{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:28:38Z","timestamp":1772555318455,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2017,5,3]],"date-time":"2017-05-03T00:00:00Z","timestamp":1493769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31670723"],"award-info":[{"award-number":["31670723"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31470033"],"award-info":[{"award-number":["31470033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472205"],"award-info":[{"award-number":["61472205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Residue\u2013residue contacts are of great value for protein structure prediction, since contact information, especially from those long-range residue pairs, can significantly reduce the complexity of conformational sampling for protein structure prediction in practice. Despite progresses in the past decade on protein targets with abundant homologous sequences, accurate contact prediction for proteins with limited sequence information is still far from satisfaction. Methodologies for these hard targets still need further improvement.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We presented a computational program DeepConPred, which includes a pipeline of two novel deep-learning-based methods (DeepCCon and DeepRCon) as well as a contact refinement step, to improve the prediction of long-range residue contacts from primary sequences. When compared with previous prediction approaches, our framework employed an effective scheme to identify optimal and important features for contact prediction, and was only trained with coevolutionary information derived from a limited number of homologous sequences to ensure robustness and usefulness for hard targets. Independent tests showed that 59.33%\/49.97%, 64.39%\/54.01% and 70.00%\/59.81% of the top L\/5, top L\/10 and top 5 predictions were correct for CASP10\/CASP11 proteins, respectively. In general, our algorithm ranked as one of the best methods for CASP targets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>All source data and codes are available at http:\/\/166.111.152.91\/Downloads.html.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx296","type":"journal-article","created":{"date-parts":[[2017,5,2]],"date-time":"2017-05-02T11:11:48Z","timestamp":1493723508000},"page":"2675-2683","source":"Crossref","is-referenced-by-count":42,"title":["A deep learning framework for improving long-range residue\u2013residue contact prediction using a hierarchical strategy"],"prefix":"10.1093","volume":"33","author":[{"given":"Dapeng","family":"Xiong","sequence":"first","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China"},{"name":"Beijing Innovation Center of Structural Biology, Tsinghua University, Beijing, China"}]},{"given":"Jianyang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Beijing Innovation Center of Structural Biology, Tsinghua University, Beijing, China"},{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China"}]},{"given":"Haipeng","family":"Gong","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China"},{"name":"Beijing Innovation Center of Structural Biology, Tsinghua University, Beijing, China"}]}],"member":"286","published-online":{"date-parts":[[2017,5,3]]},"reference":[{"key":"2023020206263141300_btx296-B1","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1002\/prot.24829","article-title":"CONFOLD: residue-residue contact-guided ab initio protein folding","volume":"83","author":"Adhikari","year":"2015","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B2","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023020206263141300_btx296-B3","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1093\/bioinformatics\/btp149","article-title":"Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue\u2013residue contacts","volume":"25","author":"Bj\u00f6rkholm","year":"2009","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B4","doi-asserted-by":"crossref","first-page":"369","DOI":"10.4310\/SII.2009.v2.n3.a10","article-title":"Penalized methods for bi-level variable selection","volume":"2","author":"Breheny","year":"2009","journal-title":"Stat. Interface"},{"key":"2023020206263141300_btx296-B5","doi-asserted-by":"crossref","first-page":"S6","DOI":"10.1186\/1471-2105-9-S12-S6","article-title":"Predicting RNA-binding sites of proteins using support vector machines and evolutionary information","volume":"9","author":"Cheng","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023020206263141300_btx296-B6","doi-asserted-by":"crossref","first-page":"113.","DOI":"10.1186\/1471-2105-8-113","article-title":"Improved residue contact prediction using support vector machines and a large feature set","volume":"8","author":"Cheng","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023020206263141300_btx296-B7","doi-asserted-by":"crossref","first-page":"2449","DOI":"10.1093\/bioinformatics\/bts475","article-title":"Deep architectures for protein contact map prediction","volume":"28","author":"Di Lena","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B8","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1093\/bioinformatics\/btm604","article-title":"Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction","volume":"24","author":"Dunn","year":"2008","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B9","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1093\/bioinformatics\/bts598","article-title":"Predicting protein residue\u2013residue contacts using deep networks and boosting","volume":"28","author":"Eickholt","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B10","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.jcp.2014.07.024","article-title":"Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences","volume":"276","author":"Ekeberg","year":"2014","journal-title":"J. Comput. Phys"},{"key":"2023020206263141300_btx296-B11","doi-asserted-by":"crossref","first-page":"012707","DOI":"10.1103\/PhysRevE.87.012707","article-title":"Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models","volume":"87","author":"Ekeberg","year":"2013","journal-title":"Phys. Rev. E"},{"key":"2023020206263141300_btx296-B12","doi-asserted-by":"crossref","first-page":"D304","DOI":"10.1093\/nar\/gkt1240","article-title":"SCOPe: