{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:14:44Z","timestamp":1787008484062,"version":"build-2736575974"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2018,12,24]],"date-time":"2018-12-24T00:00:00Z","timestamp":1545609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"publisher","award":["VR-NT 2016-03798"],"award-info":[{"award-number":["VR-NT 2016-03798"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"name":"High Performance Computing Center North"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Residue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we introduce a novel contact predictor, PconsC4, which performs on par with state of the art methods. PconsC4 is heavily optimized, does not use any external programs and therefore is significantly faster and easier to use than other methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>PconsC4 is freely available under the GPL license from https:\/\/github.com\/ElofssonLab\/PconsC4. Installation is easy using the pip command and works on any system with Python 3.5 or later and a GCC compiler. It does not require a GPU nor special hardware.<\/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\/bty1036","type":"journal-article","created":{"date-parts":[[2018,12,20]],"date-time":"2018-12-20T15:19:01Z","timestamp":1545319141000},"page":"2677-2679","source":"Crossref","is-referenced-by-count":89,"title":["PconsC4: fast, accurate and hassle-free contact predictions"],"prefix":"10.1093","volume":"35","author":[{"given":"Mirco","family":"Michel","sequence":"first","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Men\u00e9ndez Hurtado","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arne","family":"Elofsson","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,12,24]]},"reference":[{"key":"2023062713264329600_bty1036-B1","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1093\/bioinformatics\/btx781","article-title":"DNCON2: improved protein contact prediction using two-level deep convolutional neural networks","volume":"34","author":"Adhikari","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062713264329600_bty1036-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0092721","article-title":"Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners","volume":"9","author":"Baldassi","year":"2014","journal-title":"PLoS One"},{"key":"2023062713264329600_bty1036-B3","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1002\/prot.25379","article-title":"Improved protein contact predictions with the MetaPSICOV server in CASP12","volume":"86","author":"Buchan","year":"2018","journal-title":"Proteins"},{"key":"2023062713264329600_bty1036-B4","doi-asserted-by":"crossref","first-page":"014001.","DOI":"10.1088\/1749-4680\/8\/1\/014001","article-title":"Pythran: enabling static optimization of scientific Python programs","volume":"8","author":"Guelton","year":"2015","journal-title":"Comput. 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