Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

DSpace/Manakin Repository

Show simple item record

dc.contributor.author Walsh, Ian
dc.contributor.author Baù, Davide
dc.contributor.author Martin, Alberto J. M.
dc.contributor.author Mooney, Catherine
dc.contributor.author Vullo, Alessandro
dc.contributor.author Pollastri, Gianluca
dc.date.accessioned 2011-12-20T14:36:17Z
dc.date.available 2011-12-20T14:36:17Z
dc.date.copyright 2009 Walsh et al; licensee BioMed Central Ltd. en
dc.date.issued 2009-01-30
dc.identifier.citation BMC Structural Biology en
dc.identifier.uri http://hdl.handle.net/10197/3409
dc.description.abstract Background: Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure). Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. Results: We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that C trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10%) yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment) from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 Å predictions on the CASP7 targets using a pre-CASP7 PDB, and find that both predictors are state-of-the-art, with the template-based one far outperforming the best CASP7 systems if templates with sequence identity to the query of 10% or better are available. Although this is not the main focus of this paper we also report on reconstructions of C traces based on both ab initio and template-based 4-class map predictions, showing that the latter are generally more accurate even when homology is dubious. Conclusion: Accurate predictions of multi-class maps may provide valuable constraints for improved ab initio and template-based prediction of protein structures, naturally incorporate multiple templates, and yield state-of-the- art binary maps. Predictions of protein structures and 8 Å contact maps based on the multi-class distance map predictors described in this paper are freely available to academic users at the url http://distill.ucd.ie/. en
dc.description.sponsorship Science Foundation Ireland en
dc.description.sponsorship Health Research Board en
dc.format.extent 990863 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher BioMed Central en
dc.rights This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en
dc.rights.uri CC BY 2.0 en
dc.rights.uri http://creativecommons.org/licenses/by/2.0/ en
dc.subject 2D-RNN en
dc.subject Multi-class distance map prediction en
dc.subject Neural networks en
dc.subject Contact maps en
dc.subject.lcsh Proteins--Structure en
dc.subject.lcsh Neural networks (Computer science) en
dc.title Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions http://www.biomedcentral.com/1472-6807/9/5 en
dc.status Peer reviewed en
dc.identifier.volume 9 en
dc.identifier.issue 5 en
dc.identifier.doi 10.1186/1472-6807-9-5
dc.neeo.contributor Walsh|Ian|aut| en
dc.neeo.contributor Baù|Davide|aut| en
dc.neeo.contributor Martin|Alberto J. M.|aut| en
dc.neeo.contributor Mooney|Catherine|aut| en
dc.neeo.contributor Vullo|Alessandro|aut| en
dc.neeo.contributor Pollastri|Gianluca|aut| en
dc.description.othersponsorship UCD President's Award 2004 en
dc.description.admin au, ti, sp, ke, ab - kpw16/12/11 en

This item appears in the following Collection(s)

Show simple item record

This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.

If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.

Search Research Repository

Advanced Search