期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 8, 期 11, 页码 4374-4380出版社
AMER CHEMICAL SOC
DOI: 10.1021/ct300272j
关键词
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资金
- NIH [1-R01-GM093147-01, GM061867]
- NSF [IIS-1237022, DBI-1147082]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1147082] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1237022] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1239021] Funding Source: National Science Foundation
Virtually all docking methods include some local continuous minimization of an energy/scoring function in order to remove steric clashes and obtain more reliable energy values. In this paper, we describe an efficient rigid-body optimization algorithm that; compared to the most widely used algorithms, converges approximately an order of magnitude faster to conformations with equal or slightly lower energy. The space of rigid body transformations is a nonlinear manifold, namely, a space Which locally resembles a Euclidean space. We use a canonical, parametrization of the manifold, called the exponential parametrization, to map the Euclidean tangent space of the manifold onto the manifold itself. Thus, we locally transform the rigid body optimization to an optimization over a Euclidean space where basic optimization. algorithms are applicable. Compared to commonly used methods, this formulation substantially reduces the dimension of the search space. As a result, it requires far fewer costly function and gradient evaluations and leads to a more efficient algorithm. We have selected the LBFGS quasi-Newton method for local optimization since it uses only gradient information to obtain second order information about the energy function and avoids the far more costly direct Hessian evaluations. Two applications, one in protein-protein docking and the other in protein-small molecular interactions, as part of macromolecular docking protocols are presented. The code is available to the community under open source license and with minimal effort Can be incorporated into any molecular modeling package.
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