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Thomas T, Roux B. TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES. THE EUROPEAN PHYSICAL JOURNAL. B 2021; 94:203. [PMID: 36524055 PMCID: PMC9749240 DOI: 10.1140/epjb/s10051-021-00207-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/14/2021] [Indexed: 05/28/2023]
Abstract
Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology, and chemistry. Nonetheless, generating genuine knowledge about biological systems using MD simulations remains challenging. Protein tyrosine kinases are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Due to the large conformational changes and long timescales involved in their function, these kinases present particularly challenging problems to modern computational and theoretical frameworks aimed at elucidating the dynamics of complex biomolecular systems. Markov state models have achieved limited success in tackling the broader conformational ensemble and biased methods are often employed to examine specific long timescale events. Recent advances in machine learning continue to push the limitations of current methodologies and provide notable improvements when integrated with the existing frameworks. A broad perspective is drawn from a critical review of recent studies.
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Roux B. String Method with Swarms-of-Trajectories, Mean Drifts, Lag Time, and Committor. J Phys Chem A 2021; 125:7558-7571. [PMID: 34406010 PMCID: PMC8419867 DOI: 10.1021/acs.jpca.1c04110] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 07/26/2021] [Indexed: 11/29/2022]
Abstract
The kinetics of a dynamical system comprising two metastable states is formulated in terms of a finite-time propagator in phase space (position and velocity) adapted to the underdamped Langevin equation. Dimensionality reduction to a subspace of collective variables yields familiar expressions for the propagator, committor, and steady-state flux. A quadratic expression for the steady-state flux between the two metastable states can serve as a robust variational principle to determine an optimal approximate committor expressed in terms of a set of collective variables. The theoretical formulation is exploited to clarify the foundation of the string method with swarms-of-trajectories, which relies on the mean drift of short trajectories to determine the optimal transition pathway. It is argued that the conditions for Markovity within a subspace of collective variables may not be satisfied with an arbitrary short time-step and that proper kinetic behaviors appear only when considering the effective propagator for longer lag times. The effective propagator with finite lag time is amenable to an eigenvalue-eigenvector spectral analysis, as elaborated previously in the context of position-based Markov models. The time-correlation functions calculated by swarms-of-trajectories along the string pathway constitutes a natural extension of these developments. The present formulation provides a powerful theoretical framework to characterize the optimal pathway between two metastable states of a system.
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Affiliation(s)
- Benoît Roux
- Department
of Biochemistry and Molecular Biology, The
University of Chicago, Chicago, Illinois 60637, United States
- Department
of Chemistry, The University of Chicago, 5735 S. Ellis Avenue, Chicago, Illinois 60637, United States
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Rosa-Raíces JL, Zhang B, Miller TF. Path-accelerated stochastic molecular dynamics: Parallel-in-time integration using path integrals. J Chem Phys 2019; 151:164120. [DOI: 10.1063/1.5125455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Jorge L. Rosa-Raíces
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Thomas F. Miller
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
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Abstract
Comprehensive data about the composition and structure of cellular components have enabled the construction of quantitative whole-cell models. While kinetic network-type models have been established, it is also becoming possible to build physical, molecular-level models of cellular environments. This review outlines challenges in constructing and simulating such models and discusses near- and long-term opportunities for developing physical whole-cell models that can connect molecular structure with biological function.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA;
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
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Kar P, Feig M. Hybrid All-Atom/Coarse-Grained Simulations of Proteins by Direct Coupling of CHARMM and PRIMO Force Fields. J Chem Theory Comput 2017; 13:5753-5765. [PMID: 28992696 DOI: 10.1021/acs.jctc.7b00840] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Hybrid all-atom/coarse-grained (AA/CG) simulations of proteins offer a computationally efficient compromise where atomistic details are only applied to biologically relevant regions while benefiting from the speedup of treating the remaining parts of a given system at the CG level. The recently developed CG model, PRIMO, allows a direct coupling with an atomistic force field with no additional modifications or coupling terms and the ability to carry out dynamic simulations without any restraints on secondary or tertiary structures. A hybrid AA/CG scheme based on combining all-atom CHARMM and coarse-grained PRIMO representations was validated via molecular dynamics and replica exchange simulations of soluble and membrane proteins. The AA/CG scheme was also tested in the calculation of the free energy profile for the transition from the closed to the open state of adenylate kinase via umbrella sampling molecular dynamics method. The overall finding is that the AA/CG scheme generates dynamics and energetics that are qualitatively and quantitatively comparable to AA simulations while offering the computational advantages of coarse-graining. This model opens the door to challenging applications where high accuracy is required only in parts of large biomolecular complexes.
