Genetic algorithm with alternating selection pressure for protein side-chain packing and pK(a) prediction
Pascal Comte1, Sergei Vassiliev, Sheridan Houghten
1Department of Computer Science, Brock University, 500 Glenridge Ave., St. Catharines, Ontario, Canada.
Bio Systems
|June 16, 2011
Summary
This study introduces an efficient hybrid evolutionary algorithm for protein side-chain packing. The method generates diverse low-energy conformations, aiding in understanding protein function and predicting pKa values.
Area of Science:
- Computational Biology
- Biophysics
- Structural Bioinformatics
Background:
- Protein side-chain conformation prediction is crucial for understanding protein function.
- Side-chain packing, a key aspect of protein folding, is computationally complex (NP-hard).
Purpose of the Study:
- To investigate a hybrid genetic algorithm/simulated annealing technique for protein side-chain packing.
- To generate an ensemble of low-energy side-chain conformations using evolutionary sampling.
- To assess the method's utility in protein pKa prediction.
Main Methods:
- A hybrid approach combining genetic algorithms and simulated annealing was developed.
- The method optimizes amino-acid side-chains to achieve near-optimal low-energy protein conformations.
- An evolutionary sampling strategy with alternating selection pressures was employed.
Main Results:
- The hybrid technique efficiently generates distributions of solutions centered at desired energy levels.
- The method demonstrated effectiveness in producing diverse low-energy side-chain conformations.
- The approach was successfully applied to protein pKa prediction, validating its quality.
Conclusions:
- The developed evolutionary sampling methodology is highly efficient for protein side-chain packing.
- This technique provides a robust way to generate ensembles of low-energy conformations.
- The method shows promise for applications in protein function prediction and related biochemical problems.
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