1
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Yang D, Chong LT. WEDAP: A Python Package for Streamlined Plotting of Molecular Simulation Data. J Chem Inf Model 2024; 64:5749-5755. [PMID: 39013164 PMCID: PMC11323263 DOI: 10.1021/acs.jcim.4c00867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/18/2024]
Abstract
Given the growing interest in path sampling methods for extending the time scales of molecular dynamics (MD) simulations, there has been great interest in software tools that streamline the generation of plots for monitoring the progress of large-scale simulations. Here, we present the WEDAP Python package for simplifying the analysis of data generated from either conventional MD simulations or the weighted ensemble (WE) path sampling method, as implemented in the widely used WESTPA software package. WEDAP facilitates (i) the parsing of WE simulation data stored in highly compressed, hierarchical HDF5 files and (ii) incorporates trajectory weights from WE simulations into all generated plots. Our Python package consists of multiple user-friendly interfaces: a command-line interface, a graphical user interface, and a Python application programming interface. We demonstrate the plotting features of WEDAP through a series of examples using data from WE and conventional MD simulations that focus on the HIV-1 capsid protein's C-terminal domain dimer as a showcase system. The source code for WEDAP is freely available on GitHub at https://github.com/chonglab-pitt/wedap.
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Affiliation(s)
- Darian
T. Yang
- Molecular
Biophysics and Structural Biology Graduate Program, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania 15260, United States
- Department
of Structural Biology, University of Pittsburgh
School of Medicine, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Lillian T. Chong
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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2
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Yang DT, Chong LT. WEDAP: A Python Package for Streamlined Plotting of Molecular Simulation Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.18.594829. [PMID: 38826259 PMCID: PMC11142070 DOI: 10.1101/2024.05.18.594829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Given the growing interest in path sampling methods for extending the timescales of molecular dynamics (MD) simulations, there has been great interest in software tools that streamline the generation of plots for monitoring the progress of large-scale simulations. Here, we present the WEDAP Python package for simplifying the analysis of data generated from either conventional MD simulations or the weighted ensemble (WE) path sampling method, as implemented in the widely used WESTPA software package. WEDAP facilitates (i) the parsing of WE simulation data stored in highly compressed, hierarchical HDF5 files, and (ii) incorporates trajectory weights from WE simulations into all generated plots. Our Python package consists of multiple user-friendly interfaces: a command-line interface, a graphical user interface, and a Python application programming interface. We demonstrate the plotting features of WEDAP through a series of examples using data from WE and conventional MD simulations that focus on the HIV-1 capsid protein C-terminal domain dimer as a showcase system. The source code for WEDAP is freely available on GitHub at https://github.com/chonglab-pitt/wedap .
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3
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Bogetti A, Zwier MC, Chong LT. Revisiting Textbook Azide-Clock Reactions: A "Propeller-Crawling" Mechanism Explains Differences in Rates. J Am Chem Soc 2024; 146:12828-12835. [PMID: 38687173 PMCID: PMC11078601 DOI: 10.1021/jacs.4c03360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/02/2024]
Abstract
An ongoing challenge to chemists is the analysis of pathways and kinetics for chemical reactions in solution, including transient structures between the reactants and products that are difficult to resolve using laboratory experiments. Here, we enabled direct molecular dynamics simulations of a textbook series of chemical reactions on the hundreds of ns to μs time scale using the weighted ensemble (WE) path sampling strategy with hybrid quantum mechanical/molecular mechanical (QM/MM) models. We focused on azide-clock reactions involving addition of an azide anion to each of three long-lived trityl cations in an acetonitrile-water solvent mixture. Results reveal a two-step mechanism: (1) diffusional collision of reactants to form an ion-pair intermediate; (2) "activation" or rearrangement of the intermediate to the product. Our simulations yield not only reaction rates that are within error of experiment but also rates for individual steps, indicating the activation step as rate-limiting for all three cations. Further, the trend in reaction rates is due to dynamical effects, i.e., differing extents of the azide anion "crawling" along the cation's phenyl-ring "propellers" during the activation step. Our study demonstrates the power of analyzing pathways and kinetics to gain insights on reaction mechanisms, underscoring the value of including WE and other related path sampling strategies in the modern toolbox for chemists.
