title: A gradient-directed Monte Carlo approach for protein design.
abstract: We develop a new global optimization strategy, gradient-directed Monte Carlo (GDMC) sampling, to optimize protein sequence for a target structure using RosettaDesign. GDMC significantly improves the sampling of sequence space, compared to the classical Monte Carlo search protocol, for a fixed backbone conformation as well as for the simultaneous optimization of sequence and structure. As such, GDMC sampling enhances the efficiency of protein design.
authors.affiliation: Department of Chemistry, French Family Science Center, Duke University, Durham, North Carolina 27708-0346, USA.
authors.forename: Xiangqian
authors.initials: X
authors.lastname: Hu
authors.forename: Hao
authors.initials: H
authors.lastname: Hu
authors.forename: David N
authors.initials: DN
authors.lastname: Beratan
authors.forename: Weitao
authors.initials: W
authors.lastname: Yang
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D015195:Drug Design; D009010:Monte Carlo Method; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D011506:Proteins