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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
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
Summary
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.
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.
