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UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling
Motivation: Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named ‘foldons’ through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018369/ https://www.ncbi.nlm.nih.gov/pubmed/27259540 http://dx.doi.org/10.1093/bioinformatics/btw316 |
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author | Bhattacharya, Debswapna Cao, Renzhi Cheng, Jianlin |
author_facet | Bhattacharya, Debswapna Cao, Renzhi Cheng, Jianlin |
author_sort | Bhattacharya, Debswapna |
collection | PubMed |
description | Motivation: Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named ‘foldons’ through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to perform de novo protein conformational sampling from continuous space. However, existing computational approaches for de novo protein structure prediction often randomly sample protein conformational space as opposed to experimentally suggested stepwise sampling. Results: Here, we develop a novel generative, probabilistic model that simultaneously captures local structural preferences of backbone and side chain conformational space of polypeptide chains in a united-residue representation and performs experimentally motivated conditional conformational sampling via stepwise synthesis and assembly of foldon units that minimizes a composite physics and knowledge-based energy function for de novo protein structure prediction. The proposed method, UniCon3D, has been found to (i) sample lower energy conformations with higher accuracy than traditional random sampling in a small benchmark of 6 proteins; (ii) perform comparably with the top five automated methods on 30 difficult target domains from the 11th Critical Assessment of Protein Structure Prediction (CASP) experiment and on 15 difficult target domains from the 10th CASP experiment; and (iii) outperform two state-of-the-art approaches and a baseline counterpart of UniCon3D that performs traditional random sampling for protein modeling aided by predicted residue-residue contacts on 45 targets from the 10th edition of CASP. Availability and Implementation: Source code, executable versions, manuals and example data of UniCon3D for Linux and OSX are freely available to non-commercial users at http://sysbio.rnet.missouri.edu/UniCon3D/. Contact: chengji@missouri.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-5018369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-50183692016-09-12 UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling Bhattacharya, Debswapna Cao, Renzhi Cheng, Jianlin Bioinformatics Original Papers Motivation: Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named ‘foldons’ through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to perform de novo protein conformational sampling from continuous space. However, existing computational approaches for de novo protein structure prediction often randomly sample protein conformational space as opposed to experimentally suggested stepwise sampling. Results: Here, we develop a novel generative, probabilistic model that simultaneously captures local structural preferences of backbone and side chain conformational space of polypeptide chains in a united-residue representation and performs experimentally motivated conditional conformational sampling via stepwise synthesis and assembly of foldon units that minimizes a composite physics and knowledge-based energy function for de novo protein structure prediction. The proposed method, UniCon3D, has been found to (i) sample lower energy conformations with higher accuracy than traditional random sampling in a small benchmark of 6 proteins; (ii) perform comparably with the top five automated methods on 30 difficult target domains from the 11th Critical Assessment of Protein Structure Prediction (CASP) experiment and on 15 difficult target domains from the 10th CASP experiment; and (iii) outperform two state-of-the-art approaches and a baseline counterpart of UniCon3D that performs traditional random sampling for protein modeling aided by predicted residue-residue contacts on 45 targets from the 10th edition of CASP. Availability and Implementation: Source code, executable versions, manuals and example data of UniCon3D for Linux and OSX are freely available to non-commercial users at http://sysbio.rnet.missouri.edu/UniCon3D/. Contact: chengji@missouri.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-09-15 2016-06-03 /pmc/articles/PMC5018369/ /pubmed/27259540 http://dx.doi.org/10.1093/bioinformatics/btw316 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Bhattacharya, Debswapna Cao, Renzhi Cheng, Jianlin UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling |
title | UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling |
title_full | UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling |
title_fullStr | UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling |
title_full_unstemmed | UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling |
title_short | UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling |
title_sort | unicon3d: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018369/ https://www.ncbi.nlm.nih.gov/pubmed/27259540 http://dx.doi.org/10.1093/bioinformatics/btw316 |
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