A physical approach to protein structure prediction.
We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4th Critical Assessment of Techniques for Protein Structure Prediction competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.
Crivelli
Silvia
S
Physical Biosciences and NERSC Divisions, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
Eskow
Elizabeth
E
Bader
Brett
B
Lamberti
Vincent
V
Byrd
Richard
R
Schnabel
Robert
R
Head-Gordon
Teresa
T
D000465:Algorithms; D000818:Animals; D034961:Antigens, Nuclear; D001324:Autoantigens / Q000737:chemistry; D006193:Haemophilus influenzae; D006801:Humans; D007527:Isoenzymes / Q000737:chemistry; D008958:Models, Molecular; D008962:Models, Theoretical; D009687:Nuclear Proteins / Q000737:chemistry; D054799:Phospholipase C beta; D011237:Predictive Value of Tests; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D014422:Turkeys; D010738:Type C Phospholipases / Q000737:chemistry
D034961:Antigens, Nuclear; D001324:Autoantigens; D007527:Isoenzymes; D009687:Nuclear Proteins; D011506:Proteins; C066642:SP100 protein, human; D010738:Type C Phospholipases; D054799:Phospholipase C beta