ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
CONTENT TYPES

Figure 1Loading Img

Well-Tempered Variational Approach to Enhanced Sampling

View Author Information
Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
Facoltà di Informatica, Instituto di Scienze Computationali, and National Center for Computational Design and Discovery of Novel Materials MARVEL, Università della Svizzera italiana (USI), Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
Cite this: J. Chem. Theory Comput. 2015, 11, 5, 1996–2002
Publication Date (Web):April 20, 2015
https://doi.org/10.1021/acs.jctc.5b00076
Copyright © 2015 American Chemical Society

    Article Views

    1642

    Altmetric

    -

    Citations

    LEARN ABOUT THESE METRICS
    Other access options
    Supporting Info (1)»

    Abstract

    Abstract Image

    We propose a simple yet effective iterative scheme that allows us to employ the well-tempered distribution as a target distribution for the collective variables in our recently introduced variational approach to enhanced sampling and free energy calculations [Valsson and Parrinello Phys. Rev. Lett. 2014, 113, 090601]. The performance of the scheme is evaluated for the three-dimensional free energy surface of alanine tetrapeptide where the convergence can be rather poor when employing the uniform target distribution. Using the well-tempered target distribution on the other hand results in a significant improvement in convergence. The results observed in this paper indicate that the well-tempered distribution is in most cases the preferred and recommended choice for the target distribution in the variational approach.

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. You can change your affiliated institution below.

    Supporting Information

    ARTICLE SECTIONS
    Jump To

    Discussion on the averaged update option for the well-tempered target distribution. Evaluation of computational options for updating the well-tempered target distribution. Additional convergence results. Reference free energy surfaces used for the ε error metric. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jctc.5b00076.

    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    This article is cited by 41 publications.

