Knowledge-based prediction of protein backbone conformation using a structural alphabet

Vetrivel, Iyanar and Mahajan, Swapnil and Tyagi, Manoj and Hoffmann, Lionel and Sanejouand, Yves-Henri and Srinivasan, Narayanaswamy and de Brevern, Alexandre G and Cadet, Frederic and Offmann, Bernard (2017) Knowledge-based prediction of protein backbone conformation using a structural alphabet. In: PLOS ONE, 12 (11).

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Abstract

Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R-2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

Item Type:	Journal Article
Publication:	PLOS ONE
Additional Information:	Copy right for this article belongs to the PUBLIC LIBRARY SCIENCE, 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
Department/Centre:	Division of Biological Sciences > Molecular Biophysics Unit
Date Deposited:	23 Dec 2017 06:18
Last Modified:	23 Dec 2017 06:18
URI:	http://eprints.iisc.ac.in/id/eprint/58518

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