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Protein structure prediction aided by geometrical and probabilistic constraints
Author(s) -
Porwal Gaurav,
Jain Swapnil,
Babu S. Dhilly,
Singh Deepak,
Nanavati Hemant,
Noronha Santosh
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20736
Subject(s) - probabilistic logic , dihedral angle , protein structure prediction , radius of gyration , monte carlo method , algorithm , energy minimization , computer science , casp , computation , mathematical optimization , mathematics , computational chemistry , protein structure , chemistry , artificial intelligence , organic chemistry , molecule , hydrogen bond , biochemistry , statistics , polymer
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. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007