{"doi":"10.1002/prot.26193","title":"Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in <scp>CASP14</scp>","abstract":"In this article, we report 3D structure prediction results by two of our best server groups (\"Zhang-Server\" and \"QUARK\") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.","journal":"Proteins Structure Function and Bioinformatics","year":2021,"id":154478,"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":63,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9577,"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":642874,"name":"Yang Li","orcid":"0000-0003-2480-1972","position":1,"is_corresponding":false},{"id":121996,"name":"Chengxin Zhang","orcid":"0000-0001-7290-1324","position":2,"is_corresponding":false},{"id":642875,"name":"Xiaogen Zhou","orcid":"0000-0001-6839-1923","position":3,"is_corresponding":false},{"id":114797,"name":"Robin Pearce","orcid":"0000-0001-6402-734X","position":4,"is_corresponding":false},{"id":615527,"name":"Eric W. Bell","orcid":"0000-0002-3419-4398","position":5,"is_corresponding":false},{"id":477118,"name":"Xiaoqiang Huang","orcid":"0000-0002-1005-848X","position":6,"is_corresponding":false},{"id":287631,"name":"Yang Zhang","orcid":"0000-0002-2739-1916","position":7,"is_corresponding":false},{"id":529892,"name":"Wei Zheng","orcid":"0000-0002-2984-9003","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-18T23:43:54.469024Z","pmid":"34331351","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":[]}