MDTraj: a modern, open library for the analysis of molecular dynamics trajectories

McGibbon, Robert T.; Beauchamp, Kyle A.; Schwantes, Christian R.; Wang, Lee‐Ping; Hernández, Carlos X.; Harrigan, Matthew P.; Lane, Thomas J; Swails, Jason M.; Pande, Vijay S.

doi:10.1101/008896

Cited by 341 publications

(409 citation statements)

References 6 publications

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“…Geometric analysis of representative cluster centers was performed using MDTraj: 36 in particular, RMSDs, solventaccessible surface areas, and atomic distances. Ensemble average values within MSMs were calculated as the expectation value for a particular observable.…”

Section: Research Articlementioning

confidence: 99%

Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models

Zimmerman

Hart

Sibbald

et al. 2018

Biophysical Journal

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Protein stabilization is fundamental to enzyme function and evolution, yet understanding the determinants of a protein's stability remains a challenge. This is largely due to a shortage of atomically detailed models for the ensemble of relevant protein conformations and their relative populations. For example, the M182T substitution in TEM β-lactamase, an enzyme that confers antibiotic resistance to bacteria, is stabilizing but the precise mechanism remains unclear. Here, we employ Markov state models (MSMs) to uncover how M182T shifts the distribution of different structures that TEM adopts. We find that M182T stabilizes a helix that is a key component of a domain interface. We then predict the effects of other mutations, including a novel stabilizing mutation, and experimentally test our predictions using a combination of stability measurements, crystallography, NMR, and in vivo measurements of bacterial fitness. We expect our insights and methodology to provide a valuable foundation for protein design. ■ INTRODUCTIONStudying the evolution of antibiotic resistance has provided many insights into how proteins acquire new functions, but the mechanistic basis for how mutations alter a protein's activity and stability often remains unclear. For example, studying how bacteria evolve variants of TEM β-lactamase that confer resistance to new antibiotics by degrading these drugs has revealed that many of the mutations that give rise to new functions are destabilizing. Therefore, it is common for proteins to acquire one or more mutations that alter their function and then to acquire additional mutations that restore stability. 1M182T is one such stabilizing mutation in TEM, and it has appeared in numerous clinical isolates and directed evolution experiments.2−4 This substitution occurs far from the active site ( Figure 1A) and, on its own, has little effect on TEM's activity. It is often called a global suppressor because of its ability to counterbalance the destabilizing effects of a wide variety of other substitutions that do alter TEM's activity.3 Despite over two decades of work on this variant, the mechanism of stabilization by M182T is not understood well enough to predict new stabilizing mutations. Elucidating the mechanism underlying this stabilization would provide a basis for predicting other global suppressors and eventually developing quantitative design principles.A mechanistic understanding of how M182T stabilizes TEM remains elusive because of a lack of methods that provide both a detailed structural model of the relevant species and their relative populations. Spectroscopic studies have revealed that TEM-1, which we will refer to as wild-type TEM, populates at least three states at equilibrium: a native state (N), an intermediate state (I), and an unfolded state (U).5 Introducing the M182T substitution appears to reduce the number of equilibrium states to two. 4 However, there is debate over whether this results from M182T stabilizing the native state or destabilizing the intermediate. Moreove...

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Section: Research Articlementioning

confidence: 99%

Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models

Zimmerman

Hart

Sibbald

et al. 2018

Biophysical Journal

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“…Under this assumption, we then show that the slow modes of the WT can be transferred to the mutant simulation by computing an equivalent set of protein structural features. This requires using a protein structural alignment to identify equivalent residues which is readily possible using modern software 48,49 . We benchmarked our method on two test cases showing how switching force field in alanine .…”

Section: Discussionmentioning

confidence: 99%

Transfer Learning from Markov models leads to efficient sampling of related systems

Sultan

Pande

2017

Preprint

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Abstract:We recently showed that the time-structure based independent component analysis method from Markov state model literature provided a set of variationally optimal slow collective variables for Metadynamics (tICA-Metadynamics). In this paper, we extend the methodology towards efficient sampling of related mutants by borrowing ideas from transfer learning methods in machine learning. Our method explicitly assumes that a similar set of slow modes and metastable states are found in both the wild type (base line) and its mutants. Under this assumption, we describe a few simple techniques using sequence mapping for transferring the slow modes and structural information contained in the wild type simulation to a mutant model for performing enhanced sampling. The resulting simulations can then be reweighted onto the full-phase space using Multi-state Bennett Acceptance Ratio, allowing for thermodynamic comparison against the wild type. We first benchmark our methodology by re-capturing alanine dipeptide dynamics across a range of different atomistic force fields, including the polarizable Amoeba force field, after learning a set of slow modes using Amber ff99sb-ILDN. We next extend the method by including structural data from the wild type simulation and apply the technique to recapturing the affects of the GTT mutation on the FIP35 WW domain.Introduction: Efficient sampling of protein configuration space remains an unsolved problem in computational biophysics. While algorithmic advances in molecular dynamics (MD) code bases 1 combined with distributed computing hardware 2 , specialized chips 3 , and large-scale increasingly faster GPU clusters have provided routine access to microsecond timescale dynamics, there is still room for significant improvements. One such potential avenue is predicting the effects of mutations onto the protein's wild type free energy landscape. At this point it is worth explicitly noting that while we use the biological terms wild type and mutant extensively, our methodology is easily generalizable to scenarios where a baseline (aka the wild type) free energy landscape has been mapped out and we now wish to understand the dynamical consequences of a making some changes to system (aka the mutation). Under the current scheme, one would have to re-run our entire simulation to ascertain the affects of a mutation onto a protein's free energy landscape. Due to the vast

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“…88 Finally, the interactions are subsequently turned back on via a reverse of the original procedure.…”

Section: We Use Nonequilibrium Candidate Montementioning

confidence: 99%

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Gill¹,

Lim²,

Grinaway³

et al. 2018

Preprint

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Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragmentlike ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically-relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.

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MDTraj: a modern, open library for the analysis of molecular dynamics trajectories

Cited by 341 publications

References 6 publications

Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models

Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models

Transfer Learning from Markov models leads to efficient sampling of related systems

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

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