Fast dynamics perturbation analysis for prediction of protein functional sites

Dengming Ming; Cohn, Judith D.; Wall, Michael E.
January 2008
BMC Structural Biology;2008, Vol. 8, Special section p1
Academic Journal
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.


Related Articles

  • SwarmDock: a server for flexible protein–protein docking. Torchala, Mieczyslaw; Moal, Iain H.; Chaleil, Raphael A. G.; Fernandez-Recio, Juan; Bates, Paul A. // Bioinformatics;Mar2013, Vol. 29 Issue 6, p807 

    Summary: Protein–protein interactions are central to almost all biological functions, and the atomic details of such interactions can yield insights into the mechanisms that underlie these functions. We present a web server that wraps and extends the SwarmDock flexible...

  • Systems biology: Protein complexes under perturbation.  // Nature Methods;Sep2013, Vol. 10 Issue 9, p821 

    The article discusses the dynamics of protein-protein interactions (PPIs) in the context of their functional behavior under perturbation theory as specified in a study.

  • Assessing local structural perturbations in proteins. Lema, Martin A.; Echave, Julian // BMC Bioinformatics;2005, Vol. 6, p226 

    Background: Protein structure research often deals with the comparison of two or more structures of the same protein, for instance when handling alternative structure models for the same protein, point mutants, molecule movements, structure predictions, etc. Often the difference between...

  • A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome. Ghiassian, Susan Dina; Menche, Jörg; Barabási, Albert-László // PLoS Computational Biology;Apr2015, Vol. 11 Issue 4, p1 

    The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which...

  • Network-Free Inference of Knockout Effects in Yeast. Peleg, Tal; Yosef, Nir; Ruppin, Eytan; Sharan, Roded // PLoS Computational Biology;Jan2010, Vol. 6 Issue 1, p1 

    Perturbation experiments, in which a certain gene is knocked out and the expression levels of other genes are observed, constitute a fundamental step in uncovering the intricate wiring diagrams in the living cell and elucidating the causal roles of genes in signaling and regulation. Here we...

  • Automatic prediction of flexible regions improves the accuracy of protein-protein docking models. Luo, Xiaohu; Lü, Qiang; Wu, Hongjie; Yang, Lingyun; Huang, Xu; Qian, Peide; Fu, Gang // Journal of Molecular Modeling;May2012, Vol. 18 Issue 5, p2199 

    Computational models of protein-protein docking that incorporate backbone flexibility can predict perturbations of the backbone and side chains during docking and produce protein interaction models with atomic accuracy. Most previous models usually predefine flexible regions by visually...

  • meta-PPISP: a meta web server for protein-protein interaction site prediction. Sanbo Qin; Huan-Xiang Zhou // Bioinformatics;Dec2007, Vol. 23 Issue 24, p3386 

    Summary: A number of complementary methods have been developed for predicting protein-protein interaction sites. We sought to increase prediction robustness and accuracy by combining results from different predictors, and report here a meta web server, meta-PPISP, that is built on three...

  • Homodimeric Enzymes as Drug Targets. Cardinale, D.; Salo-Ahen, O. M. H.; Ferrari, S.; Ponterini, G.; Cruciani, G.; Carosati, E.; Tochowicz, A. M.; Mangani, S.; Wade, R. C.; Costi, M. P. // Current Medicinal Chemistry;Mar2010, Vol. 17 Issue 9, Special section p1 

    Many enzymes and proteins are regulated by their quaternary structure and/or by their association in homoand/ or hetero-oligomer complexes. Thus, these protein-protein interactions can be good targets for blocking or modulating protein function therapeutically. The large number of oligomeric...

  • Modular organization of protein interaction networks. Feng Luo; Yunfeng Yang; Chin-Fu Chen; Roger Chang; Jizhong Zhou; Richard H. Scheuermann // Bioinformatics;Jan2007, Vol. 23 Issue 2, p207 

    Motivation: Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems.Result: In this paper, we present a framework to identify modules within...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics