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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/9501

Title: Protein structure prediction aided by geometrical and probabilistic constraints
Authors: PORWAL, G
JAIN, S
BABU, SD
SINGH, D
NANAVATI, H
NORONHA, S
Keywords: monte-carlo-minimization
secondary structure
alpha-helices
beta-sheets
conformational searches
energy minimization
packing
peptides
sequence
touchstone
Issue Date: 2007
Publisher: JOHN WILEY & SONS INC
Citation: JOURNAL OF COMPUTATIONAL CHEMISTRY, 28(12), 1943-1952
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. (c) 2007 .
URI: http://dx.doi.org/10.1002/jcc.20736
http://dspace.library.iitb.ac.in/xmlui/handle/10054/9501
http://hdl.handle.net/10054/9501
ISSN: 0192-8651
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