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In silico protein recombination: enhancing template and sequence alignment selection for comparative protein
Bruno Contreras-Moreira1, Paul W Fitzjohn, Paul A Bates
1Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, Lincoln's Inn Fields Laboratories, 44 Lincoln's Inn Fields, London WC2A 3PX, UK.
Journal of Molecular Biology
|April 23, 2003
Summary
This study introduces a genetic algorithm for protein modeling, improving template selection and sequence alignment. This computational approach enhances the accuracy of building reliable protein atomic models.
Area of Science:
- Computational Biology
- Structural Biology
- Bioinformatics
Background:
- Comparative protein modelling predicts atomic structures from amino acid sequences using experimental template structures.
- Key challenges include accurate template selection, sequence alignment, and modelling of surface loops.
Purpose of the Study:
- To develop a novel computational method using genetic algorithms to improve template selection and sequence alignment in comparative protein modelling.
- To address critical problems in protein structure prediction for enhanced accuracy.
Main Methods:
- Application of a genetic algorithm with crossover and mutation operators for in silico protein recombination.
- Utilizing artificial selection to generate populations of optimized protein models.
Main Results:
- The genetic algorithm effectively exploits template and alignment variability, producing optimized models.
- The in silico recombination approach simplifies template selection and is robust to alignment errors.
Conclusions:
- Genetic algorithms offer a robust and effective tool for improving comparative protein modelling.
- This method is a promising candidate for the automated construction of reliable protein atomic models.