Literature DB >> 17195154

Nearest-neighbor nonparametric method for estimating the configurational entropy of complex molecules.

Vladimir Hnizdo1, Eva Darian, Adam Fedorowicz, Eugene Demchuk, Shengqiao Li, Harshinder Singh.   

Abstract

A method for estimating the configurational (i.e., non-kinetic) part of the entropy of internal motion in complex molecules is introduced that does not assume any particular parametric form for the underlying probability density function. It is based on the nearest-neighbor (NN) distances of the points of a sample of internal molecular coordinates obtained by a computer simulation of a given molecule. As the method does not make any assumptions about the underlying potential energy function, it accounts fully for any anharmonicity of internal molecular motion. It provides an asymptotically unbiased and consistent estimate of the configurational part of the entropy of the internal degrees of freedom of the molecule. The NN method is illustrated by estimating the configurational entropy of internal rotation of capsaicin and two stereoisomers of tartaric acid, and by providing a much closer upper bound on the configurational entropy of internal rotation of a pentapeptide molecule than that obtained by the standard quasi-harmonic method. As a measure of dependence between any two internal molecular coordinates, a general coefficient of association based on the information-theoretic quantity of mutual information is proposed. Using NN estimates of this measure, statistical clustering procedures can be employed to group the coordinates into clusters of manageable dimensions and characterized by minimal dependence between coordinates belonging to different clusters.

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Year:  2007        PMID: 17195154     DOI: 10.1002/jcc.20589

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  36 in total

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2.  Extraction of configurational entropy from molecular simulations via an expansion approximation.

Authors:  Benjamin J Killian; Joslyn Yundenfreund Kravitz; Michael K Gilson
Journal:  J Chem Phys       Date:  2007-07-14       Impact factor: 3.488

3.  A black-box re-weighting analysis can correct flawed simulation data.

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Review 4.  Molecular modeling of nucleic acid structure: energy and sampling.

Authors:  T E Cheatham; B R Brooks; P A Kollman
Journal:  Curr Protoc Nucleic Acid Chem       Date:  2001-05

5.  Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril.

Authors:  Crystal N Nguyen; Tom Kurtzman Young; Michael K Gilson
Journal:  J Chem Phys       Date:  2012-07-28       Impact factor: 3.488

6.  Rapid prediction of crucial hotspot interactions for icosahedral viral capsid self-assembly by energy landscape atlasing validated by mutagenesis.

Authors:  Ruijin Wu; Rahul Prabhu; Aysegul Ozkan; Meera Sitharam
Journal:  PLoS Comput Biol       Date:  2020-10-20       Impact factor: 4.475

Review 7.  Methods for calculating the entropy and free energy and their application to problems involving protein flexibility and ligand binding.

Authors:  Hagai Meirovitch; Srinath Cheluvaraja; Ronald P White
Journal:  Curr Protein Pept Sci       Date:  2009-06       Impact factor: 3.272

8.  Estimation of Solvation Entropy and Enthalpy via Analysis of Water Oxygen-Hydrogen Correlations.

Authors:  Camilo Velez-Vega; Daniel J J McKay; Tom Kurtzman; Vibhas Aravamuthan; Robert A Pearlstein; José S Duca
Journal:  J Chem Theory Comput       Date:  2015-10-21       Impact factor: 6.006

9.  Estimating absolute configurational entropies of macromolecules: the minimally coupled subspace approach.

Authors:  Ulf Hensen; Oliver F Lange; Helmut Grubmüller
Journal:  PLoS One       Date:  2010-02-23       Impact factor: 3.240

10.  Efficient calculation of configurational entropy from molecular simulations by combining the mutual-information expansion and nearest-neighbor methods.

Authors:  Vladimir Hnizdo; Jun Tan; Benjamin J Killian; Michael K Gilson
Journal:  J Comput Chem       Date:  2008-07-30       Impact factor: 3.376

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