title: Semiempirical prediction of protein folds.
abstract: We introduce a semiempirical approach to predict ab initio expeditious pathways and native backbone geometries of proteins that fold under in vitro renaturation conditions. The algorithm is engineered to incorporate a discrete codification of local steric hindrances that constrain the movements of the peptide backbone throughout the folding process. Thus, the torsional state of the chain is assumed to be conditioned by the fact that hopping from one basin of attraction to another in the Ramachandran map (local potential energy surface) of each residue is energetically more costly than the search for a specific (Phi, Psi) torsional state within a single basin. A combinatorial procedure is introduced to evaluate coarsely defined torsional states of the chain defined "modulo basins" and translate them into meaningful patterns of long range interactions. Thus, an algorithm for structure prediction is designed based on the fact that local contributions to the potential energy may be subsumed into time-evolving conformational constraints defining sets of restricted backbone geometries whereupon the patterns of nonbonded interactions are constructed. The predictive power of the algorithm is assessed by (a) computing ab initio folding pathways for mammalian ubiquitin that ultimately yield a stable structural pattern reproducing all of its native features, (b) determining the nucleating event that triggers the hydrophobic collapse of the chain, and (c) comparing coarse predictions of the stable folds of moderately large proteins (N approximately 100) with structural information extracted from the protein data bank.
authors.affiliation: Instituto de Matemática, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional del Sur, Avenida Alem 1253, Bahía Blanca 8000, Argentina. arifer@criba.edu.ar
authors.forename: A
authors.initials: A
authors.lastname: Fernández
authors.forename: A
authors.initials: A
authors.lastname: Colubri
authors.forename: G
authors.initials: G
authors.lastname: Appignanesi
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D003198:Computer Simulation; D030562:Databases, Protein; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009038:Motion; D011487:Protein Conformation; D017510:Protein Folding; D020673:Protein Renaturation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D025801:Ubiquitin / Q000737:chemistry
chemical_list: D000596:Amino Acids; D011506:Proteins; D025801:Ubiquitin