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Affiliation(s)
- Anthony
T. Bogetti
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Matthew C. Zwier
- Department
of Chemistry, Drake University, Des Moines, Iowa 50311, United States
| | - Lillian T. Chong
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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4
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Bebon R, Godec A. Controlling Uncertainty of Empirical First-Passage Times in the Small-Sample Regime. PHYSICAL REVIEW LETTERS 2023; 131:237101. [PMID: 38134782 DOI: 10.1103/physrevlett.131.237101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 10/18/2023] [Accepted: 10/31/2023] [Indexed: 12/24/2023]
Abstract
We derive general bounds on the probability that the empirical first-passage time τ[over ¯]_{n}≡∑_{i=1}^{n}τ_{i}/n of a reversible ergodic Markov process inferred from a sample of n independent realizations deviates from the true mean first-passage time by more than any given amount in either direction. We construct nonasymptotic confidence intervals that hold in the elusive small-sample regime and thus fill the gap between asymptotic methods and the Bayesian approach that is known to be sensitive to prior belief and tends to underestimate uncertainty in the small-sample setting. We prove sharp bounds on extreme first-passage times that control uncertainty even in cases where the mean alone does not sufficiently characterize the statistics. Our concentration-of-measure-based results allow for model-free error control and reliable error estimation in kinetic inference, and are thus important for the analysis of experimental and simulation data in the presence of limited sampling.
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Affiliation(s)
- Rick Bebon
- Mathematical bioPhysics Group, Max Planck Institute for Multidisciplinary Sciences, 37077 Göttingen, Germany
| | - Aljaž Godec
- Mathematical bioPhysics Group, Max Planck Institute for Multidisciplinary Sciences, 37077 Göttingen, Germany
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5
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Copperman J, Gross SM, Chang YH, Heiser LM, Zuckerman DM. Morphodynamical cell state description via live-cell imaging trajectory embedding. Commun Biol 2023; 6:484. [PMID: 37142678 PMCID: PMC10160022 DOI: 10.1038/s42003-023-04837-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 04/10/2023] [Indexed: 05/06/2023] Open
Abstract
Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.
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Affiliation(s)
- Jeremy Copperman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
| | - Sean M Gross
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA.
| | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA.
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6
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Bogetti AT, Leung JMG, Russo JD, Zhang S, Thompson JP, Saglam AS, Ray D, Mostofian B, Pratt AJ, Abraham RC, Harrison PO, Dudek M, Torrillo PA, DeGrave AJ, Adhikari U, Faeder JR, Andricioaei I, Adelman JL, Zwier MC, LeBard DN, Zuckerman DM, Chong LT. A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2023; 5:1655. [PMID: 37200895 PMCID: PMC10191340 DOI: 10.33011/livecoms.5.1.1655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present two sets of tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. The first set of more basic tutorials describes a range of simulation types, from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding. The second set ecompasses six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for larger systems and/or slower processes. The advanced tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of "binless" schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling for systems biology models. Applications of the advanced tutorials include atomistic and non-spatial models, and consist of complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics or systems biology simulations.
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Affiliation(s)
| | | | - John D. Russo
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | | | | | - Ali S. Saglam
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA
| | - Dhiman Ray
- Department of Chemistry, University of California Irvine, Irvine, CA
| | - Barmak Mostofian
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - AJ Pratt
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Rhea C. Abraham
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Page O. Harrison
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Max Dudek
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Paul A. Torrillo
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Alex J. DeGrave
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Upendra Adhikari
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - James R. Faeder
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA
| | - Ioan Andricioaei
- Department of Chemistry, University of California Irvine, Irvine, CA
| | - Joshua L. Adelman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA
| | | | | | - Daniel M. Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - Lillian T. Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
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7
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Andrea B, Atiqah A, Gianluca E. Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy. Neuroinformatics 2022; 20:665-675. [PMID: 34716564 DOI: 10.1007/s12021-021-09551-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2021] [Indexed: 12/31/2022]
Abstract
Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen's d, the power analysis indicated that a high sample size ([Formula: see text]) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.
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Affiliation(s)
- Bizzego Andrea
- Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
| | - Azhari Atiqah
- Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore, Singapore
| | - Esposito Gianluca
- Department of Psychology and Cognitive Science, University of Trento, Trento, Italy. .,Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore, Singapore. .,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
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8
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Russo JD, Zhang S, Leung JMG, Bogetti AT, Thompson JP, DeGrave AJ, Torrillo PA, Pratt AJ, Wong KF, Xia J, Copperman J, Adelman JL, Zwier MC, LeBard DN, Zuckerman DM, Chong LT. WESTPA 2.0: High-Performance Upgrades for Weighted Ensemble Simulations and Analysis of Longer-Timescale Applications. J Chem Theory Comput 2022; 18:638-649. [PMID: 35043623 PMCID: PMC8825686 DOI: 10.1021/acs.jctc.1c01154] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The weighted ensemble (WE) family of methods is one of several statistical mechanics-based path sampling strategies that can provide estimates of key observables (rate constants and pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on the major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE methods. The key features of the new WESTPA 2.0 software enhance the efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation data sets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and "binless" WE strategies.
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Affiliation(s)
- John D Russo
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - She Zhang
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Jeremy M G Leung
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Anthony T Bogetti
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Jeff P Thompson
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Alex J DeGrave
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Paul A Torrillo
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - A J Pratt
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Kim F Wong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Junchao Xia
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Jeremy Copperman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - Joshua L Adelman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Matthew C Zwier
- Department of Chemistry, Drake University, Des Moines, Iowa 50311-4505, United States
| | - David N LeBard
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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9
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Zuckerman DM, Russo JD. A gentle introduction to the non-equilibrium physics of trajectories: Theory, algorithms, and biomolecular applications. AMERICAN JOURNAL OF PHYSICS 2021; 89:1048-1061. [PMID: 35530173 PMCID: PMC9075726 DOI: 10.1119/10.0005603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/25/2021] [Indexed: 06/14/2023]
Abstract
Despite the importance of non-equilibrium statistical mechanics in modern physics and related fields, the topic is often omitted from undergraduate and core-graduate curricula. Key aspects of non-equilibrium physics, however, can be understood with a minimum of formalism based on a rigorous trajectory picture. The fundamental object is the ensemble of trajectories, a set of independent time-evolving systems, which easily can be visualized or simulated (e.g., for protein folding) and which can be analyzed rigorously in analogy to an ensemble of static system configurations. The trajectory picture provides a straightforward basis for understanding first-passage times, "mechanisms" in complex systems, and fundamental constraints on the apparent reversibility of complex processes. Trajectories make concrete the physics underlying the diffusion and Fokker-Planck partial differential equations. Last but not least, trajectory ensembles underpin some of the most important algorithms that have provided significant advances in biomolecular studies of protein conformational and binding processes.
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10
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Feng M, Gilson MK. Mechanistic analysis of light-driven overcrowded alkene-based molecular motors by multiscale molecular simulations. Phys Chem Chem Phys 2021; 23:8525-8540. [PMID: 33876015 PMCID: PMC8102045 DOI: 10.1039/d0cp06685k] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
We analyze light-driven overcrowded alkene-based molecular motors, an intriguing class of small molecules that have the potential to generate MHz-scale rotation rates. The full rotation process is simulated at multiple scales by combining quantum surface-hopping molecular dynamics (MD) simulations for the photoisomerization step with classical MD simulations for the thermal helix inversion step. A Markov state analysis resolves conformational substates, their interconversion kinetics, and their roles in the motor's rotation process. Furthermore, motor performance metrics, including rotation rate and maximal power output, are computed to validate computations against experimental measurements and to inform future designs. Lastly, we find that to correctly model these motors, the force field must be optimized by fitting selected parameters to reference quantum mechanical energy surfaces. Overall, our simulations yield encouraging agreement with experimental observables such as rotation rates, and provide mechanistic insights that may help future designs.
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Affiliation(s)
- Mudong Feng
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA.
