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A Bayes-optimal sequence-structure theory that unifies protein sequence-structure recognition and alignment

R H Lathrop1, R G Rogers, T F Smith

  • 1Department of Information and Computer Science, University of California, Irvine 92717, USA. rickl@uci.edu

Bulletin of Mathematical Biology
|December 29, 1998
PubMed
Summary

This study presents a Bayesian framework for protein sequence-structure alignment and recognition. It provides methods to identify the most probable protein structures and their alignments, enhancing bioinformatics discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein structure prediction and alignment are crucial for understanding protein function.
  • Current methods often struggle with global optimization and accurate recognition.

Purpose of the Study:

  • To develop a unified Bayesian approach for protein sequence-structure alignment and recognition.
  • To derive explicit formulae for selecting optimal structures, alignments, and segments.
  • To address computational challenges in protein structure analysis.

Main Methods:

  • Rigorous Bayesian analysis applied to protein sequence and structure data.
  • Derivation of explicit formulae for global probability calculations.
  • Development of fast exact recursions for specific cases (1D-3D).

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Main Results:

  • Formulations for selecting the most probable core structure, alignment, and joint structure-alignment.
  • Identification of most probable individual segments and secondary structures.
  • Demonstration of computational complexity (NP-hard) for general cases.

Conclusions:

  • The optimal joint structure and alignment maximizes the product of probabilities, not necessarily aligning optimal structures.
  • Sequence-independent gap penalties can affect scoring accuracy.
  • Core structure selection requires summing probabilities over all alignments, not just optimal ones.