title: Disulfide recognition in an optimized threading potential.
abstract: 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.
authors: College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA.
authors: A A
authors: AA
authors: Dombkowski
authors: G M
authors: GM
authors: Crippen
mesh_terms: 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
chemical_list: D004220:Disulfides; D012997:Solvents; D003553:Cystine; D003545:Cysteine