Automatic Method for Identifying Reaction Coordinates in Complex Systems
Abstract
To interpret simulations of a complex system to determine the physical mechanism of a dynamical process, it is necessary to identify the small number of coordinates that distinguish the stable states from the transition states. We develop an automatic method for identifying these degrees of freedom from a database of candidate physical variables. In the method neural networks are used to determine the functional dependence of the probability of committing to a stable state (committor) on a set of coordinates, and a genetic algorithm selects the combination of inputs that yields the best fit. The method enables us to obtain the first set of coordinates that is demonstrably sufficient to specify the transition state of the C(7eq)--> alpha(R) isomerization of the alanine dipeptide in the presence of explicit water molecules. It is revealed that the solute-solvent coupling can be described by a solvent-derived electrostatic torque around one of the main-chain bonds, and the collective, long-ranged nature of this interaction accounts for previous failures to characterize this reaction.
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It is possible to automate the parameter optimization, so as to maximise the similarity with the committor.
“It is then possible to automate the parameter optimization, so as to maximise the similarity with the committor 100-102 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [100]
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“To benchmark the accuracy of M&M, we used alanine dipeptide in vacuo (CH 3 -CO-NH-C α HCH 3 -CO-NH-CH 3 ), which has become a standard test case for many computational techniques 17–21 .”
MethodVanden‐Eijnden, E. (2007) · Springer eBooks · doi:10.1007/3-540-35273-2_13 · Cited by 98 · see citing works
A generalized deep learning approach for local structure identification in molecular simulations
DeFever, R. S., Targonski, C., Hall, S. W., Smith, M. C., & Sarupria, S. (2019) · Chemical Science · doi:10.1039/c9sc02097g · Cited by 95 · see citing works
Practical and conceptual path sampling issues
Bolhuis, P. G., & Christoph Dellago, A. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02419-6 · Cited by 92 · see citing works
Identification of simple reaction coordinates from complex dynamics
McGibbon, R. T., Husic, B. E., & Pande, V. S. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4974306 · Cited by 90 · see citing works
Enhanced Sampling of Nonequilibrium Steady States
Dickson, A., & Dinner, A. R. (2010) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.012809.103433 · Cited by 91 · see citing works
Self-citationPerspective: Insight into reaction coordinates and dynamics from the potential energy landscape
Wales, D. J. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4916307 · Cited by 90 · see citing works
Galerkin approximation of dynamical quantities using trajectory data
Thiede, E. H., Giannakis, D., Dinner, A. R., & Weare, J. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5063730 · Cited by 90 · see citing works
Self-citationBolhuis, P. G., & Swenson, D. W. H. (2021) · Advanced Theory and Simulations · doi:10.1002/adts.202000237 · Cited by 85 · see citing works
Chasing Collective Variables Using Autoencoders and Biased Trajectories
Belkacemi, Z., Gkeka, P., Tony Lelièvre, A., & Stoltz, G. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00415 · Cited by 85 · see citing works
Computing committor functions for the study of rare events using deep learning
Li, Q., Lin, B., & Ren, W. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5110439 · Cited by 83 · see citing works
“The isomerization of alanine dipeptide has been the subject of several theoretical and computational studies, 4,19–21 therefore it serves as a good benchmark problem for the proposed method.”
BackgroundNonequilibrium umbrella sampling in spaces of many order parameters
Dickson, A., Warmflash, A., & Dinner, A. R. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3070677 · Cited by 83 · see citing works
Self-citationIntegrating diffusion maps with umbrella sampling: Application to alanine dipeptide
Ferguson, A. L., Panagiotopoulos, A. Z., Debenedetti, P. G., & Kevrekidis, I. G. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3574394 · Cited by 81 · see citing works
Vreede, J., Juraszek, J., & Bolhuis, P. G. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0908754107 · Cited by 79 · see citing works
“More efficient committor analysis algorithms include a GNN approach (25), a Bayesian path statistical analysis (26), and a likelihood maximization (LM) method (27, 28).”
MethodA two-step nucleotide-flipping mechanism enables kinetic discrimination of DNA lesions by AGT
Hu, J., Ma, A., & Dinner, A. R. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0708058105 · Cited by 78 · see citing works
“Efficient means for generating initial paths (14) together with informatic methods (13) that we introduced now enable us to harvest a statistically significant number of trajectories of a biomedically important stochastic process in its entirety and identify the features that characterize the…”
BackgroundMethodSelf-citationRecent developments in methods for identifying reaction coordinates
Li, W., & Ma, A. (2014) · Molecular Simulation · doi:10.1080/08927022.2014.907898 · Cited by 76 · see citing works
“The method developed by Ma and Dinner [19] uses a GNN [66,67] method to extract the reaction coordinates from a pool of pre-selected candidates based on the committor information contained in a database of systems configurations.”
BackgroundMethodInfluentialSelf-citationMachine Learning of Biomolecular Reaction Coordinates.
Brandt, S. D., Sittel, F., Ernst, M., & Stock, G. (2018) · Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00759 · Cited by 71 · see citing works
“Extending previous works, 29 − 31 we propose a new algorithm that exploits this “ feature importance ” via an iterative exclusion principle (see below) in order to fi nally obtain the desired ECs.”