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Affiliation(s)
- Parimal Kar
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan 48824, United States
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan 48824, United States
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Chen Y, Kale S, Weare J, Dinner AR, Roux B. Multiple Time-Step Dual-Hamiltonian Hybrid Molecular Dynamics - Monte Carlo Canonical Propagation Algorithm. J Chem Theory Comput 2016; 12:1449-1458. [PMID: 26918826 PMCID: PMC4946962 DOI: 10.1021/acs.jctc.5b00706] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
A multiple time-step integrator based on a dual Hamiltonian and a hybrid method combining molecular dynamics (MD) and Monte Carlo (MC) is proposed to sample systems in the canonical ensemble. The Dual Hamiltonian Multiple Time-Step (DHMTS) algorithm is based on two similar Hamiltonians: a computationally expensive one that serves as a reference and a computationally inexpensive one to which the workload is shifted. The central assumption is that the difference between the two Hamiltonians is slowly varying. Earlier work has shown that such dual Hamiltonian multiple time-step schemes effectively precondition nonlinear differential equations for dynamics by reformulating them into a recursive root finding problem that can be solved by propagating a correction term through an internal loop, analogous to RESPA. Of special interest in the present context, a hybrid MD-MC version of the DHMTS algorithm is introduced to enforce detailed balance via a Metropolis acceptance criterion and ensure consistency with the Boltzmann distribution. The Metropolis criterion suppresses the discretization errors normally associated with the propagation according to the computationally inexpensive Hamiltonian, treating the discretization error as an external work. Illustrative tests are carried out to demonstrate the effectiveness of the method.
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Affiliation(s)
- Yunjie Chen
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Seyit Kale
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Jonathan Weare
- Department of Statistics & James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States
| | - Aaron R. Dinner
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Benoît Roux
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637, United States
- Center for Nanomaterials, Argonne National Laboratory, Argonne, Illinois 60439, United States
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Gowers RJ, Carbone P. A multiscale approach to model hydrogen bonding: The case of polyamide. J Chem Phys 2015; 142:224907. [PMID: 26071731 DOI: 10.1063/1.4922445] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
We present a simple multiscale model for polymer chains in which it is possible to selectively remove degrees of freedom. The model integrates all-atom and coarse-grained potentials in a simple and systematic way and allows a fast sampling of the complex conformational energy surface typical of polymers whilst maintaining a realistic description of selected atomistic interactions. In particular, we show that it is possible to simultaneously reproduce the structure of highly directional non-bonded interactions such as hydrogen bonds and efficiently explore the large number of conformations accessible to the polymer chain. We apply the method to a melt of polyamide removing from the model only the degrees of freedom associated to the aliphatic segments and keeping at atomistic resolution the amide groups involved in the formation of the hydrogen bonds. The results show that the multiscale model produces structural properties that are comparable with the fully atomistic model despite being five times faster to simulate.
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Affiliation(s)
- Richard J Gowers
- School of Chemical Engineering and Analytical Science, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Paola Carbone
- School of Chemical Engineering and Analytical Science, University of Manchester, Manchester M13 9PL, United Kingdom
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Kale S, Sode O, Weare J, Dinner AR. Finding Chemical Reaction Paths with a Multilevel Preconditioning Protocol. J Chem Theory Comput 2014; 10:5467-5475. [PMID: 25516726 PMCID: PMC4263463 DOI: 10.1021/ct500852y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Indexed: 11/28/2022]
Abstract
Finding transition paths for chemical reactions can be computationally costly owing to the level of quantum-chemical theory needed for accuracy. Here, we show that a multilevel preconditioning scheme that was recently introduced (Tempkin et al. J. Chem. Phys.2014, 140, 184114) can be used to accelerate quantum-chemical string calculations. We demonstrate the method by finding minimum-energy paths for two well-characterized reactions: tautomerization of malonaldehyde and Claissen rearrangement of chorismate to prephanate. For these reactions, we show that preconditioning density functional theory (DFT) with a semiempirical method reduces the computational cost for reaching a converged path that is an optimum under DFT by several fold. The approach also shows promise for free energy calculations when thermal noise can be controlled.
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Affiliation(s)
- Seyit Kale
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States
| | - Olaseni Sode
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Computing, Environment, and Life Sciences, Argonne National Laboratory, Argonne, Illinois 60439, United States
| | - Jonathan Weare
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States
| | - Aaron R Dinner
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States
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