Disulfide recognition in an optimized threading potential.
An energy potential is constructed and trained to succeed in fold recognition for the general population of proteins as well as an important class which has previously been problematic: small, disulfide-bearing proteins. The potential is modeled on solvation, with the energy a function of side chain burial and the number of disulfide bonds. An accurate disulfide recognition algorithm identifies cysteine pairs which have the appropriate orientation to form a disulfide bridge. The potential has 22 energy parameters which are optimized so the Protein Data Bank (PDB) structure for each sequence in a training set is the lowest in energy out of thousands of alternative structures. One parameter per amino acid type reflects burial preference and a single parameter is used in an overpacking term. Additionally, one optimized parameter provides a favorable contribution for each disulfide identified in a given protein structure. With little training, the potential is >80% accurate in ungapped threading tests using a variety of proteins. The same level of accuracy is observed in a threading test of small proteins which have disulfide bonds. Importantly, the energy potential is also successful with proteins having uncrosslinked cysteines.
Dombkowski
A A
AA
College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA.
Crippen
G M
GM
D000465:Algorithms; D001185:Artificial Intelligence; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D003545:Cysteine / Q000737:chemistry; D003553:Cystine / Q000737:chemistry* / Q000494:pharmacology; D004220:Disulfides / Q000737:chemistry*; D008958:Models, Molecular; D017510:Protein Folding; D017434:Protein Structure, Tertiary / Q000187:drug effects; D012997:Solvents / Q000494:pharmacology; D013816:Thermodynamics
D004220:Disulfides; D012997:Solvents; D003553:Cystine; D003545:Cysteine