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A structure and evolution-guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of
I Mihalek1, I Res, O Lichtarge
1Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA. imihalek@bcm.tmc.edu
Bioinformatics (Oxford, England)
|November 24, 2005
Summary
We developed a new method to select homologous sequences for protein functional surface detection. This approach optimizes sequence selection, improving the accuracy of identifying critical protein regions like active sites.
Area of Science:
- Computational Biology
- Structural Biology
- Bioinformatics
Background:
- Multiple sequence alignment (MSA) methods are used to identify functional protein surfaces (e.g., active sites, interfaces).
- The impact of sequence selection on MSA analysis outcomes is often overlooked.
- Existing methods lack optimization strategies for reliable functional surface detection.
Purpose of the Study:
- To propose a novel sequence selection strategy for improved functional surface detection in proteins.
- To optimize the selection of homologous sequences for enhanced accuracy in identifying functionally important regions.
Main Methods:
- A heuristic Metropolis Monte Carlo strategy was developed for sequence selection from homologues.
- The method utilizes the clustering of evolutionarily constrained residues to guide optimization.
- The approach is demonstrated on a dataset of 50 homodimerizing enzymes with known structures, substrates, and cofactors.
Main Results:
- The proposed method improves the prediction of functional surfaces compared to sequence similarity criteria.
- It achieves prediction quality comparable to more complex, non-structure-based sequence selection methods.
- Demonstrated effectiveness using a diverse set of enzyme examples.
Conclusions:
- Optimized sequence selection significantly enhances the reliability of detecting functional protein surfaces.
- This heuristic Monte Carlo approach offers a computationally efficient and effective strategy for bioinformatics analysis.
- The method provides a valuable tool for understanding protein function and evolution.