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:
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1771195
- Alternate Identifier(s):
- OSTI ID: 1329763; OSTI ID: 1358866
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Published Article
- Journal Name:
- Journal of Computational and Applied Mathematics
- Additional Journal Information:
- Journal Name: Journal of Computational and Applied Mathematics Journal Volume: 313 Journal Issue: C; Journal ID: ISSN 0377-0427
- Publisher:
- Elsevier
- Country of Publication:
- Belgium
- 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. Belgium: 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. Belgium. https://doi.org/10.1016/j.cam.2016.08.019
Mu, Lin, Xia, Kelin, and Wei, Guowei. Wed .
"Geometric and electrostatic modeling using molecular rigidity functions". Belgium. https://doi.org/10.1016/j.cam.2016.08.019.
@article{osti_1771195,
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 = {Belgium},
year = {Wed Mar 01 00:00:00 EST 2017},
month = {Wed Mar 01 00:00:00 EST 2017}
}
https://doi.org/10.1016/j.cam.2016.08.019
Web of Science
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Works referencing / citing this record:
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