Literature DB >> 31809040

Explicit Characterization of the Free-Energy Landscape of a Protein in the Space of All Its Cα Carbons.

Giulia Sormani1, Alex Rodriguez2, Alessandro Laio1.   

Abstract

By using an approach that allows computing the free energy in high-dimensional spaces together with a clustering technique capable of identifying kinetic attractors stabilized by conformational disorder, we analyze a molecular dynamics trajectory of the Villin headpiece from Lindorff-Larsen, K.; et al. How fast-folding proteins fold. Science 2011, 334, 517-520. We compute its free-energy landscape in the space of all its Cα carbons. This landscape has the shape of a 12-dimensional funnel with the free energy decreasing monotonically as a function of the native contacts. There are no significant folding barriers. The funnel can be partitioned in five regions, three mainly folded and two unfolded, which behave as Markov states. The slowest relaxation time among these states corresponds to the folding transition. The second slowest time is only twice smaller and corresponds to a transition within the unfolded state. This indicates that the unfolded part of the funnel has a nontrivial shape, which induces a sizable kinetic barrier between disordered states.

Entities:  

Year:  2019        PMID: 31809040     DOI: 10.1021/acs.jctc.9b00800

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  4 in total

1.  Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders.

Authors:  Mahdi Ghorbani; Samarjeet Prasad; Jeffery B Klauda; Bernard R Brooks
Journal:  J Chem Phys       Date:  2021-11-21       Impact factor: 3.488

2.  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

3.  Candidate Binding Sites for Allosteric Inhibition of the SARS-CoV-2 Main Protease from the Analysis of Large-Scale Molecular Dynamics Simulations.

Authors:  Matteo Carli; Giulia Sormani; Alex Rodriguez; Alessandro Laio
Journal:  J Phys Chem Lett       Date:  2020-12-11       Impact factor: 6.475

Review 4.  Collective variable-based enhanced sampling and machine learning.

Authors:  Ming Chen
Journal:  Eur Phys J B       Date:  2021-10-20       Impact factor: 1.500

  4 in total

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