{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:35:00Z","timestamp":1772138100123,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T00:00:00Z","timestamp":1571270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Over the past decade, there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein\u2013protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein\u2013protein inter-residue contacts remains relatively limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein\u2013protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70% and 24% and the average relative precision by 80% and 36% in comparison with two state-of-the-art methods, PSICOV and GREMLIN.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/BioMLBoston\/PatchDCA<\/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\/btz791","type":"journal-article","created":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T18:27:58Z","timestamp":1571164078000},"page":"1460-1467","source":"Crossref","is-referenced-by-count":11,"title":["Patch-DCA: improved protein interface prediction by utilizing structural information and clustering DCA scores"],"prefix":"10.1093","volume":"36","author":[{"given":"Amir","family":"Vajdi","sequence":"first","affiliation":[{"name":"Computer Science Department, University of Massachusetts Boston , Boston, MA 02125, USA"},{"name":"Department of Informatics and Analytics, Dana-Farber Cancer Institute , Boston, MA 02215, USA"}]},{"given":"Kourosh","family":"Zarringhalam","sequence":"additional","affiliation":[{"name":"Mathematics Department, University of Massachusetts Boston , Boston, MA 02125, USA"}]},{"given":"Nurit","family":"Haspel","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Massachusetts Boston , Boston, MA 02125, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,10,17]]},"reference":[{"key":"2023060910271642300_btz791-B1","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1002\/prot.20842","article-title":"Whiscy: what information does surface conservation yield? 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