Structural Classification of Proteins\u2014extended, integrating SCOP and ASTRAL data and classification of new structures","volume":"42","author":"Fox","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023020206263141300_btx296-B13","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput"},{"key":"2023020206263141300_btx296-B14","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"2023020206263141300_btx296-B15","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1214\/12-STS392","article-title":"A selective review of group selection in high-dimensional models","volume":"27","author":"Huang","year":"2012","journal-title":"Stat. Sci"},{"key":"2023020206263141300_btx296-B16","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1093\/bioinformatics\/btr638","article-title":"PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments","volume":"28","author":"Jones","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B17","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1093\/bioinformatics\/btu791","article-title":"MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins","volume":"31","author":"Jones","year":"2015","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B18","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1002\/bip.360221211","article-title":"Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features","volume":"22","author":"Kabsch","year":"1983","journal-title":"Biopolymers"},{"key":"2023020206263141300_btx296-B19","doi-asserted-by":"crossref","first-page":"15674","DOI":"10.1073\/pnas.1314045110","article-title":"Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era","volume":"110","author":"Kamisetty","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020206263141300_btx296-B20","doi-asserted-by":"crossref","first-page":"13797","DOI":"10.1073\/pnas.0906514106","article-title":"Peptides modulating conformational changes in secreted chaperones: from in silico design to preclinical proof of concept","volume":"106","author":"Kliger","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020206263141300_btx296-B21","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1002\/prot.24863","article-title":"Accurate contact predictions using covariation techniques and machine learning","volume":"84","author":"Kosciolek","year":"2015","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B22","doi-asserted-by":"crossref","first-page":"2506","DOI":"10.1093\/bioinformatics\/btp455","article-title":"A new method for revealing correlated mutations under the structural and functional constraints in proteins","volume":"25","author":"Lee","year":"2009","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B23","doi-asserted-by":"crossref","first-page":"3379","DOI":"10.1093\/bioinformatics\/btr579","article-title":"Predicting residue\u2013residue contacts using random forest models","volume":"27","author":"Li","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B24","doi-asserted-by":"crossref","first-page":"e4762.","DOI":"10.1371\/journal.pone.0004762","article-title":"Identification of coevolving residues and coevolution potentials emphasizing structure, bond formation and catalytic coordination in protein evolution","volume":"4","author":"Little","year":"2009","journal-title":"PLoS One"},{"key":"2023020206263141300_btx296-B25","doi-asserted-by":"crossref","first-page":"3506","DOI":"10.1093\/bioinformatics\/btv472","article-title":"Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning","volume":"31","author":"Ma","year":"2015","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B26","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1093\/bioinformatics\/btu352","article-title":"SSpro\/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity","volume":"30","author":"Magnan","year":"2014","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B27","doi-asserted-by":"crossref","first-page":"e28766","DOI":"10.1371\/journal.pone.0028766","article-title":"Protein 3D structure computed from evolutionary sequence variation","volume":"6","author":"Marks","year":"2011","journal-title":"PLoS ONE"},{"key":"2023020206263141300_btx296-B28","doi-asserted-by":"crossref","first-page":"i482","DOI":"10.1093\/bioinformatics\/btu458","article-title":"PconsFold: improved contact predictions improve protein models","volume":"30","author":"Michel","year":"2014","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B29","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1093\/bioinformatics\/btn248","article-title":"Using inferred residue contacts to distinguish between correct and incorrect protein models","volume":"24","author":"Miller","year":"2008","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B30","doi-asserted-by":"crossref","first-page":"5361","DOI":"10.1073\/pnas.0509355103","article-title":"Physically realistic homology models built with rosetta can be more accurate than their templates","volume":"103","author":"Misura","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020206263141300_btx296-B31","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1002\/prot.24340","article-title":"Evaluation of residue\u2013residue contact prediction in CASP10","volume":"82","author":"Monastyrskyy","year":"2014","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B32","first-page":"1","article-title":"New encouraging developments in contact prediction: Assessment of the CASP11 results","volume":"84","author":"Monastyrskyy","year":"2015","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B33","doi-asserted-by":"crossref","first-page":"E1293","DOI":"10.1073\/pnas.1111471108","article-title":"Direct-coupling analysis of residue coevolution captures native contacts across many protein families","volume":"108","author":"Morcos","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020206263141300_btx296-B34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","article-title":"Deep learning applications and challenges in big data analytics","volume":"2","author":"Najafabadi","year":"2015","journal-title":"J. Big