MethodComputing the committor with the committor to study the transition state ensemble
Kang, P., Trizio, E., & Parrinello, M. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00645-0 · Cited by 79 · see citing works
Transition Path Sampling Simulations of Biological Systems
Dellago, C., & Bolhuis, P. G. (2006) · Springer eBooks · doi:10.1007/128_085 · Cited by 76 · see citing works
Nonlinear Machine Learning of Patchy Colloid Self-Assembly Pathways and Mechanisms
Long, A., & Ferguson, A. L. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp500350b · Cited by 75 · see citing works
Separating forward and backward pathways in nonequilibrium umbrella sampling
Dickson, A., Warmflash, A., & Dinner, A. R. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3244561 · Cited by 75 · see citing works
Self-citationNetwork and graph analyses of folding free energy surfaces
Caflisch, A. (2006) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2006.01.002 · Cited by 74 · see citing works
Peters, B., Bolhuis, P. G., Mullen, R. G., & Shea, J. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4775807 · Cited by 74 · see citing works
Revisiting and Computing Reaction Coordinates with Directional Milestoning
Kirmizialtin, S., & Elber, R. (2011) · Journal of Physical Chemistry A · doi:10.1021/jp111093c · Cited by 66 · see citing works
Local initiation conditions for water autoionization
Moqadam, M., Lervik, A., Riccardi, E., Venkatraman, V., Alsberg, B. K., & van Erp, T. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714070115 · Cited by 73 · see citing works
“Machine learning (ML) applied to path-sampling data (33, 34) is a promising approach to find important collective variables that can easily be missed by human intuition.”
BackgroundNonlinear reaction coordinate analysis in the reweighted path ensemble
Lechner, W., Rogal, J., Juraszek, J., Ensing, B., & Bolhuis, P. G. (2010) · The Journal of Chemical Physics · doi:10.1063/1.3491818 · Cited by 71 · see citing works
Toward the Mechanism of Ionic Dissociation in Water
Ballard, A. J., & Christoph Dellago, A. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp309300b · Cited by 71 · see citing works
An Experimental and Computational Investigation of Spontaneous Lasso Formation in Microcin J25
Ferguson, A. L., Zhang, S., Dikiy, I., Panagiotopoulos, A. Z., Debenedetti, P. G., & Link, A. J. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2010.08.073 · Cited by 70 · see citing works
“We believe that concepts from control theory such as the single input effectiveness (47), and techniques to systematically screen pools of candidate variables such as those proposed by Ma and Dinner (48) and Trout and coworkers (49,50), may provide means to make inroads into this important problem.”
BackgroundRibeiro, J. M. L., & Tiwary, P. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00869 · Cited by 65 · see citing works
Peters, B. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3271024 · Cited by 65 · see citing works
Ravindra, P., Smith, Z., & Tiwary, P. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00115h · Cited by 67 · see citing works
Trajectory‐Based Rare Event Simulations
Bolhuis, P. G., & Christoph Dellago, A. (2010) · Reviews in computational chemistry · doi:10.1002/9780470890905.ch3 · Cited by 66 · see citing works
Allen, R. J., Valeriani, C., Tănase‐Nicola, S., ten Wolde, P. R., & Frenkel, D. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2981052 · Cited by 65 · see citing works
Perspective: Computer simulations of long time dynamics.
Elber, R. (2016) · Journal of Chemical Physics · doi:10.1063/1.4940794 · Cited by 70 · see citing works
The Free Energy Landscape Analysis of Protein (FIP35) Folding Dynamics
Krivov, S. V. (2011) · The Journal of Physical Chemistry B · doi:10.1021/jp208585r · Cited by 65 · see citing works
Learning free energy landscapes using artificial neural networks
Sidky, H., & Whitmer, J. K. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5018708 · Cited by 63 · see citing works
Stamati, H., Clementi, C., & Kavraki, L. E. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22526 · Cited by 63 · see citing works
Transition path sampling of rare events by shooting from the top
Jung, H., Okazaki, K.-I., & Hummer, G. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4997378 · Cited by 63 · see citing works
Maragliano, L., Vanden‐Eijnden, E., & Roux, B. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct900279z · Cited by 62 · see citing works
Peters, B. (2010) · Molecular Simulation · doi:10.1080/08927020903536382 · Cited by 62 · see citing works
“The GNN approach provided the first reaction coordinate to explain the role of solvent dynamics in isomerisation of an alanine dipeptide [59].”
BackgroundMethodInfluentialSolute Precipitate Nucleation: A Review of Theory and Simulation Advances
Agarwal, V., & Peters, B. (2014) · Advances in chemical physics · doi:10.1002/9781118755815.ch03 · Cited by 62 · see citing works
Machine learning assembly landscapes from particle tracking data
Long, A., Zhang, J., Granick, S., & Ferguson, A. L. (2015) · Soft Matter · doi:10.1039/c5sm01981h · Cited by 61 · see citing works
- Authors
- Ma, A., & Dinner, A. R.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Dinner, Aaron R.
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2005-02-11
- Volume
- 109
- Issue
- 14
- Pages
- 6769-6779
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- OpenAlex
- W2051116535
- Cited by
- 400
- References
- 49
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 27 · 2023: 27 · 2022: 29 · 2021: 19 · 2020: 17 · 2019: 14 · 2018: 18 · 2017: 18 · 2016: 19 · 2015: 13 · 2014: 17 · 2013: 17 · 2012: 14
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Physical Chemistry B (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- committor function, transition state, stable states, alanine dipeptide, solute-solvent coupling, artificial neural networks, genetic algorithm, explicit water
- MeSH terms
- Alanine, Automation, Carbon, Computer Simulation, Dipeptides, Models, Chemical, Models, Genetic, Models, Molecular, Models, Theoretical, Molecular Conformation, Solvents, Thermodynamics, Neural Networks, Computer, Systems Biology, Static Electricity
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