Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

Rohrdanz, Mary A.; Zheng, Wenwei; Clementi, Cecilia

doi:10.1146/annurev-physchem-040412-110006

Cited by 242Protein Structure and DynamicsAdvanced Physical and Chemical Molecular InteractionsComputational Drug Discovery Methods

Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

Rohrdanz, Mary A., Zheng, Wenwei, Clementi, Cecilia·2013·Annual Review of Physical Chemistry

DOI 10.1146/annurev-physchem-040412-110006

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Abstract

The long-timescale dynamics of macromolecular systems can be oftentimes viewed as a reaction connecting metastable states of the system. In the past decade, various approaches have been developed to discover the collective motions associated with this dynamics. The corresponding collective variables are used in many applications, e.g., to understand the reaction mechanism, to quantify the system's free energy landscape, to enhance the sampling of the reaction path, and to determine the reaction rate. In this review we focus on a number of key developments in this field, providing an overview of several methods along with their relative regimes of applicability.

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Diffusion maps try to link geometric data to the characteristic time scale of the system's motion.
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Virshup, A. M., Chen, J., & Martı́nez, T. J. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4742066 · Cited by 70 · see citing works
Simulating the T-Jump-Triggered Unfolding Dynamics of trpzip2 Peptide and Its Time-Resolved IR and Two-Dimensional IR Signals Using the Markov State Model Approach
Zhuang, W., Cui, R. Z., Silva, D.-A., & Huang, X. (2011) · The Journal of Physical Chemistry B · doi:10.1021/jp109592b · Cited by 70 · see citing works
Multiple state transition path sampling
Rogal, J., & Bolhuis, P. G. (2008) · The Journal of Chemical Physics · doi:10.1063/1.3029696 · Cited by 67 · see citing works
Unassisted Transport of N-Acetyl- l -tryptophanamide through Membrane: Experiment and Simulation of Kinetics
Cárdenas, A. E., Jas, G. S., DeLeon‐Pennell, K. Y., Hegefeld, W. A., Kuczera, K., & Elber, R. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp2102447 · Cited by 60 · see citing works
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Lätzer, J., Shen, T., & Wolynes, P. G. (2008) · Biochemistry · doi:10.1021/bi701350v · Cited by 57 · see citing works
Multiscale Estimation of Intrinsic Dimensionality of Data Sets
Little, A. V., Jung, Y.-M., & Maggioni, M. (2009) · National Conference on Artificial Intelligence · Cited by 54 · see citing works
The folding energy landscape and phosphorylation: modeling the conformational switch of the NFAT regulatory domain
Shen, T., Zong, C. C., Hamelberg, D., McCammon, J. A., & Wolynes, P. G. (2005) · The FASEB Journal · doi:10.1096/fj.04-3590hyp · Cited by 49 · see citing works
Toward Identification of the Reaction Coordinate Directly from the Transition State Ensemble Using the Kernel PCA Method
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Flexibility of Monomeric and Dimeric HIV-1 Protease
Levy, Y., & Caflisch, A. (2003) · The Journal of Physical Chemistry B · doi:10.1021/jp0219956 · Cited by 43 · see citing works
Dynamic and structural analysis of isotropically distributed molecular ensembles
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Delineation of Folding Pathways of a β-Sheet Miniprotein
Zheng, W., Qi, B., Rohrdanz, M. A., Caflisch, A., Dinner, A. R., & Clementi, C. (2011) · The Journal of Physical Chemistry B · doi:10.1021/jp2076935 · Cited by 39 · see citing works
Self-citation
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Becker, N. B., Allen, R. J., & ten Wolde, P. R. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4704810 · Cited by 38 · see citing works
Transition path sampling of protein conformational changes
Juraszek, J., Vreede, J., & Bolhuis, P. G. (2011) · Chemical Physics · doi:10.1016/j.chemphys.2011.04.032 · Cited by 37 · see citing works
Eliminating fast reactions in stochastic simulations of biochemical networks: A bistable genetic switch
Morelli, M. J., Allen, R. J., Tănase‐Nicola, S., & ten Wolde, P. R. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2821957 · Cited by 37 · see citing works
The rate constant of polymer reversal inside a pore
Huang, L., & Makarov, D. E. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2890006 · Cited by 36 · see citing works
Kinetics and mechanism of the unfolding native-to-loop transition of Trp-cage in explicit solvent via optimized forward flux sampling simulations
Velez‐Vega, C., Borrero, E. E., & Escobedo, F. A. (2010) · The Journal of Chemical Physics · doi:10.1063/1.3474803 · Cited by 33 · see citing works
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Nolde, S. B., Arseniev, A. S., Orekhov, V., & Billeter, M. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10030 · Cited by 28 · see citing works
Exploring the free energy surfaces of clusters using reconnaissance metadynamics
Tribello, G. A., Cuny, J., Eshet, H., & Parrinello, M. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3628676 · Cited by 26 · see citing works
The Fast and the Slow: Folding and Trapping of λ6–85
Prigozhin, M. B., & Gruebele, M. (2011) · Journal of the American Chemical Society · doi:10.1021/ja209073z · Cited by 25 · see citing works
Folding kinetics of a polymer
Růžička, Š., Quigley, D., & Allen, M. P. (2012) · Physical Chemistry Chemical Physics · doi:10.1039/c2cp00051b · Cited by 18 · see citing works
The Transmission Coefficient in Reaction Rate Theory
Eyring, H. B. (1962) · Reviews of Modern Physics · doi:10.1103/revmodphys.34.616 · Cited by 15 · see citing works
Sequence-Specific Recognition of Cancer Drug-DNA Adducts by HMGB1a Repair Protein
Elder, R. M., & Jayaraman, A. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2012.04.013 · Cited by 13 · see citing works
Simulation studies of the fidelity of biomolecular structure ensemble recreation
Lätzer, J., Eastwood, M. P., & Wolynes, P. G. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2375121 · Cited by 12 · see citing works
Optimization and dynamics of protein–protein complexes using B‐splines
Gillilan, R. E., & Lilien, R. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20088 · Cited by 7 · see citing works
Nucleation of wetting films on cylindrical and spherical substrates: A numerical study by the string method
Qiu, C., & Qian, T. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3239462 · Cited by 6 · see citing works
Simulation of structural phase transitions by metadynamicsPreprint
Martoňák, R., Laio, A., Bernasconi, M., Ceriani, C., Raiteri, P., & Parrinello, M. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.cond-mat/0411559 · Cited by 1 · see citing works

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Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945 · see citing works
PLUMED 2: New feathers for an old bird
Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C., & Bussi, G. (2013) · Computer Physics Communications · doi:10.1016/j.cpc.2013.09.018 · Cited by 3,427 · see citing works
Comparing molecules and solids across structural and alchemical space
De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00415f · Cited by 778 · see citing works
Enhancing Important Fluctuations: Rare Events and Metadynamics from a Conceptual Viewpoint
Valsson, Ó., Tiwary, P., & Parrinello, M. (2016) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-040215-112229 · Cited by 619 · see citing works
Modeling and Simulations of Polymers: A Roadmap
Gartner, T. E., & Jayaraman, A. (2019) · Macromolecules · doi:10.1021/acs.macromol.8b01836 · Cited by 556 · see citing works
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Cśanyi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 556 · see citing works
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A., Ortner, C., Cśanyi, G., Ceriotti, M., et al. (2021) · Cambridge University Engineering Department Publications Database · Cited by 440 · see citing works
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Wehmeyer, C., & Noé, F. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5011399 · Cited by 401 · see citing works
Unsupervised Learning Methods for Molecular Simulation Data
Glielmo, A., Husic, B. E., Rodríguez, Á., Clementi, C., Noé, F., & Laio, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01195 · Cited by 408 · see citing works
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Practical Aspects of Free-Energy Calculations: A Review
Hansen, N., & van Gunsteren, W. F. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500161f · Cited by 384 · see citing works
Principal component analysis of molecular dynamics: On the use of Cartesian vs. internal coordinates
Sittel, F., Jain, A., & Stock, G. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4885338 · Cited by 268 · see citing works
Sparse learning of stochastic dynamical equations
Boninsegna, L., Nüske, F., & Clementi, C. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5018409 · Cited by 262 · see citing works
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Deep learning the slow modes for rare events sampling
Bonati, L., Piccini, G., & Parrinello, M. (2021) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2113533118 · Cited by 260 · see citing works
Molecular enhanced sampling with autoencoders: On‐the‐fly collective variable discovery and accelerated free energy landscape exploration
Chen, W., & Ferguson, A. L. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25520 · Cited by 234 · see citing works
Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation
Noé, F., & Clementi, C. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00553 · Cited by 214 · see citing works
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Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics
Maximova, T., Moffatt, R., Ma, B., Nussinov, R., & Shehu, A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004619 · Cited by 211 · see citing works
Strategies for the exploration of free energy landscapes: Unity in diversity and challenges ahead
Pietrucci, F. (2017) · Reviews in Physics · doi:10.1016/j.revip.2017.05.001 · Cited by 184 · see citing works
Unsupervised machine learning in atomistic simulations, between predictions and understanding
Ceriotti, M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5091842 · Cited by 173 · see citing works
tICA-Metadynamics: Accelerating Metadynamics by Using Kinetically Selected Collective Variables
Sultan, M. M., & Pande, V. S. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00182 · Cited by 167 · see citing works
Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods
Noé, F., & Clementi, C. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.02.006 · Cited by 161 · see citing works
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Sittel, F., & Stock, G. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5049637 · Cited by 146 · see citing works
Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design
Chen, W., Tan, A. R., & Ferguson, A. L. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5023804 · Cited by 143 · see citing works
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
Sidky, H., Chen, W., & Ferguson, A. L. (2020) · Molecular Physics · doi:10.1080/00268976.2020.1737742 · Cited by 147 · see citing works
Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules
Wang, W., Cao, S., Zhu, L., & Huang, X. (2017) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1343 · Cited by 145 · see citing works
Automated design of collective variables using supervised machine learning
Sultan, M. M., & Pande, V. S. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5029972 · Cited by 137 · see citing works
Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations
Wu, H., Nüske, F., Paul, F., Klus, S., Koltai, P., & Noé, F. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4979344 · Cited by 140 · see citing works
Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces
Schneider, E., Dai, L., Topper, R. Q., Drechsel‐Grau, C., & Tuckerman, M. E. (2017) · Physical Review Letters · doi:10.1103/physrevlett.119.150601 · Cited by 139 · see citing works
Fast recovery of free energy landscapes via diffusion-map-directed molecular dynamics
Preto, J., & Clementi, C. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c3cp54520b · Cited by 129 · see citing works
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Intrinsic map dynamics exploration for uncharted effective free-energy landscapes
Chiavazzo, E., Covino, R., Coifman, R. R., Gear, C. W., Georgiou, A. S., Hummer, G., & Kevrekidis, I. G. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621481114 · Cited by 125 · see citing works
Demonstrating an Order-of-Magnitude Sampling Enhancement in Molecular Dynamics Simulations of Complex Protein Systems
Pan, A. C., Weinreich, T. M., Piana, S., & Shaw, D. E. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00913 · Cited by 119 · see citing works
Transferable Neural Networks for Enhanced Sampling of Protein Dynamics.
Sultan, M. M., Wayment-Steele, H. K., & Pande, V. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00025 · Cited by 110 · see citing works
Contact- and distance-based principal component analysis of protein dynamics
Ernst, M., Sittel, F., & Stock, G. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4938249 · Cited by 105 · see citing works
Perspective: Computational chemistry software and its advancement as illustrated through three grand challenge cases for molecular science
Krylov, A. I., Windus, T. L., Barnes, T., Marin‐Rimoldi, E., Nash, J. A., Pritchard, B. P., Smith, D. G. A., Altarawy, D., Saxe, P. W., Clementi, C., et al. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5052551 · Cited by 99 · see citing works
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Conformational landscapes of membrane proteins delineated by enhanced sampling molecular dynamics simulations
Harpole, T. J., & Delemotte, L. (2017) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2017.10.033 · Cited by 94 · see citing works
Rapid exploration of configuration space with diffusion-map-directed molecular dynamics
Zheng, W., Rohrdanz, M. A., & Clementi, C. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp401911h · Cited by 86 · see citing works
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Coarse-grained modelling out of equilibrium
Schilling, T. (2021) · Physics reports · doi:10.1016/j.physrep.2022.04.006 · Cited by 92 · see citing works
Hierarchical visualization of materials space with graph convolutional neural networks
Xie, T., & Grossman, J. C. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5047803 · Cited by 81 · see citing works
Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics
Schuetz, D. A., Bernetti, M., Bertazzo, M., Müsil, D., Eggenweiler, H., Recanatini, M., Masetti, M., Ecker, G. F., & Cavalli, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00614 · Cited by 79 · see citing works
Robust Density-Based Clustering To Identify Metastable Conformational States of Proteins
Sittel, F., & Stock, G. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b01233 · Cited by 76 · see citing works
Recent 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
Machine Learning of Biomolecular Reaction Coordinates
Brandt, S., Sittel, F., Ernst, M., & Stock, G. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00759 · Cited by 73 · see citing works
Principal component analysis on a torus: Theory and application to protein dynamics.
Sittel, F., Filk, T., & Stock, G. (2017) · Journal of Chemical Physics · doi:10.1063/1.4998259 · Cited by 64 · see citing works
Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond
Gasparotto, P., & Ceriotti, M. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4900655 · Cited by 72 · see citing works
Variational selection of features for molecular kinetics
