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 highlights 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 (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

DATA SET USED IN THE PAPERDATA SET FOR Knowledge based prediction of protein backbone conformation using a structural alphabet Authors : Iyanar Vetrivel, Swapnil Mahajan, Manoj Tyagi, Lionel Hoffmann, Yves-Henri Sanejouand, Narayanaswamy Srinivasan, Alexandre G. de Brevern, Frédéric Cadet, Bernard Offmann Corresponding author : bernard.offmann@univ-nantes.fr FILES DESCRIPTIONS : 1) PDB30_aaseq.txt. This file contains the amino acid sequence of the PDB chains from PDB30 dataset described in the manuscript. 2) PDB30_assigned_pb.txt. This file contains the PB sequence assignment of the PDB chains from the same PDB30 dataset. PB assignment is performed according to reference [29] in the manuscript. 3) PDB30_predited_pb.txt. This file contains the predicted PB sequences of the PDB chains from the PDB30 dataset as per the hybrid method described in the manuscript.DATA_KPRED.tar.gz