    1. Benjamin Pampel, Omar Valsson. Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials. Journal of Chemical Theory and Computation 2022, 18 (7) , 4127-4141. https://doi.org/10.1021/acs.jctc.2c00197
    2. Kristof M. Bal. Reweighted Jarzynski Sampling: Acceleration of Rare Events and Free Energy Calculation with a Bias Potential Learned from Nonequilibrium Work. Journal of Chemical Theory and Computation 2021, 17 (11) , 6766-6774. https://doi.org/10.1021/acs.jctc.1c00574
    3. Zachary Smith, Pratyush Tiwary. Making High-Dimensional Molecular Distribution Functions Tractable through Belief Propagation on Factor Graphs. The Journal of Physical Chemistry B 2021, 125 (40) , 11150-11158. https://doi.org/10.1021/acs.jpcb.1c05717
    4. Jakub Rydzewski, Omar Valsson. Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling. The Journal of Physical Chemistry A 2021, 125 (28) , 6286-6302. https://doi.org/10.1021/acs.jpca.1c02869
    5. Michele Invernizzi, Michele Parrinello. Rethinking Metadynamics: From Bias Potentials to Probability Distributions. The Journal of Physical Chemistry Letters 2020, 11 (7) , 2731-2736. https://doi.org/10.1021/acs.jpclett.0c00497
    6. Kristof M. Bal, Annemie Bogaerts, Erik C. Neyts. Ensemble-Based Molecular Simulation of Chemical Reactions under Vibrational Nonequilibrium. The Journal of Physical Chemistry Letters 2020, 11 (2) , 401-406. https://doi.org/10.1021/acs.jpclett.9b03356
    7. Jayashrita Debnath, Michele Invernizzi, Michele Parrinello. Enhanced Sampling of Transition States. Journal of Chemical Theory and Computation 2019, 15 (4) , 2454-2459. https://doi.org/10.1021/acs.jctc.8b01283
    8. Michele Invernizzi, Michele Parrinello. Making the Best of a Bad Situation: A Multiscale Approach to Free Energy Calculation. Journal of Chemical Theory and Computation 2019, 15 (4) , 2187-2194. https://doi.org/10.1021/acs.jctc.9b00032
    9. Zhongji Pu, Mengdi Zhao, Yue Zhang, Wenhui Sun, Yongming Bao. Dynamic Description of the Catalytic Cycle of Malate Enzyme: Stereoselective Recognition of Substrate, Chemical Reaction, and Ligand Release. The Journal of Physical Chemistry B 2018, 122 (51) , 12241-12250. https://doi.org/10.1021/acs.jpcb.8b05135
    10. Yi Isaac Yang, Haiyang Niu, Michele Parrinello. Combining Metadynamics and Integrated Tempering Sampling. The Journal of Physical Chemistry Letters 2018, 9 (22) , 6426-6430. https://doi.org/10.1021/acs.jpclett.8b03005
    11. Tarak Karmakar, Pablo M. Piaggi, Claudio Perego, Michele Parrinello. A Cannibalistic Approach to Grand Canonical Crystal Growth. Journal of Chemical Theory and Computation 2018, 14 (5) , 2678-2683. https://doi.org/10.1021/acs.jctc.8b00191
    12. Ferruccio Palazzesi, Omar Valsson, and Michele Parrinello . Conformational Entropy as Collective Variable for Proteins. The Journal of Physical Chemistry Letters 2017, 8 (19) , 4752-4756. https://doi.org/10.1021/acs.jpclett.7b01770
    13. Raimondas Galvelis and Yuji Sugita . Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics. Journal of Chemical Theory and Computation 2017, 13 (6) , 2489-2500. https://doi.org/10.1021/acs.jctc.7b00188
    14. Marco Nava, Ferruccio Palazzesi, Claudio Perego, and Michele Parrinello . Dimer Metadynamics. Journal of Chemical Theory and Computation 2017, 13 (2) , 425-430. https://doi.org/10.1021/acs.jctc.6b00691
    15. James McCarty, Omar Valsson, and Michele Parrinello . Bespoke Bias for Obtaining Free Energy Differences within Variationally Enhanced Sampling. Journal of Chemical Theory and Computation 2016, 12 (5) , 2162-2169. https://doi.org/10.1021/acs.jctc.6b00125
    16. Jakub Rydzewski, Ming Chen, Omar Valsson. Manifold learning in atomistic simulations: a conceptual review. Machine Learning: Science and Technology 2023, 4 (3) , 031001. https://doi.org/10.1088/2632-2153/ace81a
    17. Baltzar Stevensson, Mattias Edén. Improved reweighting protocols for variationally enhanced sampling simulations with multiple walkers. Physical Chemistry Chemical Physics 2023, 25 (33) , 22063-22078. https://doi.org/10.1039/D2CP04009C
    18. Benjamin Pampel, Simon Holbach, Lisa Hartung, Omar Valsson. Sampling rare event energy landscapes via birth-death augmented dynamics. Physical Review E 2023, 107 (2) https://doi.org/10.1103/PhysRevE.107.024141
    19. Yuki Mitsuta, Toshio Asada, Yasuteru Shigeta. Calculation of the permeability coefficients of small molecules through lipid bilayers by free-energy reaction network analysis following the explicit treatment of the internal conformation of the solute. Physical Chemistry Chemical Physics 2022, 24 (42) , 26070-26082. https://doi.org/10.1039/D2CP03678A
    20. Pablo M. Piaggi, Roberto Car. Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations. Molecular Physics 2021, 119 (19-20) https://doi.org/10.1080/00268976.2021.1916634
    21. Pablo M. Piaggi, Roberto Car. Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations. The Journal of Chemical Physics 2020, 152 (20) https://doi.org/10.1063/5.0011140
    22. Omar Valsson, Michele Parrinello. Variationally Enhanced Sampling. 2020, 621-634. https://doi.org/10.1007/978-3-319-44677-6_50
    23. Luigi Bonati, Yue-Yu Zhang, Michele Parrinello. Neural networks-based variationally enhanced sampling. Proceedings of the National Academy of Sciences 2019, 116 (36) , 17641-17647. https://doi.org/10.1073/pnas.1907975116
    24. Pablo M. Piaggi, Michele Parrinello. Calculation of phase diagrams in the multithermal-multibaric ensemble. The Journal of Chemical Physics 2019, 150 (24) https://doi.org/10.1063/1.5102104
    25. Puja Banerjee, Sayantan Mondal, Biman Bagchi. Effect of ethanol on insulin dimer dissociation. The Journal of Chemical Physics 2019, 150 (8) https://doi.org/10.1063/1.5079501
    26. Pablo M. Piaggi, Michele Parrinello. Multithermal-Multibaric Molecular Simulations from a Variational Principle. Physical Review Letters 2019, 122 (5) https://doi.org/10.1103/PhysRevLett.122.050601
    27. Giovanni Bussi, Gareth A. Tribello. Analyzing and Biasing Simulations with PLUMED. 2019, 529-578. https://doi.org/10.1007/978-1-4939-9608-7_21
    28. Samuel Alexander Jobbins, Salah Eddine Boulfelfel, Stefano Leoni. Metashooting: a novel tool for free energy reconstruction from polymorphic phase transition mechanisms. Faraday Discussions 2018, 211 , 235-251. https://doi.org/10.1039/C8FD00053K
    29. Andrea Cesari, Sabine Reißer, Giovanni Bussi. Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments. Computation 2018, 6 (1) , 15. https://doi.org/10.3390/computation6010015
    30. Omar Valsson, Michele Parrinello. Variationally Enhanced Sampling. 2018, 1-14. https://doi.org/10.1007/978-3-319-42913-7_50-1
    31. Hongrui Wang, Hongwei Liu, Leixin Cai, Caixia Wang, Qiang Lv. Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein–small molecule docking. BMC Bioinformatics 2017, 18 (1) https://doi.org/10.1186/s12859-017-1733-6
    32. Pablo M. Piaggi, Omar Valsson, Michele Parrinello. Enhancing Entropy and Enthalpy Fluctuations to Drive Crystallization in Atomistic Simulations. Physical Review Letters 2017, 119 (1) https://doi.org/10.1103/PhysRevLett.119.015701
    33. Michele Invernizzi, Omar Valsson, Michele Parrinello. Coarse graining from variationally enhanced sampling applied to the Ginzburg–Landau model. Proceedings of the National Academy of Sciences 2017, 114 (13) , 3370-3374. https://doi.org/10.1073/pnas.1618455114
    34. Yong Wang, João Miguel Martins, Kresten Lindorff-Larsen. Biomolecular conformational changes and ligand binding: from kinetics to thermodynamics. Chemical Science 2017, 8 (9) , 6466-6473. https://doi.org/10.1039/C7SC01627A
    35. C. Perego, F. Giberti, M. Parrinello. Chemical potential calculations in dense liquids using metadynamics. The European Physical Journal Special Topics 2016, 225 (8-9) , 1621-1628. https://doi.org/10.1140/epjst/e2016-60094-x
    36. Omar Valsson, Pratyush Tiwary, Michele Parrinello. Enhancing Important Fluctuations: Rare Events and Metadynamics from a Conceptual Viewpoint. Annual Review of Physical Chemistry 2016, 67 (1) , 159-184. https://doi.org/10.1146/annurev-physchem-040215-112229
    37. Claudio Toniolo, Pierandrea Temussi. Conformational flexibility of aspartame. Peptide Science 2016, 106 (3) , 376-384. https://doi.org/10.1002/bip.22847
    38. Pratyush Tiwary, B. J. Berne. Spectral gap optimization of order parameters for sampling complex molecular systems. Proceedings of the National Academy of Sciences 2016, 113 (11) , 2839-2844. https://doi.org/10.1073/pnas.1600917113
    39. Patrick Shaffer, Omar Valsson, Michele Parrinello. Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin. Proceedings of the National Academy of Sciences 2016, 113 (5) , 1150-1155. https://doi.org/10.1073/pnas.1519712113
    40. Pablo M. Piaggi, Omar Valsson, Michele Parrinello. A variational approach to nucleation simulation. Faraday Discussions 2016, 195 , 557-568. https://doi.org/10.1039/C6FD00127K
    41. James McCarty, Omar Valsson, Pratyush Tiwary, Michele Parrinello. Variationally Optimized Free-Energy Flooding for Rate Calculation. Physical Review Letters 2015, 115 (7) https://doi.org/10.1103/PhysRevLett.115.070601

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    You’ve supercharged your research process with ACS and Mendeley!

    STEP 1:
    Click to create an ACS ID

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    MENDELEY PAIRING EXPIRED
    Your Mendeley pairing has expired. Please reconnect