Protein Recognition by Sequence-to-Structure Fitness: Bridging Efficiency and Capacity of Threading Models
- Richard A. Friesner
Published Online: 13 MAR 2002
DOI: 10.1002/0471224421.ch3
Copyright © 2002 John Wiley & Sons, Inc.
Book Title
Computational Methods for Protein Folding, Volume 120
Additional Information
How to Cite
Meller, J. and Elber, R. (2002) Protein Recognition by Sequence-to-Structure Fitness: Bridging Efficiency and Capacity of Threading Models, in Computational Methods for Protein Folding, Volume 120 (ed R. A. Friesner), John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471224421.ch3
Editor Information
Columbia University, New York, New York, USA
Publication History
- Published Online: 13 MAR 2002
- Published Print: 4 JAN 2002
Book Series:
Book Series Editors:
- I. Prigogine5,6,
- Stuart A. Rice7
Series Editor Information
- 5
Center for Studies in Statistical Mechanics and Complex Systems, The University of Texas, Austin, Texas, USA
- 6
International Solvay Institutes, Université Libre de Bruxelles, Brussels, Belgium
- 7
Department of Chemistry and The James Franck Institute, The University of Chicago, Chicago, Illinois, USA
ISBN Information
Print ISBN: 9780471209553
Online ISBN: 9780471224426
- Summary
- Chapter
Keywords:
- protein recognition;
- sequence-to-structure fitness;
- threading models;
- energy;
- functional form;
- energy parameters;
- pair and profile energies;
- gaps;
- deletions;
- statistical significance
Summary
In the present chapter the authors evaluate several different scoring functions for sequence-to-structure alignments, with parameters optimized by linear programming (LP). The first half of the chapter is devoted to the design of scoring functions. Two topics are discussed: the choice of the functional form (Section II) and the choice of the parameters (Section III). The capacity of the energies is explored and optimal parameters are determined (Section IV). The second part of the chapter deals with optimal alignments. The authors design gap energies (Section V) and introduce a double Z-score measure (from global and local alignments) to assess the results (Section VI). Presentation of extensive tests of the algorithm (Section VII) is followed by the conclusions and closing remarks.