Scherer, M. K., Husic, B. E., Hoffmann, M., Paul, F., Wu, H., & Noé, F. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5083040 · Cited by 63 · see citing works
Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems
Ravindra, P., Smith, Z., & Tiwary, P. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00115h · Cited by 67 · see citing works
Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems
Peng, J., Wang, W., Yu, Y., Gu, H.-L., & Huang, X.-H. (2018) · Chinese Journal of Chemical Physics · doi:10.1063/1674-0068/31/cjcp1806147 · Cited by 52 · see citing works
Efficient Construction of Free Energy Profiles of Breathing Metal–Organic Frameworks Using Advanced Molecular Dynamics Simulations
Demuynck, R., Rogge, S. M. J., Vanduyfhuys, L., Wieme, J., Waroquier, M., & Speybroeck, V. V. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b01014 · Cited by 66 · see citing works
Learning To Fold Proteins Using Energy Landscape Theory
Schafer, N. P., Kim, B. L., Zheng, W., & Wolynes, P. G. (2014) · Israel Journal of Chemistry · doi:10.1002/ijch.201300145 · Cited by 66 · see citing works
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Ligand diffusion in proteins via enhanced sampling in molecular dynamics
Rydzewski, J., & Nowak, W. (2017) · Physics of Life Reviews · doi:10.1016/j.plrev.2017.03.003 · Cited by 63 · see citing works
Quantitative comparison of adaptive sampling methods for protein dynamics
Hruška, E., Abella, J. R., Nüske, F., Kavraki, L. E., & Clementi, C. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5053582 · Cited by 67 · see citing works
Self-citation
Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables
Hashemian, B., Millán, D., & Arroyo, M. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4830403 · Cited by 63 · see citing works
On the intrinsic dynamic nature of the rigid UiO-66 metal–organic framework
Hajek, J., Caratelli, C., Demuynck, R., Wispelaere, K. D., Vanduyfhuys, L., Waroquier, M., & Speybroeck, V. V. (2018) · Chemical Science · doi:10.1039/c7sc04947a · Cited by 63 · see citing works
Computational methods for exploring protein conformations
Allison, J. R. (2020) · Biochemical Society Transactions · doi:10.1042/bst20200193 · Cited by 54 · see citing works
Challenges in computational studies of enzyme structure, function and dynamics
Carvalho, A. T. P., Barrozo, A. H., Doron, D., Vardi‐Kilshtain, A., Major, D. T., & Kamerlin, S. C. L. (2014) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2014.09.003 · Cited by 60 · see citing works
Systematic characterization of protein folding pathways using diffusion maps: Application to Trp-cage miniprotein
Kim, S. B., Dsilva, C. J., Kevrekidis, I. G., & Debenedetti, P. G. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4913322 · Cited by 58 · see citing works
A Machine-Driven Hunt for Global Reaction Coordinates of Azobenzene Photoisomerization
Tavadze, P., Avendaño-Franco, G., Ren, P., Wen, X., Li, Y., & Lewis, J. P. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.7b10030 · Cited by 58 · see citing works
Principal component analysis of nonequilibrium molecular dynamics simulations
Post, M., Wolf, S., & Stock, G. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5089636 · Cited by 57 · see citing works
Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach
Wang, J., Chmiela, S., Müller, K., Noé, F., & Clementi, C. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0007276 · Cited by 52 · see citing works
Self-citation
Common Features of Extraordinary Rate Theories
Peters, B. (2015) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5b02547 · Cited by 53 · see citing works
Analysis of the Geometrical Evolution in On-the-Fly Surface-Hopping Nonadiabatic Dynamics with Machine Learning Dimensionality Reduction Approaches: Classical Multidimensional Scaling and Isometric Feature Mapping
Li, X., Xie, Y. Y., Hu, D., & Lan, Z. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00394 · Cited by 53 · see citing works
Commute Maps: Separating Slowly Mixing Molecular Configurations for Kinetic Modeling
Noé, F., Banisch, R., & Clementi, C. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00762 · Cited by 53 · see citing works
Self-citation
Correlation-Based Feature Selection to Identify Functional Dynamics in Proteins
Diez, G., Nagel, D., & Stock, G. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00337 · Cited by 52 · see citing works
Phase space and collective variable based simulation methods for studies of rare events
Paul, S., Nair, N. N., & Vashisth, H. (2019) · Molecular Simulation · doi:10.1080/08927022.2019.1634268 · Cited by 50 · see citing works
Transition-Path Probability as a Test of Reaction-Coordinate Quality Reveals DNA Hairpin Folding Is a One-Dimensional Diffusive Process
