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TESS: a geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases.

A C Wallace1, N Borkakoti, J M Thornton

  • 1Department of Biochemistry and Molecular Biology, University College, London, England.

Protein Science : a Publication of the Protein Society
|February 12, 1998
PubMed
Summary

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This study introduces TESS, an algorithm for creating 3D structural templates from protein data banks. These templates aid in identifying protein functions and designing novel proteins.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein science

Background:

  • Sequence templates (PROSITE, PRINTS) are vital for predicting protein function and structure.
  • The rapid increase in protein structure data necessitates 3D structural templates.

Purpose of the Study:

  • To develop an algorithm (TESS) for automatically generating 3D templates from protein structures.
  • To enable scanning new protein structures against 3D templates for functional site identification.

Main Methods:

  • Algorithm development for deriving 3D templates from the Brookhaven Protein Data Bank.
  • Application of TESS to generate templates for catalytic triads, ribonucleases, and lysozymes.
  • Scanning a dataset of non-identical proteins using derived 3D templates.

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Main Results:

  • Successful derivation of 3D templates for specific enzyme classes.
  • Identification of several functional sites in a large protein dataset using 3D templates.
  • Demonstration of TESS's utility in identifying unknown protein functions.

Conclusions:

  • 3D templates offer a powerful method for analyzing protein structures.
  • A 3D template database can assist in elucidating protein functions and guiding protein design.
  • TESS provides a scalable approach to generating and utilizing 3D structural information.