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GASS: identifying enzyme active sites with genetic algorithms.

Sandro C Izidoro1, Raquel C de Melo-Minardi2, Gisele L Pappa2

  • 1Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil.

Bioinformatics (Oxford, England)
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Summary

A new method, genetic active site search (GASS), predicts protein function by identifying conserved active sites. GASS accurately finds protein active site templates, aiding in understanding unknown protein functions.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • A significant portion of annotated proteins (25%) lack known functions.
  • Protein active sites, comprising catalytic and substrate binding sites, are conserved and crucial for function prediction.
  • Existing methods for active site identification have limitations.

Purpose of the Study:

  • To introduce a novel heuristic method, genetic active site search (GASS), for predicting protein function.
  • To develop a tool capable of searching for 3D active site templates in unknown proteins.
  • To overcome limitations of existing methods by allowing non-exact amino acid matches and accommodating varied active site sizes and chain locations.

Main Methods:

  • Genetic active site search (GASS) algorithm.
  • Utilizes 3D active site templates for searching.
  • Incorporates conservative mutations (non-exact amino acid matches).
  • Does not impose restrictions on active site size or residue location across chains.

Main Results:

  • GASS demonstrated high accuracy, correctly identifying over 90% of searched templates across four datasets.
  • Performance was validated against the Catalytic Site Atlas (CSA).
  • In the CASP 10 competition for substrate binding site prediction, GASS ranked fourth out of 18 methods.

Conclusions:

  • GASS is an effective and accurate method for identifying protein active site templates.
  • The method shows promise for improving protein function prediction, especially for proteins with unknown functions.
  • GASS offers flexibility and robustness compared to existing approaches.