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11
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Schultz AJ, Kofke DA. Identifying and estimating bias in overlap-sampling free-energy calculations. MOLECULAR SIMULATION 2021. [DOI: 10.1080/08927022.2020.1758695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Andrew J. Schultz
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - David A. Kofke
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA
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12
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DeGrave AJ, Bogetti AT, Chong LT. The RED scheme: Rate-constant estimation from pre-steady state weighted ensemble simulations. J Chem Phys 2021; 154:114111. [PMID: 33752378 PMCID: PMC7972523 DOI: 10.1063/5.0041278] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/11/2021] [Indexed: 12/13/2022] Open
Abstract
We present the Rate from Event Durations (RED) scheme, a new scheme that more efficiently calculates rate constants using the weighted ensemble path sampling strategy. This scheme enables rate-constant estimation from shorter trajectories by incorporating the probability distribution of event durations, or barrier-crossing times, from a simulation. We have applied the RED scheme to weighted ensemble simulations of a variety of rare-event processes that range in complexity: residue-level simulations of protein conformational switching, atomistic simulations of Na+/Cl- association in explicit solvent, and atomistic simulations of protein-protein association in explicit solvent. Rate constants were estimated with up to 50% greater efficiency than the original weighted ensemble scheme. Importantly, our scheme accounts for the systematic error that results from statistical bias toward the observation of events with short durations and reweights the event duration distribution accordingly. The RED scheme is relevant to any simulation strategy that involves unbiased trajectories of similar length to the most probable event duration, including weighted ensemble, milestoning, and standard simulations as well as the construction of Markov state models.
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Affiliation(s)
| | - Anthony T. Bogetti
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - Lillian T. Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
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13
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Copperman J, Zuckerman DM. Accelerated Estimation of Long-Timescale Kinetics from Weighted Ensemble Simulation via Non-Markovian "Microbin" Analysis. J Chem Theory Comput 2020; 16:6763-6775. [PMID: 32990438 PMCID: PMC8045600 DOI: 10.1021/acs.jctc.0c00273] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The weighted ensemble (WE) simulation strategy provides unbiased sampling of nonequilibrium processes, such as molecular folding or binding, but the extraction of rate constants relies on characterizing steady-state behavior. Unfortunately, WE simulations of sufficiently complex systems will not relax to steady state on observed simulation times. Here, we show that a postsimulation clustering of molecular configurations into "microbins" using methods developed in the Markov State Model (MSM) community can yield unbiased kinetics from WE data before steady-state convergence of the WE simulation itself. Because WE trajectories are directional and not equilibrium distributed, the history-augmented MSM (haMSM) formulation can be used, which yields the mean first-passage time (MFPT) without bias for arbitrarily small lag times. Accurate kinetics can be obtained while bypassing the often prohibitive convergence requirements of the nonequilibrium weighted ensemble. We validate the method in a simple diffusive process on a two-dimensional (2D) random energy landscape and then analyze atomistic protein folding simulations using WE molecular dynamics. We report significant progress toward the unbiased estimation of protein folding times and pathways, though key challenges remain.
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Affiliation(s)
- Jeremy Copperman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239, United States
| | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239, United States
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14
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Mostofian B, Johnson QR, Smith JC, Cheng X. Carotenoids promote lateral packing and condensation of lipid membranes. Phys Chem Chem Phys 2020; 22:12281-12293. [PMID: 32432296 DOI: 10.1039/d0cp01031f] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Carotenoids are pigment molecules that protect biomembranes against degradation and may be involved in the formation of functional bacterial membrane microdomains. Little is known on whether different types of carotenoids have different effects on the membrane or if there is any concentration dependence of these effects. In this work, we present results from molecular dynamics simulations of phospholipid bilayers containing different amounts of either β-carotene or zeaxanthin. Both β-carotene and zeaxanthin show the ability to laterally condense the membrane lipids and reduce their inter-leaflet interactions. With increasing concentrations, both carotenoids increase the bilayer thickness and rigidity. The results reveal that carotenoids have similar effects to cholesterol on regulating the behavior of fluid-phase membranes, suggesting that they could function as sterol substitutes and confirming their potential role in the formation of functional membrane domains.
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Affiliation(s)
- Barmak Mostofian
- Center for Molecular Biophysics, Oak Ridge National Lab, Oak Ridge, TN 37830, USA.
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15
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da Silva FA, Viana AP, Corrêa CCG, Carvalho BM, de Sousa CMB, Amaral BD, Ambrósio M, Glória LS. Impact of Bayesian Inference on the Selection of Psidium guajava. Sci Rep 2020; 10:1999. [PMID: 32029823 PMCID: PMC7005186 DOI: 10.1038/s41598-020-58850-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 01/19/2020] [Indexed: 11/09/2022] Open
Abstract
Perennial breeding species demand substantial investment in various resources, mainly the required time to obtain adult and productive plants. Estimating several genetic parameters in these species, in a more confidence way, means saving resources when selecting a new genotype. A model using the Bayesian approach was compared with the frequentist methodology for selecting superior genotypes. A population of 17 families of full-siblings of guava tree was evaluated, and the yield, fruit mass, and pulp mass were measured. The Bayesian methodology suggest more accurate estimates of variance components, as well as better results to fit of model in a cross-validation. Proper priori for Bayesian model is very important to convergency of chains, mainly for small datasets. Even with poor priori, Bayesian was better than frequentist approach.
