title: Protein structure prediction aided by geometrical and probabilistic constraints.
abstract: Database-assisted ab initio protein structure prediction methods have exhibited considerable promise in the recent past, with several implementations being successful in community-wide experiments (CASP). We have employed combinatorial optimization techniques toward solving the protein structure prediction problem. A Monte Carlo minimization algorithm has been employed on a constrained search space to identify minimum energy configurations. The search space is constrained by using radius of gyration cutoffs, the loop backbone dihedral probability distributions, and various secondary structure packing conformations. Simulations have been carried out on several sequences and 1000 conformations have been initially generated. Of these, 50 best candidates have then been selected as probable conformations. The search for the optimum has been simplified by incorporating various geometrical constraints on secondary structural elements using distance restraint potential functions. The advantages of the reported methodology are its simplicity, and modifiability to include other geometric and probabilistic restraints.
authors: Department of Chemical Engineering, IIT Bombay, Powai, Mumbai 400076, India.
authors: Gaurav
authors: G
authors: Porwal
authors: Swapnil
authors: S
authors: Jain
authors: S Dhilly
authors: SD
authors: Babu
authors: Deepak
authors: D
authors: Singh
authors: Hemant
authors: H
authors: Nanavati
authors: Santosh
authors: S
authors: Noronha
mesh_terms: D000465:Algorithms; D009010:Monte Carlo Method; D011336:Probability; D011487:Protein Conformation; D013816:Thermodynamics