Self-consistently optimized energy functions for protein structure prediction by molecular dynamics.
The protein energy landscape theory is used to obtain optimal energy functions for protein structure prediction via simulated annealing. The analysis here takes advantage of a more complete statistical characterization of the protein energy landscape and thereby improves on previous approximations. This schema partially takes into account correlations in the energy landscape. It also incorporates the relationships between folding dynamics and characteristic energy scales that control the collapse of the proteins and modulate rigidity of short-range interactions. Simulated annealing for the optimal energy functions, which are associative memory hamiltonians using a database of folding patterns, generally leads to quantitatively correct structures. In some cases the algorithm achieves "creativity," i.e., structures result that are better than any homolog in the database.
Koretke
K K
KK
School of Chemical Sciences, University of Illinois, Urbana, IL 61801, USA.
Luthey-Schulten
Z
Z
Wolynes
P G
PG
D001185:Artificial Intelligence; D003198:Computer Simulation; D003574:Cytochrome c Group / Q000737:chemistry; D005544:Forecasting; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D008962:Models, Theoretical; D011487:Protein Conformation; D017510:Protein Folding; D045522:Recombinases; D013816:Thermodynamics; D019895:Transposases / Q000737:chemistry; D014598:Uteroglobin / Q000737:chemistry
D003574:Cytochrome c Group; D045522:Recombinases; D014598:Uteroglobin; D019895:Transposases