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Affiliation(s)
- Flavia Alves da Silva
- Laboratory of Plant Genetic Breeding (LMGV), Center for Agricultural Sciences and Technologies (CCTA), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Av. Alberto Lamego 2000, 28013-602, Campos dos Goytacazes - RJ, Brazil.
| | - Alexandre Pio Viana
- Laboratory of Plant Genetic Breeding (LMGV), Center for Agricultural Sciences and Technologies (CCTA), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Av. Alberto Lamego 2000, 28013-602, Campos dos Goytacazes - RJ, Brazil
| | - Caio Cezar Guedes Corrêa
- Laboratory of Plant Genetic Breeding (LMGV), Center for Agricultural Sciences and Technologies (CCTA), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Av. Alberto Lamego 2000, 28013-602, Campos dos Goytacazes - RJ, Brazil
| | - Beatriz Murizini Carvalho
- Laboratory of Plant Genetic Breeding (LMGV), Center for Agricultural Sciences and Technologies (CCTA), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Av. Alberto Lamego 2000, 28013-602, Campos dos Goytacazes - RJ, Brazil
| | - Carlos Misael Bezerra de Sousa
- Laboratory of Plant Genetic Breeding (LMGV), Center for Agricultural Sciences and Technologies (CCTA), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Av. Alberto Lamego 2000, 28013-602, Campos dos Goytacazes - RJ, Brazil
| | - Bruno Dias Amaral
- Laboratory of Plant Genetic Breeding (LMGV), Center for Agricultural Sciences and Technologies (CCTA), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Av. Alberto Lamego 2000, 28013-602, Campos dos Goytacazes - RJ, Brazil
| | - Moisés Ambrósio
- Laboratory of Plant Genetic Breeding (LMGV), Center for Agricultural Sciences and Technologies (CCTA), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Av. Alberto Lamego 2000, 28013-602, Campos dos Goytacazes - RJ, Brazil
| | - Leonardo Siqueira Glória
- Laboratory of Animal Science (LZO), Center for Agricultural Sciences and Technologies (CCTA), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Av. Alberto Lamego 2000, 28013-602, Campos dos Goytacazes - RJ, Brazil
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16
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Ghysels A, Krämer A, Venable RM, Teague WE, Lyman E, Gawrisch K, Pastor RW. Permeability of membranes in the liquid ordered and liquid disordered phases. Nat Commun 2019; 10:5616. [PMID: 31819053 PMCID: PMC6901538 DOI: 10.1038/s41467-019-13432-7] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 11/06/2019] [Indexed: 11/09/2022] Open
Abstract
The functional significance of ordered nanodomains (or rafts) in cholesterol rich eukaryotic cell membranes has only begun to be explored. This study exploits the correspondence of cellular rafts and liquid ordered (Lo) phases of three-component lipid bilayers to examine permeability. Molecular dynamics simulations of Lo phase dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), and cholesterol show that oxygen and water transit a leaflet through the DOPC and cholesterol rich boundaries of hexagonally packed DPPC microdomains, freely diffuse along the bilayer midplane, and escape the membrane along the boundary regions. Electron paramagnetic resonance experiments provide critical validation: the measured ratio of oxygen concentrations near the midplanes of liquid disordered (Ld) and Lo bilayers of DPPC/DOPC/cholesterol is 1.75 ± 0.35, in very good agreement with 1.3 ± 0.3 obtained from simulation. The results show how cellular rafts can be structurally rigid signaling platforms while remaining nearly as permeable to small molecules as the Ld phase.
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Affiliation(s)
- An Ghysels
- Center for Molecular Modeling, Ghent University, Technologiepark 46, 9052, Gent, Belgium.
| | - Andreas Krämer
- Laboratory of Computational Biology, National Heart Lung Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Richard M Venable
- Laboratory of Computational Biology, National Heart Lung Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Walter E Teague
- Laboratory of Membrane Biochemistry and Biophysics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Edward Lyman
- Department of Physics and Astronomy and Department of Chemistry and Biochemistry, University of Delaware, Newark, 19716, DE, USA
| | - Klaus Gawrisch
- Laboratory of Membrane Biochemistry and Biophysics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Richard W Pastor
- Laboratory of Computational Biology, National Heart Lung Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
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17
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Bogetti AT, Mostofian B, Dickson A, Pratt AJ, Saglam AS, Harrison PO, Adelman JL, Dudek M, Torrillo PA, DeGrave AJ, Adhikari U, Zwier MC, Zuckerman DM, Chong LT. A Suite of Tutorials for the WESTPA Rare-Events Sampling Software [Article v1.0]. ACTA ACUST UNITED AC 2019; 1. [PMID: 32395705 DOI: 10.33011/livecoms.1.2.10607] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present five tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. Users are expected to already have significant experience with running standard molecular dynamics simulations using the underlying dynamics engine of interest (e.g. Amber, Gromacs, OpenMM). The tutorials range from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding.
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Affiliation(s)
| | - Barmak Mostofian
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - Alex Dickson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI
| | - A J Pratt
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Ali S Saglam
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Page O Harrison
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Joshua L Adelman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA; currently unaffiliated
| | - Max Dudek
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Paul A Torrillo
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Alex J DeGrave
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA.,Current address: Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA
| | - Upendra Adhikari
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR.,Current address: Department of Chemistry, Missouri Valley College, Marshall, MO
| | | | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
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18
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Mostofian B, Zhuang T, Cheng X, Nickels JD. Branched-Chain Fatty Acid Content Modulates Structure, Fluidity, and Phase in Model Microbial Cell Membranes. J Phys Chem B 2019; 123:5814-5821. [DOI: 10.1021/acs.jpcb.9b04326] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Barmak Mostofian
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, Oregon 97239, United States
| | - Tony Zhuang
- College of Medicine, University of Tennessee, Memphis, Tennessee 38163, United States
| | - Xiaolin Cheng
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, Ohio 43210, United States
| | - Jonathan D. Nickels
- Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, Ohio 45221, United States
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19
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Adhikari U, Mostofian B, Copperman J, Subramanian SR, Petersen AA, Zuckerman DM. Computational Estimation of Microsecond to Second Atomistic Folding Times. J Am Chem Soc 2019; 141:6519-6526. [PMID: 30892023 PMCID: PMC6660137 DOI: 10.1021/jacs.8b10735] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Despite the development of massively parallel computing hardware including inexpensive graphics processing units (GPUs), it has remained infeasible to simulate the folding of atomistic proteins at room temperature using conventional molecular dynamics (MD) beyond the microsecond scale. Here, we report the folding of atomistic, implicitly solvated protein systems with folding times τ ranging from ∼10 μs to ∼100 ms using the weighted ensemble (WE) strategy in combination with GPU computing. Starting from an initial structure or set of structures, WE organizes an ensemble of GPU-accelerated MD trajectory segments via intermittent pruning and replication events to generate statistically unbiased estimates of rate constants for rare events such as folding; no biasing forces are used. Although the variance among atomistic WE folding runs is significant, multiple independent runs are used to reduce and quantify statistical uncertainty. Folding times are estimated directly from WE probability flux and from history-augmented Markov analysis of the WE data. Three systems were examined: NTL9 at low solvent viscosity (yielding τf = 0.8-9 μs), NTL9 at water-like viscosity (τf = 0.2-2 ms), and Protein G at low viscosity (τf = 3-200 ms). In all cases, the folding time, uncertainty, and ensemble properties could be estimated from WE simulation; for Protein G, this characterization required significantly less overall computing than would be required to observe a single folding event with conventional MD simulations. Our results suggest that the use and calibration of force fields and solvent models for precise estimation of kinetic quantities is becoming feasible.
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Affiliation(s)
- Upendra Adhikari
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, OR 97239
| | - Barmak Mostofian
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, OR 97239
| | - Jeremy Copperman
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, OR 97239
| | | | - Andrew A. Petersen
- NCSU Data Science Resources, North Carolina State University, Raleigh, NC 27695
| | - Daniel M. Zuckerman
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, OR 97239
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