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Comparison of Different Reweighting Approaches for the Calculation of Conformational Variability of Macromolecules from Molecular Simulations
Author(s) -
Medeiros Selegato Denise,
Bracco Cesare,
Giannelli Carlotta,
Parigi Giacomo,
Luchinat Claudio,
Sgheri Luca,
Ravera Enrico
Publication year - 2021
Publication title -
chemphyschem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.016
H-Index - 140
eISSN - 1439-7641
pISSN - 1439-4235
DOI - 10.1002/cphc.202000714
Subject(s) - principle of maximum entropy , statistical physics , observable , molecular dynamics , conformational ensembles , macromolecule , obstacle , entropy (arrow of time) , function (biology) , conformational entropy , experimental data , molecular conformation , computer science , biological system , chemistry , computational chemistry , physics , thermodynamics , molecule , mathematics , statistics , artificial intelligence , biology , quantum mechanics , biochemistry , evolutionary biology , political science , law
Conformational variability and heterogeneity are crucial determinants of the function of biological macromolecules. The possibility of accessing this information experimentally suffers from severe under‐determination of the problem, since there are a few experimental observables to be accounted for by a (potentially) infinite number of available conformational states. Several computational methods have been proposed over the years in order to circumvent this theoretically insurmountable obstacle. A large share of these strategies is based on reweighting an initial conformational ensemble which arises from, for example, molecular simulations of different qualities and levels of theory. In this work, we compare the outcome of three reweighting approaches based on radically different views of the conformational heterogeneity problem, namely Maximum Entropy, Maximum Parsimony and Maximum Occurrence, and we do so using the same experimental data. In this comparison we find both expected as well as unexpected similarities.