Fold Recognition: Prediction Reports

Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling†

  1. Donald Petrey,
  2. Zhexin Xiang,
  3. Christopher L. Tang,
  4. Lei Xie,
  5. Marina Gimpelev,
  6. Therese Mitros,
  7. Cinque S. Soto,
  8. Sharon Goldsmith-Fischman,
  9. Andrew Kernytsky,
  10. Avner Schlessinger,
  11. Ingrid Y.Y. Koh,
  12. Emil Alexov,
  13. Barry Honig*

Article first published online: 15 OCT 2003

DOI: 10.1002/prot.10550

Issue

Proteins: Structure, Function, and Bioinformatics

Proteins: Structure, Function, and Bioinformatics

Supplement: Fifth Meeting on the Critical Assessment of Techniques for Protein Structure Prediction

Volume 53, Issue Supplement 6, pages 430–435, 2003

Additional Information

How to Cite

Petrey, D., Xiang, Z., Tang, C. L., Xie, L., Gimpelev, M., Mitros, T., Soto, C. S., Goldsmith-Fischman, S., Kernytsky, A., Schlessinger, A., Koh, I. Y.Y., Alexov, E. and Honig, B. (2003), Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling. Proteins, 53: 430–435. doi: 10.1002/prot.10550

Author Information

  1. Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University New York, New York

*Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032

  1. †

    Donald Petrey and Zhexin Xiang contributed equally to the work described in this manuscript.

Publication History

  1. Issue published online: 15 OCT 2003
  2. Article first published online: 15 OCT 2003
  3. Manuscript Accepted: 17 MAR 2003
  4. Manuscript Received: 14 FEB 2003

Funded by

  • National Science Foundation. Grant Number: DBI-9904841
  • National Institutes of Health. Grant Number: GM-30518

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Keywords:

  • homology modeling;
  • fold recognition;
  • protein structure alignment;
  • profile-profile alignment

Abstract

We participated in the fold recognition and homology sections of CASP5 using primarily in-house software. The central feature of our structure prediction strategy involved the ability to generate good sequence-to-structure alignments and to quickly transform them into models that could be evaluated both with energy-based methods and manually. The in-house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)—a profile-to-profile alignment method that is derived from sequence-enhanced multiple structure alignments in core regions, and sequence motifs in non-structurally conserved regions. b) NEST–a fast model building program that applies an “artificial evolution” algorithm to construct a model from a given template and alignment. c) GRASP2–a new structure and alignment visualization program incorporating multiple structure superposition and domain database scanning modules. These methods were combined with model evaluation based on all atom and simplified physical-chemical energy functions. All of these methods were under development during CASP5 and consequently a great deal of manual analysis was carried out at each stage of the prediction process. This interactive model building procedure has several advantages and suggests important ways in which our and other methods can be improved, examples of which are provided. Proteins 2003;53:430–435. © 2003 Wiley-Liss, Inc.

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