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Gilardoni I, Piomponi V, Fröhlking T, Bussi G. MDRefine: A Python package for refining molecular dynamics trajectories with experimental data. J Chem Phys 2025; 162:192501. [PMID: 40371829 DOI: 10.1063/5.0256841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2025] [Accepted: 04/28/2025] [Indexed: 05/16/2025] Open
Abstract
Molecular dynamics (MD) simulations play a crucial role in resolving the underlying conformational dynamics of molecular systems. However, their capability to correctly reproduce and predict dynamics in agreement with experiments is limited by the accuracy of the force-field model. This capability can be improved by refining the structural ensembles or the force-field parameters. Furthermore, discrepancies with experimental data can be due to imprecise forward models, namely, functions mapping simulated structures to experimental observables. Here, we introduce MDRefine, a Python package aimed at implementing the refinement of the ensemble, the force field, and/or the forward model by comparing MD-generated trajectories with the experimental data. The software consists of several tools that can be employed separately from each other or combined together in different ways, providing a seamless interpolation between these three different types of refinement. We use some benchmark cases to show that the combined approach is superior to separately applied refinements. MDRefine has been released as an open-source package under the LGPLv2+ license. Source code, documentation, and examples are available at https://pypi.org/project/MDRefine and https://github.com/bussilab/MDRefine.
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Affiliation(s)
- Ivan Gilardoni
- Scuola Internazionale Superiore di Studi Avanzati, SISSA, Via Bonomea, 265, 34136 Trieste, Italy
| | - Valerio Piomponi
- Area Science Park, Località Padriciano, 99, 34149 Trieste, Italy
| | | | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, SISSA, Via Bonomea, 265, 34136 Trieste, Italy
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Sasmal S, Pal T, Hocky GM, McCullagh M. Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM. J Chem Theory Comput 2024; 20:3492-3502. [PMID: 38662196 PMCID: PMC11104435 DOI: 10.1021/acs.jctc.4c00223] [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: 02/22/2024] [Revised: 04/05/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
Quantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as protein folding, interconversion between folded states, ligand binding, and allosteric regulation. Accurate quantification of these ensembles remains a challenge for conventional molecular simulations of all but the simplest molecules due to insufficient sampling. Enhanced sampling approaches, such as metadynamics, were designed to overcome this challenge; however, the nonuniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of nonuniform frame weights into a structural clustering method entitled shapeGMM. The result of frame-weighted shapeGMM is a high dimensional probability density and generative model for the unbiased system from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a shapeGMM probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the underlying free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantifying biomolecular ensembles from biased simulations.
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Affiliation(s)
- Subarna Sasmal
- Department of Chemistry, New York
University, New York, New York 10003, United
States
| | - Triasha Pal
- Department of Chemistry, New York
University, New York, New York 10003, United
States
| | - Glen M. Hocky
- Department of Chemistry, New York
University, New York, New York 10003, United
States
- Simons Center for Computational Physical Chemistry,
New York University, New York, New York 10003,
United States
| | - Martin McCullagh
- Department of Chemistry, Oklahoma State
University, Stillwater, Oklahoma 74078, United
States
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Gomez D, Peña Ccoa WJ, Singh Y, Rojas E, Hocky GM. Molecular Paradigms for Biological Mechanosensing. J Phys Chem B 2021; 125:12115-12124. [PMID: 34709040 DOI: 10.1021/acs.jpcb.1c06330] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Many proteins in living cells are subject to mechanical forces, which can be generated internally by molecular machines, or externally, e.g., by pressure gradients. In general, these forces fall in the piconewton range, which is similar in magnitude to forces experienced by a molecule due to thermal fluctuations. While we would naively expect such moderate forces to produce only minimal changes, a wide variety of "mechanosensing" proteins have evolved with functions that are responsive to forces in this regime. The goal of this article is to provide a physical chemistry perspective on protein-based molecular mechanosensing paradigms used in living systems, and how these paradigms can be explored using novel computational methods.
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Affiliation(s)
- David Gomez
- Department of Biology, New York University, New York, New York 10003, United States.,Department of Chemistry, New York University, New York, New York 10003, United States
| | - Willmor J Peña Ccoa
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yuvraj Singh
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Enrique Rojas
- Department of Biology, New York University, New York, New York 10003, United States
| | - Glen M Hocky
- Department of Chemistry, New York University, New York, New York 10003, United States
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Abstract
QM/MM simulations have become an indispensable tool in many chemical and biochemical investigations. Considering the tremendous degree of success, including recognition by a 2013 Nobel Prize in Chemistry, are there still "burning challenges" in QM/MM methods, especially for biomolecular systems? In this short Perspective, we discuss several issues that we believe greatly impact the robustness and quantitative applicability of QM/MM simulations to many, if not all, biomolecules. We highlight these issues with observations and relevant advances from recent studies in our group and others in the field. Despite such limited scope, we hope the discussions are of general interest and will stimulate additional developments that help push the field forward in meaningful directions.
