Protein folding simulations with genetic algorithms and a detailed molecular description.
We have explored the application of genetic algorithms (GA) to the determination of protein structure from sequence, using a full atom representation. A free energy function with point charge electrostatics and an area based solvation model is used. The method is found to be superior to previously investigated Monte Carlo algorithms. For selected fragments, up to 14 residues long, the lowest free energy structures produced by the GA are similar in conformation to the corresponding experimental structures in most cases. There are three main conclusions from these results. First, the genetic algorithm is an effective method for searching amongst the compact conformations of a polypeptide chain. Second, the free energy function is generally able to select native-like conformations. However, some deficiencies are identified, and further development is proposed. Third, the selection of native-like conformations for some protein fragments establishes that in these cases the conformation observed in the full protein structure is largely context independent. The implications for the nature of protein folding pathways are discussed.
Pedersen
J T
JT
Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, Rockville, MD 20850, USA.
Moult
J
J
D000465:Algorithms; D007611:Aprotinin / Q000737:chemistry; D001426:Bacterial Proteins; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010446:Peptide Fragments / Q000737:chemistry; D011487:Protein Conformation; D017510:Protein Folding; D015203:Reproducibility of Results; D012260:Ribonucleases / Q000737:chemistry; D013816:Thermodynamics
D001426:Bacterial Proteins; D010446:Peptide Fragments; D007611:Aprotinin; D012260:Ribonucleases; C019760:Bacillus amyloliquefaciens ribonuclease