NDLI: CReF: a central-residue-fragment-based method for predicting approximate 3-D polypeptides structures

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CReF: a central-residue-fragment-based method for predicting approximate 3-D polypeptides structures

Content Provider	ACM Digital Library
Author	de Souza, Osmar Norberto Dorn, Márcio
Abstract	In silico prediction of protein tertiary structure is one of the most important and unsolved problems in current structural bioinformatics. In this article, we describe CReF, a central-residue-fragment-based method to predict approximate 3-D polypeptides structures. With CReF we expect to obtain approximate 3-D structures which can then be used as starting conformations in refinement procedures employing state-of-the-art molecular mechanics methods such as molecular dynamics simulations. CReF does not make use of entire fragments, but only the phi, psi torsion angle information of the central residue in the template fragments obtained from PDB. After applying clustering techniques to these data, and guided by a consensus secondary structure prediction of the target sequence, we build approximate conformations for the target sequence. The method is very fast. We illustrate its efficacy in three case studies of polypeptides whose sizes vary from 34 to 70 amino acids. As indicated by the RMSD values, our initial results show that the predicted conformations adopt a fold similar to the experimental structures. Starting from these approximate conformations, the search space is expected to be greatly reduced and the refinement steps can consequently demand a much reduced computational effort to achieve a more accurate polypeptide 3-D structure.
Starting Page	1261
Ending Page	1267
Page Count	7
File Format	PDF
ISBN	9781595937537
DOI	10.1145/1363686.1363979
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-03-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cref Knowledge-based methods Ab initio 3-d protein structure prediction Scientific data bases De novo
Content Type	Text
Resource Type	Article

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