Skip to Main content Skip to Navigation
Journal articles

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

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 cutoff. 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
Complete list of metadata

Cited literature [39 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01677391
Contributor : Yves-Henri Sanejouand Connect in order to contact the contributor
Submitted on : Tuesday, July 3, 2018 - 1:32:12 PM
Last modification on : Tuesday, October 19, 2021 - 5:56:23 PM
Long-term archiving on: : Monday, October 1, 2018 - 10:15:09 AM

File

Knowledge_based_prediction_of_...
Publisher files allowed on an open archive

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Iyanar Vetrivel, Swapnil Mahajan, Manoj Tyagi, Lionel Hoffmann, Yves-Henri Sanejouand, et al.. Knowledge-based prediction of protein backbone conformation using a structural alphabet. PLoS ONE, Public Library of Science, 2017, 12 (11), pp.e0186215. ⟨10.1371/journal.pone.0186215⟩. ⟨hal-01677391⟩

Share

Metrics

Record views

819

Files downloads

1468