Chapter 7. Sequence-Based Prediction of Residue-Level Properties in Proteins

  1. Yan-Qing Zhang3 and
  2. Jagath C. Rajapakse4
  1. Shandar Ahmad1,2,
  2. Yemlembam Hemjit Singh2,
  3. Marcos J. Araúzo-Bravo1 and
  4. Akinori Sarai1

Published Online: 21 APR 2008

DOI: 10.1002/9780470397428.ch7

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics

How to Cite

Ahmad, S., Singh, Y. H., Araúzo-Bravo, M. J. and Sarai, A. (2008) Sequence-Based Prediction of Residue-Level Properties in Proteins, in Machine Learning in Bioinformatics (eds Y.-Q. Zhang and J. C. Rajapakse), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470397428.ch7

Editor Information

  1. 3

    Georgia State University, Atlanta, Georgia

  2. 4

    School of Computer Engineering, and The Bioinformatics Research Center, Nanyang Technological University, Nanyang, Singapore

Author Information

  1. 1

    Kyushu Institute of Technology, Kyushu, Japan

  2. 2

    Jamia Millia Islamia, New Delhi, India

Publication History

  1. Published Online: 21 APR 2008
  2. Published Print: 12 NOV 2008

Book Series:

  1. Bioinformatics: Computational Techniques and Engineering

Book Series Editors:

  1. Professor Yi Pan and
  2. Professor Albert Y. Zomaya

ISBN Information

Print ISBN: 9780470116623

Online ISBN: 9780470397428

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Keywords:

  • sequence-based prediction of residue-level properties in proteins;
  • residue propensities and context dependence;
  • secondary structure prediction using sequence similarity

Summary

This chapter contains sections titled:

  • Introduction

  • Proteins as Amino Acid Sequences

  • Residue-Level Properties (RLP)

  • Residue Propensities and Context Dependence

  • Representation of Amino Acid Residues and Their Sequence Neighbors

  • Evolutionary Information and Profile-Based Predictions

  • Predictive Algorithms

  • Special Problems

  • Conclusion

  • References