Calculation of configurational entropy differences from conformational ensembles using Gaussian mixtures

Gergely Gyimesi, P. Závodszky, András Szilágyi

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

We present a novel, conceptually simple approach to calculate the configurational entropy difference between two conformational ensembles of a molecular system. The method estimates the full-dimensional probability density function of the system by a Gaussian mixture, using an efficient greedy learning algorithm with a cross-validation-based stopping criterion. An evaluation of the method on conformational ensembles corresponding to substates of five small peptide systems shows that excellent agreement is found with the exact entropy differences obtained from a full enumeration of conformations. Compared with the quasiharmonic method and two other, more recently developed methods, the Gaussian mixture method yields more accurate results at smaller sample sizes. We illustrate the power of the method by calculating the backbone torsion angle entropy difference between disulfide-bonded and nondisulfide-bonded states of tachyplesin, a 17-residue antimicrobial peptide, and between two substates in the native ensemble of the 58-residue bovine pancreatic trypsin inhibitor.

Original languageEnglish
Pages (from-to)29-41
Number of pages13
JournalJournal of Chemical Theory and Computation
Volume13
Issue number1
DOIs
Publication statusPublished - Jan 1 2017

Fingerprint

Entropy
entropy
Peptides
peptides
trypsin
enumeration
Aprotinin
disulfides
probability density functions
stopping
Disulfides
Torsional stress
inhibitors
Probability density function
Learning algorithms
learning
torsion
Conformations
evaluation
estimates

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

Cite this

Calculation of configurational entropy differences from conformational ensembles using Gaussian mixtures. / Gyimesi, Gergely; Závodszky, P.; Szilágyi, András.

In: Journal of Chemical Theory and Computation, Vol. 13, No. 1, 01.01.2017, p. 29-41.

Research output: Contribution to journalArticle

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