Journal Article
In: Ther Drug Monit, vol. 27, no. 6, pp. 747-751, 2005.
@article{Smith:2005a,
title = {METLIN - A metabolite mass spectral database},
author = {CA Smith and G O'Maille and EJ Want and C Qin and SA Trauger and TR Brandon and DE Custodio and R Abagyan and G Siuzdak},
doi = {10.1097/01.ftd.0000179845.53213.39},
year = {2005},
date = {2005-12-01},
journal = {Ther Drug Monit},
volume = {27},
number = {6},
pages = {747-751},
publisher = {LIPPINCOTT WILLIAMS \& WILKINS},
address = {530 WALNUT ST, PHILADELPHIA, PA 19106-3261 USA},
abstract = {Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin. scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLIN includes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin. scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLIN includes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.
YX Huang, CA Smith, HB Song, BP Morgan, R Abagyan, S Tomlinson
Insights into the human CD59 complement binding interface toward engineering new therapeutics Journal Article
In: J Biol Chem, vol. 280, no. 40, pp. 34073-34079, 2005.
@article{Huang:2005,
title = {Insights into the human CD59 complement binding interface toward engineering new therapeutics},
author = {YX Huang and CA Smith and HB Song and BP Morgan and R Abagyan and S Tomlinson},
doi = {DOI 10.1074/jbc.M504922200},
year = {2005},
date = {2005-10-01},
journal = {J Biol Chem},
volume = {280},
number = {40},
pages = {34073-34079},
publisher = {AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC},
address = {9650 ROCKVILLE PIKE, BETHESDA, MD 20814-3996 USA},
abstract = {CD59 is a 77-amino acid membrane glycoprotein that plays an important role in regulating the terminal pathway of complement by inhibiting formation of the cytolytic membrane attack complex ( MAC or C5b-9). The MAC is formed by the self assembly of C5b, C6, C7, C8, and multiple C9 molecules, with CD59 functioning by binding C5b-8 and C5b-9 in the assembling complex. We performed a scanning alanine mutagenesis screen of residues 16 - 57, a region previously identified to contain the C8/C9 binding interface. We have also created an improved NMR model from previously published data for structural understanding of CD59. Based on the scanning mutagenesis data, refined models, and additional site-specific mutations, we identified a binding interface that is much broader than previously thought. In addition to identifying substitutions that decreased CD59 activity, a surprising number of substitutions significantly enhanced CD59 activity. Because CD59 has significant therapeutic potential for the treatment of various inflammatory conditions, we investigated further the ability to enhance CD59 activity by additional mutagenesis studies. Based on the enhanced activity of membrane-bound mutant CD59 molecules, clinically relevant soluble mutant CD59-based proteins were prepared and shown to have up to a 3-fold increase in complement inhibitory activity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
CD59 is a 77-amino acid membrane glycoprotein that plays an important role in regulating the terminal pathway of complement by inhibiting formation of the cytolytic membrane attack complex ( MAC or C5b-9). The MAC is formed by the self assembly of C5b, C6, C7, C8, and multiple C9 molecules, with CD59 functioning by binding C5b-8 and C5b-9 in the assembling complex. We performed a scanning alanine mutagenesis screen of residues 16 - 57, a region previously identified to contain the C8/C9 binding interface. We have also created an improved NMR model from previously published data for structural understanding of CD59. Based on the scanning mutagenesis data, refined models, and additional site-specific mutations, we identified a binding interface that is much broader than previously thought. In addition to identifying substitutions that decreased CD59 activity, a surprising number of substitutions significantly enhanced CD59 activity. Because CD59 has significant therapeutic potential for the treatment of various inflammatory conditions, we investigated further the ability to enhance CD59 activity by additional mutagenesis studies. Based on the enhanced activity of membrane-bound mutant CD59 molecules, clinically relevant soluble mutant CD59-based proteins were prepared and shown to have up to a 3-fold increase in complement inhibitory activity.
RC Gentleman, VJ Carey, DM Bates, B Bolstad, M Dettling, S Dudoit, B Ellis, L Gautier, YC Ge, J Gentry, K Hornik, T Hothorn, W Huber, S Iacus, R Irizarry, F Leisch, C Li, M Maechler, AJ Rossini, G Sawitzki, C Smith, G Smyth, L Tierney, JYH Yang, JH Zhang
Bioconductor: open software development for computational biology and bioinformatics Journal Article
In: Genome Biol, vol. 5, no. 10, 2004.
@article{Gentleman:2004,
title = {Bioconductor: open software development for computational biology and bioinformatics},
author = {RC Gentleman and VJ Carey and DM Bates and B Bolstad and M Dettling and S Dudoit and B Ellis and L Gautier and YC Ge and J Gentry and K Hornik and T Hothorn and W Huber and S Iacus and R Irizarry and F Leisch and C Li and M Maechler and AJ Rossini and G Sawitzki and C Smith and G Smyth and L Tierney and JYH Yang and JH Zhang},
doi = {10.1186/gb-2004-5-10-r80},
year = {2004},
date = {2004-01-01},
journal = {Genome Biol},
volume = {5},
number = {10},
publisher = {BIOMED CENTRAL LTD},
address = {MIDDLESEX HOUSE, 34-42 CLEVELAND ST, LONDON W1T 4LB, ENGLAND},
abstract = {The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.