Literature DB >> 29195289

A variational conformational dynamics approach to the selection of collective variables in metadynamics.

James McCarty1, Michele Parrinello1.   

Abstract

In this paper, we combine two powerful computational techniques, well-tempered metadynamics and time-lagged independent component analysis. The aim is to develop a new tool for studying rare events and exploring complex free energy landscapes. Metadynamics is a well-established and widely used enhanced sampling method whose efficiency depends on an appropriate choice of collective variables. Often the initial choice is not optimal leading to slow convergence. However by analyzing the dynamics generated in one such run with a time-lagged independent component analysis and the techniques recently developed in the area of conformational dynamics, we obtain much more efficient collective variables that are also better capable of illuminating the physics of the system. We demonstrate the power of this approach in two paradigmatic examples.

Year:  2017        PMID: 29195289     DOI: 10.1063/1.4998598

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  12 in total

Review 1.  Markov State Models to Elucidate Ligand Binding Mechanism.

Authors:  Yunhui Ge; Vincent A Voelz
Journal:  Methods Mol Biol       Date:  2021

2.  Ligand binding free-energy calculations with funnel metadynamics.

Authors:  Stefano Raniolo; Vittorio Limongelli
Journal:  Nat Protoc       Date:  2020-08-19       Impact factor: 13.491

3.  Deep learning the slow modes for rare events sampling.

Authors:  Luigi Bonati; GiovanniMaria Piccini; Michele Parrinello
Journal:  Proc Natl Acad Sci U S A       Date:  2021-11-02       Impact factor: 11.205

4.  Proton coupling and the multiscale kinetic mechanism of a peptide transporter.

Authors:  Chenghan Li; Zhi Yue; Simon Newstead; Gregory A Voth
Journal:  Biophys J       Date:  2022-05-25       Impact factor: 3.699

5.  Exploration vs Convergence Speed in Adaptive-Bias Enhanced Sampling.

Authors:  Michele Invernizzi; Michele Parrinello
Journal:  J Chem Theory Comput       Date:  2022-05-26       Impact factor: 6.578

6.  Unsupervised Learning Methods for Molecular Simulation Data.

Authors:  Aldo Glielmo; Brooke E Husic; Alex Rodriguez; Cecilia Clementi; Frank Noé; Alessandro Laio
Journal:  Chem Rev       Date:  2021-05-04       Impact factor: 60.622

Review 7.  Pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical experiments.

Authors:  Soumendranath Bhakat
Journal:  RSC Adv       Date:  2021-03-17       Impact factor: 3.361

8.  On identifying collective displacements in apo-proteins that reveal eventual binding pathways.

Authors:  Dheeraj Dube; Navjeet Ahalawat; Himanshu Khandelia; Jagannath Mondal; Surajit Sengupta
Journal:  PLoS Comput Biol       Date:  2019-01-15       Impact factor: 4.475

9.  A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.

Authors:  João Marcelo Lamim Ribeiro; Davide Provasi; Marta Filizola
Journal:  J Chem Phys       Date:  2020-09-28       Impact factor: 3.488

Review 10.  Computational methods for exploring protein conformations.

Authors:  Jane R Allison
Journal:  Biochem Soc Trans       Date:  2020-08-28       Impact factor: 5.407

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