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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&amp;12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&amp;12 targets and 44.4% for the CAMEO targets in the top-\n                    <jats:italic>L<\/jats:italic>\n                    long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-\n                    <jats:italic>L<\/jats:italic>\n                    \/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.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1008865","type":"journal-article","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T13:46:30Z","timestamp":1616766390000},"page":"e1008865","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":69,"title":["Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional 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