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Title: Geometric and electrostatic modeling using molecular rigidity functions

Abstract

Geometric and electrostatic modeling is an essential component in computational biophysics and molecular biology. Commonly used geometric representations admit geometric singularities such as cusps, tips and self-intersecting facets that lead to computational instabilities in the molecular modeling. Our present work explores the use of flexibility and rigidity index (FRI), which has a proved superiority in protein B-factor prediction, for biomolecular geometric representation and associated electrostatic analysis. FRI rigidity surfaces are free of geometric singularities. We propose a rigidity based Poisson–Boltzmann equation for biomolecular electrostatic analysis. These approaches to surface and electrostatic modeling are validated by a set of 21 proteins. Our results are compared with those of established methods. Finally, being smooth and analytically differentiable, FRI rigidity functions offer excellent curvature analysis, which characterizes concave and convex regions on protein surfaces. Polarized curvatures constructed by using the product of minimum curvature and electrostatic potential is shown to predict potential protein–ligand binding sites.

Authors:
ORCiD logo [1];  [2];  [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematical Division
  2. Nanyang Technological Univ. (Singapore). School of Physical and Mathematical Sciences
  3. Michigan State Univ., East Lansing, MI (United States). Dept. of Mathematics and Electrical and Computer Engineering and Biochemistry and Molecular Biology
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1329763
Alternate Identifier(s):
OSTI ID: 1358866
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Computational and Applied Mathematics
Additional Journal Information:
Journal Volume: 313; Journal Issue: C; Journal ID: ISSN 0377-0427
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES; Flexibility rigidity index; Rigidity function; Molecular surface; Curvature

Citation Formats

Mu, Lin, Xia, Kelin, and Wei, Guowei. Geometric and electrostatic modeling using molecular rigidity functions. United States: N. p., 2017. Web. doi:10.1016/j.cam.2016.08.019.
Mu, Lin, Xia, Kelin, & Wei, Guowei. Geometric and electrostatic modeling using molecular rigidity functions. United States. doi:10.1016/j.cam.2016.08.019.
Mu, Lin, Xia, Kelin, and Wei, Guowei. Wed . "Geometric and electrostatic modeling using molecular rigidity functions". United States. doi:10.1016/j.cam.2016.08.019. https://www.osti.gov/servlets/purl/1329763.
@article{osti_1329763,
title = {Geometric and electrostatic modeling using molecular rigidity functions},
author = {Mu, Lin and Xia, Kelin and Wei, Guowei},
abstractNote = {Geometric and electrostatic modeling is an essential component in computational biophysics and molecular biology. Commonly used geometric representations admit geometric singularities such as cusps, tips and self-intersecting facets that lead to computational instabilities in the molecular modeling. Our present work explores the use of flexibility and rigidity index (FRI), which has a proved superiority in protein B-factor prediction, for biomolecular geometric representation and associated electrostatic analysis. FRI rigidity surfaces are free of geometric singularities. We propose a rigidity based Poisson–Boltzmann equation for biomolecular electrostatic analysis. These approaches to surface and electrostatic modeling are validated by a set of 21 proteins. Our results are compared with those of established methods. Finally, being smooth and analytically differentiable, FRI rigidity functions offer excellent curvature analysis, which characterizes concave and convex regions on protein surfaces. Polarized curvatures constructed by using the product of minimum curvature and electrostatic potential is shown to predict potential protein–ligand binding sites.},
doi = {10.1016/j.cam.2016.08.019},
journal = {Journal of Computational and Applied Mathematics},
number = C,
volume = 313,
place = {United States},
year = {Wed Mar 01 00:00:00 EST 2017},
month = {Wed Mar 01 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
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