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Affiliation(s)
- Qiang Cui
- Departments of Chemistry, Physics, and Biomedical Engineering, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Tanmoy Pal
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Luke Xie
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
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Amirkulova DB, Chakraborty M, White AD. Experimentally Consistent Simulation of Aβ 21-30 Peptides with a Minimal NMR Bias. J Phys Chem B 2020; 124:8266-8277. [PMID: 32845146 DOI: 10.1021/acs.jpcb.0c07129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Misfolded amyloid peptides are neurotoxic molecules associated with Alzheimer's disease. The Aβ21-30 peptide fragment is a decapeptide fragment of the complete Aβ42 peptide which is a hypothesized cause of Alzheimer's disease via amyloid fibrillogenesis. Aβ21-30 is investigated here with a combination of NMR (nuclear magnetic resonance) spectroscopy experiments and molecular dynamics simulations with experiment directed simulation (EDS). EDS is a maximum entropy biasing method that augments a molecular dynamics simulation with experimental data (NMR chemical shifts) to improve agreement with experiments and thus accuracy. EDS molecular dynamics shows that the Aβ21-30 monomer has a β turn stabilized by the following interactions: S26-K28, D23-S26, and D23-K28. NMR, total correlation spectroscopy, and rotating frame Overhauser effect spectroscopy experiments provide independent agreement. Subsequent two- and four-monomer EDS simulations show aggregation. Diffusion coefficients calculated from molecular simulation also agreed with experimentally measured values only after using EDS, providing independent assessment of accuracy. This work demonstrates how accuracy can be improved by directly using experimental data in molecular dynamics of complex processes like self-assembly.
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Affiliation(s)
- Dilnoza B Amirkulova
- Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States
| | - Maghesree Chakraborty
- Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States
| | - Andrew D White
- Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States
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Calio PB, Hocky GM, Voth GA. Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Ab Initio Molecular Dynamics Simulations of Water. J Chem Theory Comput 2020; 16:5675-5684. [DOI: 10.1021/acs.jctc.0c00558] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Paul B. Calio
- Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
| | - Glen M. Hocky
- Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Gregory A. Voth
- Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
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Hartmann MJ, Singh Y, Vanden-Eijnden E, Hocky GM. Infinite switch simulated tempering in force (FISST). J Chem Phys 2020; 152:244120. [DOI: 10.1063/5.0009280] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Affiliation(s)
| | - Yuvraj Singh
- Department of Chemistry, New York University, New York, New York 10003, USA
| | - Eric Vanden-Eijnden
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
| | - Glen M. Hocky
- Department of Chemistry, New York University, New York, New York 10003, USA
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Amirkulova DB, White AD. Recent advances in maximum entropy biasing techniques for molecular dynamics. MOLECULAR SIMULATION 2019. [DOI: 10.1080/08927022.2019.1608988] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- D. B. Amirkulova
- Department of Chemical Engineering, University of Rochester, Rochester, NY, USA
| | - A. D. White
- Department of Chemical Engineering, University of Rochester, Rochester, NY, USA
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Abstract
This chapter discusses how the PLUMED plugin for molecular dynamics can be used to analyze and bias molecular dynamics trajectories. The chapter begins by introducing the notion of a collective variable and by then explaining how the free energy can be computed as a function of one or more collective variables. A number of practical issues mostly around periodic boundary conditions that arise when these types of calculations are performed using PLUMED are then discussed. Later parts of the chapter discuss how PLUMED can be used to perform enhanced sampling simulations that introduce simulation biases or multiple replicas of the system and Monte Carlo exchanges between these replicas. This section is then followed by a discussion on how free-energy surfaces and associated error bars can be extracted from such simulations by using weighted histogram and block averaging techniques.
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Affiliation(s)
- Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy.
| | - Gareth A Tribello
- Atomistic Simulation Centre, School of Mathematics and Physics, Queen's University Belfast, Belfast, UK.
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Advances in coarse-grained modeling of macromolecular complexes. Curr Opin Struct Biol 2018; 52:119-126. [PMID: 30508766 DOI: 10.1016/j.sbi.2018.11.005] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 11/05/2018] [Accepted: 11/17/2018] [Indexed: 01/12/2023]
Abstract
Recent progress in coarse-grained (CG) molecular modeling and simulation has facilitated an influx of computational studies on biological macromolecules and their complexes. Given the large separation of length-scales and time-scales that dictate macromolecular biophysics, CG modeling and simulation are well-suited to bridge the microscopic and mesoscopic or macroscopic details observed from all-atom molecular simulations and experiments, respectively. In this review, we first summarize recent innovations in the development of CG models, which broadly include structure-based, knowledge-based, and dynamics-based approaches. We then discuss recent applications of different classes of CG models to explore various macromolecular complexes. Finally, we conclude with an outlook for the future in this ever-growing field of biomolecular modeling.
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Capelli R, Tiana G, Camilloni C. An implementation of the maximum-caliber principle by replica-averaged time-resolved restrained simulations. J Chem Phys 2018; 148:184114. [PMID: 29764124 DOI: 10.1063/1.5030339] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Inferential methods can be used to integrate experimental informations and molecular simulations. The maximum entropy principle provides a framework for using equilibrium experimental data, and it has been shown that replica-averaged simulations, restrained using a static potential, are a practical and powerful implementation of such a principle. Here we show that replica-averaged simulations restrained using a time-dependent potential are equivalent to the principle of maximum caliber, the dynamic version of the principle of maximum entropy, and thus may allow us to integrate time-resolved data in molecular dynamics simulations. We provide an analytical proof of the equivalence as well as a computational validation making use of simple models and synthetic data. Some limitations and possible solutions are also discussed.
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Affiliation(s)
- Riccardo Capelli
- Center for Complexity and Biosystems and Department of Physics, Università degli Studi di Milano and INFN, Via Celoria 16, I-20133 Milano, Italy
| | - Guido Tiana
- Center for Complexity and Biosystems and Department of Physics, Università degli Studi di Milano and INFN, Via Celoria 16, I-20133 Milano, Italy
| | - Carlo Camilloni
- Dipartimento di Bioscienze, Università degli Studi di Milano, Via Celoria 26, I-20133 Milano, Italy
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Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments. COMPUTATION 2018. [DOI: 10.3390/computation6010015] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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