Neupane, K., Manuel, A. P., Lambert, J., & Woodside, M. T. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00176 · Cited by 48 · see citing works
Non-Markovian out-of-equilibrium dynamics: A general numerical procedure to construct time-dependent memory kernels for coarse-grained observables
Meyer, H., Pelagejcev, P., & Schilling, T. (2020) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/128/40001 · Cited by 48 · see citing works
Hierarchical Folding Free Energy Landscape of HP35 Revealed by Most Probable Path Clustering
Jain, A., & Stock, G. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp410398a · Cited by 48 · see citing works
Colloidal crystal grain boundary formation and motion
Edwards, T. D., Yang, Y., Beltran-Villegas, D. J., & Bevan, M. A. (2014) · Scientific Reports · doi:10.1038/srep06132 · Cited by 45 · see citing works
Prebiotic chemistry and origins of life research with atomistic computer simulations
Pérez-Villa, A., Pietrucci, F., & Saitta, A. M. (2018) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.09.004 · Cited by 45 · see citing works
Advances in enhanced sampling molecular dynamics simulations for biomolecules
Wang, A., Zhang, Z., & Li, G. (2019) · Chinese Journal of Chemical Physics · doi:10.1063/1674-0068/cjcp1905091 · Cited by 45 · see citing works
Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning.
Fu, H.-H., Bian, H., Shao, X.-G., & Cai, W.-S. (2024) · Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c03542 · Cited by 39 · see citing works
Data-Driven Molecular Dynamics: A Multifaceted Challenge
Bernetti, M., Bertazzo, M., & Masetti, M. (2020) · Pharmaceuticals · doi:10.3390/ph13090253 · Cited by 43 · see citing works
Identification and Validation of Reaction Coordinates Describing Protein Functional Motion: Hierarchical Dynamics of T4 Lysozyme
Ernst, M., Wolf, S., & Stock, G. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00571 · Cited by 42 · see citing works
Reaction coordinates in complex systems-a perspective
Rogal, J. (2021) · The European Physical Journal B · doi:10.1140/epjb/s10051-021-00233-5 · Cited by 43 · see citing works
Mapping and classifying molecules from a high-throughput structural database
De, S., Musil, F., Ingram, T., Baldauf, C., & Ceriotti, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0192-4 · Cited by 42 · see citing works
Sampling large conformational transitions: adenylate kinase as a testing ground
Seyler, S. L., & Beckstein, O. (2014) · Molecular Simulation · doi:10.1080/08927022.2014.919497 · Cited by 42 · see citing works
Epithelial-to-mesenchymal transition proceeds through directional destabilization of multidimensional attractor
Wang, W., Poe, D., Yang, Y., Hyatt, T., & Xing, J. (2022) · eLife · doi:10.7554/elife.74866 · Cited by 39 · see citing works
Multi-dimensional spectral gap optimization of order parameters (SGOOP) through conditional probability factorization
Smith, Z., Pramanik, D., Tsai, S.-T., & Tiwary, P. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5064856 · Cited by 40 · see citing works
Ab initio enhanced sampling kinetic study on MTO ethene methylation reaction
Bailleul, S., Dedecker, K., Cnudde, P., Vanduyfhuys, L., Waroquier, M., & Speybroeck, V. V. (2020) · Journal of Catalysis · doi:10.1016/j.jcat.2020.04.015 · Cited by 39 · see citing works
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Authors: Rohrdanz, M. A., Zheng, W., & Clementi, C.
Affiliations: Rice University (US)
ORCID: Zheng, Wenwei
Clementi, Cecilia
Venue: Annual Review of Physical Chemistry
Publisher: Annual Reviews
Published: 2013-04-01
Volume: 64
Issue: 1
Pages: 295-316
ISSN: 0066-426X (ISSN-L)
1545-1593
DOI: 10.1146/annurev-physchem-040412-110006
OpenAlex: W2110497788
Cited by: 242
References: 137
Citations by year: 2026: 6 · 2025: 10 · 2024: 13 · 2023: 17 · 2022: 12 · 2021: 17 · 2020: 15 · 2019: 22 · 2018: 28 · 2017: 54 · 2016: 18 · 2015: 13 · 2014: 12 · 2013: 5
FWCI: 1.50
Type: journal article
Language: en
Open access: closed
Locations: Annual Review of Physical Chemistry (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation
Division of Chemistry
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 › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords: reaction coordinate, reaction pathways, collective variables, metastable states, free energy landscape, enhanced sampling, reaction mechanism, reaction rate, molecular dynamics

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Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

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