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

Authors:
Márcio Dorn;Osmar Norberto de Souza
Affiliations:
LABIO, Faculdade de Informática, PPGCC, PUCRS, Avenida Ipiranga, Porto Alegre, RS, Brazil;LABIO, Faculdade de Informática, PPGCC, PUCRS, Avenida Ipiranga, Porto Alegre, RS, Brazil
Venue:
Proceedings of the 2008 ACM symposium on Applied computing
Year:
2008

Citing 3
Cited 5

Protein structure prediction by a data-level parallel algorithm

Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Biopython: Python tools for computational biology

ACM SIGBIO Newsletter
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

A3N: An artificial neural network n-gram-based method to approximate 3-D polypeptides structure prediction

Expert Systems with Applications: An International Journal
A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides

Expert Systems with Applications: An International Journal
A molecular dynamics and knowledge-based computational strategy to predict native-like structures of polypeptides

Expert Systems with Applications: An International Journal
An expert protein loop refinement protocol by molecular dynamics simulations with restraints

Expert Systems with Applications: An International Journal
An interval-based algorithm to represent conformational states of experimentally determined polypeptide templates and fast prediction of approximated 3D protein structures

International Journal of Bioinformatics Research and Applications

Quantified Score

Hi-index	0.01

Visualization

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.