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The recognition of protein structure and function from sequence: adding value to genome data

A C May1, M S Johnson, S D Rufino

  • 1Department of Crystallography, Birkbeck College, University of London, U.K.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|June 29, 1994
PubMed
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Genome projects yield vast DNA data, requiring advanced methods to predict protein structure and function. This study presents an integrated approach to maximize sequence information value.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • The rapid increase in DNA sequence data from genome projects presents significant challenges for understanding protein structure and function.
  • Existing knowledge of protein structure-function relationships needs expansion to effectively analyze novel sequences.

Purpose of the Study:

  • To describe an integrated, knowledge-based approach for predicting protein structure and function.
  • To maximize the utility of available DNA sequence information in the context of large-scale genomic data.

Main Methods:

  • Deriving rules that define the relationships between protein sequence and structure.
  • Utilizing tertiary templates for recognizing common protein folds.
  • Employing new techniques for rule-based modeling of distantly related proteins.

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Main Results:

  • Development of an approach to address the challenges posed by the explosion of DNA sequence data.
  • Enhanced ability to recognize common protein folds using tertiary templates.
  • Progress in rule-based modeling for distantly related proteins.

Conclusions:

  • The presented integrated and knowledge-based approach effectively maximizes the value of sequence information.
  • This methodology aids in predicting protein structure and function from genomic data.
  • It contributes to meeting the challenges of analyzing large-scale biological sequence datasets.