{"doi":"10.1371/journal.pcbi.1008865","title":"Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks","abstract":"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.","journal":"PLoS Computational Biology","year":2021,"id":151255,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":84,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9472,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":121996,"name":"Chengxin Zhang","orcid":"0000-0001-7290-1324","position":1,"is_corresponding":false},{"id":615527,"name":"Eric W. Bell","orcid":"0000-0002-3419-4398","position":2,"is_corresponding":false},{"id":529892,"name":"Wei Zheng","orcid":"0000-0002-2984-9003","position":3,"is_corresponding":false},{"id":642875,"name":"Xiaogen Zhou","orcid":"0000-0001-6839-1923","position":4,"is_corresponding":false},{"id":626617,"name":"Dong‐Jun Yu","orcid":"0000-0002-6786-8053","position":5,"is_corresponding":false},{"id":287631,"name":"Yang Zhang","orcid":"0000-0002-2739-1916","position":6,"is_corresponding":false},{"id":642874,"name":"Yang Li","orcid":"0000-0003-2480-1972","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-18T23:43:11.293086Z","pmid":"33770072","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}