Stochastic model for protein flexibility analysis.
Protein flexibility is an intrinsic property and plays a fundamental role in protein functions. Computational analysis of protein flexibility is crucial to protein function prediction, macromolecular flexible docking, and rational drug design. Most current approaches for protein flexibility analysis are based on Hamiltonian mechanics. We introduce a stochastic model to study protein flexibility. The essential idea is to analyze the free induction decay of a perturbed protein structural probability, which satisfies the master equation. The transition probability matrix is constructed by using probability density estimators including monotonically decreasing radial basis functions. We show that the proposed stochastic model gives rise to some of the best predictions of Debye-Waller factors or B factors for three sets of protein data introduced in the literature.
Department of Mathematics, Michigan State University, Michigan 48824, USA.
Kelin
K
Xia
Department of Mathematics, Michigan State University, Michigan 48824, USA and Department of Electrical and Computer Engineering, Michigan State University, Michigan 48824, USA and Department of Biochemistry and Molecular Biology, Michigan State University, Michigan 48824, USA.
Guo-Wei
GW
Wei
D008958:Models, Molecular; D011336:Probability; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D013269:Stochastic Processes
D011506:Proteins