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A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers
MOTIVATION: Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963319/ https://www.ncbi.nlm.nih.gov/pubmed/35134816 http://dx.doi.org/10.1093/bioinformatics/btac063 |
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author | Roy, Raj S Quadir, Farhan Soltanikazemi, Elham Cheng, Jianlin |
author_facet | Roy, Raj S Quadir, Farhan Soltanikazemi, Elham Cheng, Jianlin |
author_sort | Roy, Raj S |
collection | PubMed |
description | MOTIVATION: Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue–residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue–residue contacts in homodimers from residue–residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue–residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. RESULTS: Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset and CASP-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.10% and 33.50% respectively at 6 Å contact threshold, which is substantially better than DeepHomo and DNCON2_inter and similar to Glinter. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs well, even though its accuracy is lower than using true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers. AVAILABILITY AND IMPLEMENTATION: The source code of DRCon is available at https://github.com/jianlin-cheng/DRCon. The datasets are available at https://zenodo.org/record/5998532#.YgF70vXMKsB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8963319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-89633192022-03-29 A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers Roy, Raj S Quadir, Farhan Soltanikazemi, Elham Cheng, Jianlin Bioinformatics Original Papers MOTIVATION: Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue–residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue–residue contacts in homodimers from residue–residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue–residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. RESULTS: Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset and CASP-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.10% and 33.50% respectively at 6 Å contact threshold, which is substantially better than DeepHomo and DNCON2_inter and similar to Glinter. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs well, even though its accuracy is lower than using true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers. AVAILABILITY AND IMPLEMENTATION: The source code of DRCon is available at https://github.com/jianlin-cheng/DRCon. The datasets are available at https://zenodo.org/record/5998532#.YgF70vXMKsB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-02-04 /pmc/articles/PMC8963319/ /pubmed/35134816 http://dx.doi.org/10.1093/bioinformatics/btac063 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://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 Roy, Raj S Quadir, Farhan Soltanikazemi, Elham Cheng, Jianlin A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers |
title | A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers |
title_full | A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers |
title_fullStr | A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers |
title_full_unstemmed | A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers |
title_short | A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers |
title_sort | deep dilated convolutional residual network for predicting interchain contacts of protein homodimers |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963319/ https://www.ncbi.nlm.nih.gov/pubmed/35134816 http://dx.doi.org/10.1093/bioinformatics/btac063 |
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