Geometric and electrostatic modeling using molecular rigidity functions

被引:6
|
作者
Mu, Lin [1 ]
Xia, Kelin [2 ]
Wei, Guowei [3 ,4 ,5 ]
机构
[1] Oak Ridge Natl Lab, Comp Sci & Math Div, Oak Ridge, TN 37831 USA
[2] Nanyang Technol Univ, Sch Phys & Math Sci, Div Math Sci, 21 Nanyang Link, Singapore 637371, Singapore
[3] Michigan State Univ, Dept Math, E Lansing, MI 48824 USA
[4] Michigan State Univ, Dept Elect & Comp Engn, E Lansing, MI 48824 USA
[5] Michigan State Univ, Dept Biochem & Mol Biol, E Lansing, MI 48824 USA
基金
美国国家科学基金会;
关键词
Flexibility rigidity index; Rigidity function; Molecular surface; Curvature; MATCHED INTERFACE; MESH GENERATION; SURFACE; PROTEINS; CONSTRUCTION; DYNAMICS; BINDING; CHARGE; SHAPE;
D O I
10.1016/j.cam.2016.08.019
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
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. The 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 proposed a rigidity based Poisson -Boltzmann equation for biomolecular electrostatic analysis. Our 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. (C) 2016 Elsevier B.V. All rights reserved.
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页码:18 / 37
页数:20
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