Data"},{"key":"2023020206263141300_btx296-B35","doi-asserted-by":"crossref","first-page":"2960","DOI":"10.1093\/bioinformatics\/bti454","article-title":"PROFcon: novel prediction of long-range contacts","volume":"21","author":"Punta","year":"2005","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B36","doi-asserted-by":"crossref","first-page":"e108438.","DOI":"10.1371\/journal.pone.0108438","article-title":"Combining physicochemical and evolutionary information for protein contact prediction","volume":"9","author":"Schneider","year":"2014","journal-title":"PLoS One"},{"key":"2023020206263141300_btx296-B37","doi-asserted-by":"crossref","first-page":"3128","DOI":"10.1093\/bioinformatics\/btu500","article-title":"CCMpred\u2014fast and precise prediction of protein residue\u2013residue contacts from correlated mutations","volume":"30","author":"Seemayer","year":"2014","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B38","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1002\/prot.10539","article-title":"Predicting interresidue contacts using templates and pathways","volume":"53","author":"Shao","year":"2003","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B39","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1002\/prot.20106","article-title":"Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm","volume":"56","author":"Skolnick","year":"2004","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B40","doi-asserted-by":"crossref","first-page":"e1003889","DOI":"10.1371\/journal.pcbi.1003889","article-title":"Improved contact predictions using the recognition of protein like contact patterns","volume":"10","author":"Skwark","year":"2014","journal-title":"PLoS Comp. Biol"},{"key":"2023020206263141300_btx296-B41","doi-asserted-by":"crossref","first-page":"W515","DOI":"10.1093\/nar\/gkp305","article-title":"NNcon: improved protein contact map prediction using 2D-recursive neural networks","volume":"37","author":"Tegge","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2023020206263141300_btx296-B42","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1002\/prot.22714","article-title":"Predicted residue\u2013residue contacts can help the scoring of 3D models","volume":"78","author":"Tress","year":"2010","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B43","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TCBB.2008.27","article-title":"Reconstruction of 3D structures from protein contact maps","volume":"5","author":"Vassura","year":"2008","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform"},{"key":"2023020206263141300_btx296-B44","doi-asserted-by":"crossref","first-page":"i266","DOI":"10.1093\/bioinformatics\/btt211","article-title":"Predicting protein contact map using evolutionary and physical constraints by integer programming","volume":"29","author":"Wang","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B45","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1073\/pnas.0805923106","article-title":"Identification of direct residue contacts in protein\u2013protein interaction by message passing","volume":"106","author":"Weigt","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020206263141300_btx296-B46","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1093\/bioinformatics\/btn069","article-title":"A comprehensive assessment of sequence-based and template-based methods for protein contact prediction","volume":"24","author":"Wu","year":"2008","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B47","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1002\/prot.24806","article-title":"RBRIdent: an algorithm for improved identification of RNA-binding residues in proteins from primary sequences","volume":"83","author":"Xiong","year":"2015","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B48","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1002\/prot.22329","article-title":"Predicting residue\u2013residue contact maps by a two-layer, integrated neural-network method","volume":"76","author":"Xue","year":"2009","journal-title":"Proteins"},{"key":"2023020206263141300_btx296-B49","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1093\/bioinformatics\/btw181","article-title":"R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter","volume":"32","author":"Yang","year":"2016","journal-title":"Bioinformatics"},{"key":"2023020206263141300_btx296-B50","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.jtbi.2012.11.005","article-title":"Protein space: a natural method for realizing the nature of protein universe","volume":"318","author":"Yu","year":"2013","journal-title":"J. Theor. Biol"},{"key":"2023020206263141300_btx296-B51","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s10822-005-0578-7","article-title":"Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme","volume":"18","author":"Zhang","year":"2004","journal-title":"J. Comput. Aid. Mol. Des"},{"key":"2023020206263141300_btx296-B52","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0006-3495(03)74551-2","article-title":"TOUCHSTONE II: a new approach to ab initio protein structure prediction","volume":"85","author":"Zhang","year":"2003","journal-title":"Biophys. J"},{"key":"2023020206263141300_btx296-B53","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1142\/S0218213005002429","article-title":"Prediction of contact maps using support vector machines","volume":"14","author":"Zhao","year":"2005","journal-title":"Int. J. Artif. Intell. Tools"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/17\/2675\/49040687\/bioinformatics_33_17_2675.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/17\/2675\/49040687\/bioinformatics_33_17_2675.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T06:29:07Z","timestamp":1675319347000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/33\/17\/2675\/3791808"}},"subtitle":[],"editor":[{"given":"Alfonso","family":"Valencia","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2017,5,3]]},"references-count":53,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2017,9,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btx296","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2017,9,1]]},"published":{"date-parts":[[2017,5,3]]}}}