Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks...
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Public Library of Science (PLoS)
2021-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008865&type=printable |
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| author | Yang Li Chengxin Zhang Eric W Bell Wei Zheng Xiaogen Zhou Dong-Jun Yu Yang Zhang |
| author_facet | Yang Li Chengxin Zhang Eric W Bell Wei Zheng Xiaogen Zhou Dong-Jun Yu Yang Zhang |
| author_sort | Yang Li |
| collection | DOAJ |
| description | The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library. |
| format | Article |
| id | doaj-art-ebd691bba94f4b35990eb6e0cb86a802 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Computational Biology |
| spelling | doaj-art-ebd691bba94f4b35990eb6e0cb86a8022025-08-20T02:01:03ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-03-01173e100886510.1371/journal.pcbi.1008865Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.Yang LiChengxin ZhangEric W BellWei ZhengXiaogen ZhouDong-Jun YuYang ZhangThe topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008865&type=printable |
| spellingShingle | Yang Li Chengxin Zhang Eric W Bell Wei Zheng Xiaogen Zhou Dong-Jun Yu Yang Zhang Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. PLoS Computational Biology |
| title | Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. |
| title_full | Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. |
| title_fullStr | Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. |
| title_full_unstemmed | Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. |
| title_short | Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. |
| title_sort | deducing high accuracy protein contact maps from a triplet of coevolutionary matrices through deep residual convolutional networks |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008865&type=printable |
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