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Using sketch-map coordinates to analyze and bias molecular dynamics simulations
Open accessAbstract
When examining complex problems, such as the folding of proteins, coarse grained descriptions of the system drive our investigation and help us to rationalize the results. Oftentimes collective variables (CVs), derived through some chemical intuition about the process of interest, serve this purpose. Because finding these CVs is the most difficult part of any investigation, we recently developed a dimensionality reduction algorithm, sketch-map, that can be used to build a low-dimensional map of a phase space of high-dimensionality. In this paper we discuss how these machine-generated CVs can be used to accelerate the exploration of phase space and to reconstruct free-energy landscapes. To do so, we develop a formalism in which high-dimensional configurations are no longer represented by low-dimensional position vectors. Instead, for each configuration we calculate a probability distribution, which has a domain that encompasses the entirety of the low-dimensional space. To construct a biasing potential, we exploit an analogy with metadynamics and use the trajectory to adaptively construct a repulsive, history-dependent bias from the distributions that correspond to the previously visited configurations. This potential forces the system to explore more of phase space by making it desirable to adopt configurations whose distributions do not overlap with the bias. We apply this algorithm to a small model protein and succeed in reproducing the free-energy surface that we obtain from a parallel tempering calculation.
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“The challenge of identifying the most appropriate low-dimensional space to accelerate conformational sampling is reflected in the fact that most of early (and recent) applications of dimensionality reduction to atomistic modeling involved simulations of rare events or phase transitions 71,72,95-98 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [72]
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GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
Hess, B., Kutzner, C., van der Spoel, D., & Lindahl, E. R. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct700301q · Cited by 15,830
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Numerical Recipes, The Art of Scientific Computing
Press, W. H., Flannery, B. P., Teukolsky, S. A., Vetterling, W. T., & Gould, H. (1987) · American Journal of Physics · doi:10.1119/1.14981 · Cited by 10,032
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Schölkopf, B., Smola, A. J., & Müller, K. (1998) · Neural Computation · doi:10.1162/089976698300017467 · Cited by 8,196
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562
THE weighted histogram analysis method for free‐energy calculations on biomolecules. I. The method
Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H., & Kollman, P. A. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130812 · Cited by 6,808
Numerical Recipes: The Art of Scientific Computing
Ziegel, E. R., Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1987) · Technometrics · doi:10.2307/1269484 · Cited by 6,704
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809
Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
Self-citationUnderstanding Molecular Simulation
Frenkel, D., Smit, B., Tobochnik, J., McKay, S. R., & Christian, W. (1997) · Computers in Physics · doi:10.1063/1.4822570 · Cited by 5,395
Research works printed but not linked: 1
- DJ Wales Energy Landscapes (Cambridge Univ Press, Cambridge, UK, 2003).
Cited by
Sorted by citations
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Role of Molecular Dynamics and Related Methods in Drug Discovery
Vivo, M. D., Masetti, M., Bottegoni, G., & Cavalli, A. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01684 · Cited by 1,358
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
Self-citationUsing metadynamics to explore complex free-energy landscapes
Bussi, G., & Laio, A. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-0153-0 · Cited by 726
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 646
Self-citationBig-Data Science in Porous Materials: Materials Genomics and Machine Learning
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 656
Machine Learning for Electronically Excited States of Molecules
Westermayr, J., & Marquetand, P. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00749 · Cited by 445
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 436
Estimating the intrinsic dimension of datasets by a minimal neighborhood information
Facco, E., D’Errico, M. N., Rodríguez, Á., & Laio, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-11873-y · Cited by 360
Kinetics of protein–ligand unbinding: Predicting pathways, rates, and rate-limiting steps
Tiwary, P., Limongelli, V., Salvalaglio, M., & Parrinello, M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1424461112 · Cited by 343
Self-citationWell-Tempered Metadynamics Converges Asymptotically
Dama, J. F., Parrinello, M., & Voth, G. A. (2014) · Physical Review Letters · doi:10.1103/physrevlett.112.240602 · Cited by 347
Self-citationRohrdanz, M. A., Zheng, W., & Clementi, C. (2013) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-040412-110006 · Cited by 242
Chen, W., & Ferguson, A. L. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25520 · Cited by 244
Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics
Pfaendtner, J., & Bonomi, M. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00846 · Cited by 242
