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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
Ideal amino acid exchange forms for approximating substitution matrices.
Piotr Pokarowski1, Andrzej Kloczkowski, Szymon Nowakowski
1Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, Warsaw University, 02-097 Warsaw, Poland. pokar@mimuw.edu.pl
Proteins
|July 12, 2007
Summary
This study compares protein substitution matrices (SMs) and contact potentials (CPs), revealing distinct groups and a new approximation for SMs. This approximation improves understanding of amino acid similarity and enhances protein sequence alignment methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Structural Biology
Background:
- Substitution matrices (SMs) and contact potentials (CPs) are crucial for analyzing protein sequences and structures.
- Classical SMs, often derived from globular protein alignments, show high inter-correlation.
- Existing methods for protein sequence analysis can be refined with improved understanding of amino acid relationships.
Purpose of the Study:
- To compare and categorize published substitution matrices and protein contact potentials.
- To identify new mathematical approximations for substitution matrices.
- To assess the impact of these approximations on protein sequence alignment.
Main Methods:
- Analysis of 29 substitution matrices and 5 protein contact potentials.
- Correlation analysis to group and compare matrices and potentials.
- Development and validation of a novel approximation formula for substitution matrices.
Main Results:
- Three distinct groups of substitution matrices were identified based on their correlation patterns.
- A new approximation for SMs was developed, correlating highly with amino acid hydrophobicity, molecular volume, and coil preferences.
- The new approximation showed high correlation (0.9) with existing SMs and led to less than 5% difference in sequence alignment results.
Conclusions:
- Substitution matrices exhibit distinct clustering based on their derivation methods.
- A novel mathematical approximation provides insights into amino acid similarity and physicochemical properties.
- The developed approximation offers a valuable tool for enhancing protein sequence alignment and related bioinformatics applications.
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