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Open Access Methodology article

Fast dynamics perturbation analysis for prediction of protein functional sites

Dengming Ming12, Judith D Cohn13 and Michael E Wall134*

Author Affiliations

1 Computer, Computational, and Statistical Scienes Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA

2 School of Life Sciences, Nanjing University, Nanjing, Jiangsu Province, China

3 Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA

4 Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, USA

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BMC Structural Biology 2008, 8:5  doi:10.1186/1472-6807-8-5

Published: 30 January 2008

Abstract

Background

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy Dx. Such regions are associated with functional sites.

Results

The Fast DPA algorithm, which accelerates DPA calculations, is motivated by an empirical observation that Dx in a normal-modes model is highly correlated with an entropic term that only depends on the eigenvalues of the normal modes. The eigenvalues are accurately estimated using first-order perturbation theory, resulting in a N-fold reduction in the overall computational requirements of the algorithm, where N is the number of residues in the protein. The performance of the original and Fast DPA algorithms was compared using protein structures from a standard small-molecule docking test set. For nominal implementations of each algorithm, top-ranked Fast DPA predictions overlapped the true binding site 94% of the time, compared to 87% of the time for original DPA. In addition, per-protein recall statistics (fraction of binding-site residues that are among predicted residues) were slightly better for Fast DPA. On the other hand, per-protein precision statistics (fraction of predicted residues that are among binding-site residues) were slightly better using original DPA. Overall, the performance of Fast DPA in predicting ligand-binding-site residues was comparable to that of the original DPA algorithm.

Conclusion

Compared to the original DPA algorithm, the decreased run time with comparable performance makes Fast DPA well-suited for implementation on a web server and for high-throughput analysis.