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Updated: Jul 18, 2026
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Analysis and prediction of functionally important sites in proteins.
Saikat Chakrabarti1, Christopher J Lanczycki
1National Center for Biotechnology Information, National Libary of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. chakraba@ncbi.nlm.nih.gov
Protein Science : a Publication of the Protein Society
|December 29, 2006
Summary
Computational methods can identify functionally important protein sites by analyzing sequence data. This approach uses evolutionary and compositional information to predict functional importance, aiding large-scale protein annotation.
Area of Science:
- Bioinformatics
- Computational Biology
- Protein Science
Background:
- The exponential growth of protein sequence and structure data necessitates efficient methods for functional importance determination.
- Computational approaches are crucial for extracting biochemical and evolutionary insights, especially from functionally critical protein regions.
Purpose of the Study:
- To conduct a comprehensive analysis of compositional and evolutionary constraints at functionally important protein sites.
- To develop and validate a computational method for predicting functionally important columns (FIC) in protein sequence alignments.
Main Methods:
- Surveyed compositional and evolutionary constraints across diverse protein families and functional categories.
- Created a library of functional templates based on identified constraints.
- Developed a prediction module using a 'template match score' combining residue composition and evolutionary conservation metrics.
Main Results:
- Demonstrated good sensitivity and specificity in predicting functional sites.
- Achieved high accuracy in assigning correct molecular function types to predicted sites.
- The method relies solely on homologous sequence information, requiring no structural data.
Conclusions:
- The developed computational method effectively predicts functionally important protein sites using sequence-based evolutionary and compositional data.
- This approach facilitates large-scale functional annotation of proteins without needing structural information.
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