Research Article

HingeMaster: Normal mode hinge prediction approach and integration of complementary predictors

  1. Samuel C. Flores1,2,*,
  2. Kevin S. Keating3,
  3. Jay Painter4,
  4. Faruck Morcos5,
  5. Khang Nguyen2,
  6. Ethan A. Merritt4,
  7. Leslie A. Kuhn6,7,8,
  8. Mark B. Gerstein2,3,9,*

Article first published online: 23 APR 2008

DOI: 10.1002/prot.22060

Issue

Proteins: Structure, Function, and Bioinformatics

Proteins: Structure, Function, and Bioinformatics

Volume 73, Issue 2, pages 299–319, 1 November 2008

Additional Information

How to Cite

Flores, S. C., Keating, K. S., Painter, J., Morcos, F., Nguyen, K., Merritt, E. A., Kuhn, L. A. and Gerstein, M. B. (2008), HingeMaster: Normal mode hinge prediction approach and integration of complementary predictors. Proteins: Structure, Function, and Bioinformatics, 73: 299–319. doi: 10.1002/prot.22060

Author Information

  1. 1

    Department of Physics, Yale University, Bass 432, New Haven, Connecticut 06520

  2. 2

    Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, New Haven, Connecticut 06520

  3. 3

    Program in Computational Biology and Bioinformatics, Yale University, Bass 432, New Haven, Connecticut 06520

  4. 4

    Department of Biochemistry, University of Washington, Mailstop 357742, Seattle, Washington 98195-7742

  5. 5

    Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana 46556

  6. 6

    Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, Michigan 48824

  7. 7

    Department of Computer Science & Engineering, Michigan State University, East Lansing, Michigan 48824

  8. 8

    Department of Physics & Astronomy, Michigan State University, East Lansing, Michigan 48824

  9. 9

    Department of Computer Science, Yale University, Bass 432, New Haven, Connecticut 06520

*266 Whitney Ave., New Haven, CT 06520

Publication History

  1. Issue published online: 3 SEP 2008
  2. Article first published online: 23 APR 2008
  3. Manuscript Accepted: 21 FEB 2008
  4. Manuscript Revised: 27 DEC 2007
  5. Manuscript Received: 1 JUN 2007

Funded by

  • National Institutes of Health (NIH)
  • Simbios, the NIH Roadmap for Medical Research. Grant Number: U54 GM072970
  • National Library of Medicine. Grant Number: T15 LM07056

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Keywords:

  • protein;
  • motion;
  • domain;
  • hinge;
  • bending;
  • atlas;
  • flexibility;
  • conformation;
  • change;
  • molmovdb

Abstract

Protein motion is often the link between structure and function and a substantial fraction of proteins move through a domain hinge bending mechanism. Predicting the location of the hinge from a single structure is thus a logical first step towards predicting motion. Here, we describe ways to predict the hinge location by grouping residues with correlated normal-mode motions. We benchmarked our normal-mode based predictor against a gold standard set of carefully annotated hinge locations taken from the Database of Macromolecular Motions. We then compared it with three existing structure-based hinge predictors (TLSMD, StoneHinge, and FlexOracle), plus HingeSeq, a sequence-based hinge predictor. Each of these methods predicts hinges using very different sources of information—normal modes, experimental thermal factors, bond constraint networks, energetics, and sequence, respectively. Thus it is logical that using these algorithms together would improve predictions. We integrated all the methods into a combined predictor using a weighted voting scheme. Finally, we encapsulated all our results in a web tool which can be used to run all the predictors on submitted proteins and visualize the results. © 2008 Wiley-Liss, Inc.

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