Perspective on integrating machine learning into computational chemistry and materials science
Westermayr, J., Gastegger, M., Schütt, K. T., & Maurer, R. J. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0047760 · Cited by 216
Gkeka, P., Stoltz, G., Farimani, A. B., Belkacemi, Z., Ceriotti, M., Chodera, J. D., Dinner, A. R., Ferguson, A. L., Maillet, J., Minoux, H., et al. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00355 · Cited by 222
Self-citationApplication of molecular dynamics simulation in biomedicine
Wu, X., Xu, L., Li, E., & Dong, G. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14038 · Cited by 206
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 191
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
Self-citationMachine 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 157
Manifold Learning: What, How, and Why
Meilă, M., & Zhang, H. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040522-115238 · Cited by 141
Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space
Degiacomi, M. T. (2018) · Structure · doi:10.1016/j.str.2019.03.018 · Cited by 130
Demonstrating the Transferability and the Descriptive Power of Sketch-Map
Ceriotti, M., Tribello, G. A., & Parrinello, M. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct3010563 · Cited by 126
Self-citationZhou, H.-Y., Wang, F., & Tao, P. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00652 · Cited by 119
Enhanced sampling techniques in biomolecular simulations
Spiwok, V., Sucur, Z., & Hosek, P. (2014) · Biotechnology Advances · doi:10.1016/j.biotechadv.2014.11.011 · Cited by 112
EncoderMap: Dimensionality Reduction and Generation of Molecule Conformations.
Lemke, T., & Peter, C. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00975 · Cited by 102
Free‐Energy Calculations with Metadynamics: Theory and Practice
Bussi, G., & Branduardi, D. (2015) · Reviews in computational chemistry · doi:10.1002/9781118889886.ch1 · Cited by 107
Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics
Bonomi, M., Camilloni, C., & Vendruscolo, M. (2016) · Scientific Reports · doi:10.1038/srep31232 · Cited by 98
Piotrowski, M. J., Ungureanu, C. G., Tereshchuk, P. L., Batista, K. E. A., Chaves, A. S., Guedes‐Sobrinho, D., & Silva, J. L. F. D. (2016) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.6b10404 · Cited by 101
Practical and conceptual path sampling issues
Bolhuis, P. G., & Dellago, C. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02419-6 · Cited by 94
Statistical Mechanics of the Denatured State of a Protein Using Replica-Averaged Metadynamics
Camilloni, C., & Vendruscolo, M. (2014) · Journal of the American Chemical Society · doi:10.1021/ja5027584 · Cited by 79
Stratified construction of neural network based interatomic models for multicomponent materials
Hajinazar, S., Shao, J., & Kolmogorov, A. N. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.95.014114 · Cited by 81
Bonati, L., Trizio, E., Rizzi, A., & Parrinello, M. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0156343 · Cited by 90
Self-citationDama, J. F., Rotskoff, G. M., Parrinello, M., & Voth, G. A. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500441q · Cited by 85
Self-citationUsing Dimensionality Reduction to Analyze Protein Trajectories
Tribello, G. A., & Gasparotto, P. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00046 · Cited by 76
Self-citationMoradi, M., & Tajkhorshid, E. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct5002285 · Cited by 75
Gasparotto, P., & Ceriotti, M. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4900655 · Cited by 72
Self-citationCollective variable-based enhanced sampling and machine learning
Chen, M. (2021) · The European Physical Journal B · doi:10.1140/epjb/s10051-021-00220-w · Cited by 81
Free-energy landscape of ion-channel voltage-sensor–domain activation
Delemotte, L., Kasimova, M. A., Klein, M. L., Tarek, M., & Carnevale, V. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1416959112 · Cited by 70
Analyzing and Biasing Simulations with PLUMED
Bussi, G., & Tribello, G. A. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9608-7_21 · Cited by 69
Self-citationA Review of Enhanced Sampling Approaches for Accelerated Molecular Dynamics
Tiwary, P., & van de Walle, A. (2016) · Springer series in materials science · doi:10.1007/978-3-319-33480-6_6 · Cited by 67
Hashemian, B., Millán, D., & Arroyo, M. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4830403 · Cited by 63
Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories
Spiwok, V., & Kříž, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00132 · Cited by 58
Emergent Memory and Kinetic Hysteresis in Strongly Driven Networks
Hartich, D., & Godec, A. (2021) · Physical Review X · doi:10.1103/physrevx.11.041047 · Cited by 63
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
The interaction with gold suppresses fiber-like conformations of the amyloid β (16–22) peptide
Bellucci, L., Ardèvol, A., Parrinello, M., Lutz, H., Lü, H., Weidner, T., & Corni, S. (2016) · Nanoscale · doi:10.1039/c6nr01539e · Cited by 59
Self-citationRecognizing Local and Global Structural Motifs at the Atomic Scale
Gasparotto, P., Meißner, R. H., & Ceriotti, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00993 · Cited by 50
Self-citationMAISE: Construction of neural network interatomic models and evolutionary structure optimization
Hajinazar, S., Thorn, A., Sandoval, E. D., Kharabadze, S., & Kolmogorov, A. N. (2020) · Computer Physics Communications · doi:10.1016/j.cpc.2020.107679 · Cited by 59
Deep learning collective variables from transition path ensemble.
Ray, D., Trizio, E., & Parrinello, M. (2023) · Journal of Chemical Physics · doi:10.1063/5.0148872 · Cited by 54
Self-citationLi, X., Xie, Y. Y., Hu, D., & Lan, Z. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00394 · Cited by 55
Elbaz, Y., Furman, D., & Toroker, M. C. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201900778 · Cited by 54
Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
Zhu, K., Trizio, E., Zhang, J., Hu, R., Jiang, L., Hou, T., & Bonati, L. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00700 · Cited by 65
Thiemann, F. L., Rowe, P., Müller, E. A., & Michaelides, A. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c05831 · Cited by 57
Explore Protein Conformational Space With Variational Autoencoder
Tian, H., Jiang, X., Trozzi, F., Xiao, S., Larson, E. C., & Tao, P. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.781635 · Cited by 51
Representations in neural network based empirical potentials
Cubuk, E. D., Malone, B. D., Onat, B., Waterland, A., & Kaxiras, E. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4990503 · Cited by 51
Rydzewski, J., & Nowak, W. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00212 · Cited by 45
Data-Driven Molecular Dynamics: A Multifaceted Challenge
Bernetti, M., Bertazzo, M., & Masetti, M. (2020) · Pharmaceuticals · doi:10.3390/ph13090253 · Cited by 43
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 45
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
Self-citationAdversarial reverse mapping of equilibrated condensed-phase molecular structures
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Dama, J. F., Hocky, G. M., Sun, R., & Voth, G. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00907 · Cited by 39
Rydzewski, J., & Valsson, Ó. (2021) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.1c02869 · Cited by 38
Voı̈tchovsky, K., Giofré, D., Segura, J. J., Stellacci, F., & Ceriotti, M. (2016) · Nature Communications · doi:10.1038/ncomms13064 · Cited by 39
Self-citationRossi, K., Jurásková, V., Wischert, R., Garel, L., Corminbœuf, C., & Ceriotti, M. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00362 · Cited by 39
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Badaoui, M., Buigues, P. J., Berta, D., Mandana, G. M., Gu, H., Földes, T., Dickson, C. J., Horn̆ák, V., Kato, M., Molteni, C., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00924 · Cited by 39
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Zheng, S., & Pfaendtner, J. (2014) · The Journal of Physical Chemistry C · doi:10.1021/jp500398k · Cited by 35
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Tiwary, P., & van de Walle, A. (2013) · Physical Review B · doi:10.1103/physrevb.87.094304 · Cited by 34
Kukharenko, O., Sawade, K., Steuer, J., & Peter, C. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00503 · Cited by 32
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Li, Y., Li, X., Ma, W., & Dong, Z. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500162b · Cited by 28
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Metadata
- Authors
- Tribello, G. A., Ceriotti, M., & Parrinello, M.
- Affiliations
- ETH Zurich (CH)
- University of Oxford (GB)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-03-16
- Volume
- 109
- Issue
- 14
- Pages
- 5196-5201
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3325650
- OpenAlex
- W2038458884
- Cited by
- 202
- References
- 48
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 5 · 2023: 9 · 2022: 19 · 2021: 20 · 2020: 18 · 2019: 16 · 2018: 12 · 2017: 40 · 2016: 15 · 2015: 13 · 2014: 10 · 2013: 5 · 2012: 3
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Research Portal (Queen's University Belfast) (submittedVersion, other-oa)
- Funders
- FP7 Ideas: European Research Council (247075)
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- sketch maps, dimensionality reduction, collective variables, free energy landscape, biasing potential, metadynamics, phase space exploration, protein folding, coarse-grained models, parallel tempering
- MeSH terms
- Probability, Protein Folding, Molecular Dynamics Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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