{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T06:17:14Z","timestamp":1775715434255,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Swedish National Research Council","award":["VR-NT-2016-03798"],"award-info":[{"award-number":["VR-NT-2016-03798"]}]},{"name":"Swedish Research Council partly paid the salary"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSAs). The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein\u2013protein interfaces. However, most earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This article introduces a fold-and-dock method based on predicted residue-residue distances with trRosetta.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The method can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein\u2013protein docking yielded limited success. However, using alternative methods for generating MSAs allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. This methodology contributed to the best model for one of the CASP14 oligomeric targets, H1065.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>All scripts for predictions and analysis are available from https:\/\/github.com\/ElofssonLab\/bioinfo-toolbox\/ and https:\/\/gitlab.com\/ElofssonLab\/benchmark5\/. All models joined alignments, and evaluation results are available from the following figshare repository https:\/\/doi.org\/10.6084\/m9.figshare.14654886.v2.<\/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\/btab760","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T15:18:03Z","timestamp":1636471083000},"page":"954-961","source":"Crossref","is-referenced-by-count":19,"title":["Limits and potential of combined folding and docking"],"prefix":"10.1093","volume":"38","author":[{"given":"Gabriele","family":"Pozzati","sequence":"first","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University , 171 21 Solna, Sweden"}]},{"given":"Wensi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University , 171 21 Solna, Sweden"}]},{"given":"Claudio","family":"Bassot","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University , 171 21 Solna, Sweden"}]},{"given":"John","family":"Lamb","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University , 171 21 Solna, Sweden"}]},{"given":"Petras","family":"Kundrotas","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University , 171 21 Solna, Sweden"},{"name":"Center for Computational Biology, The University of Kansas , Lawrence, KS 66047, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7115-9751","authenticated-orcid":false,"given":"Arne","family":"Elofsson","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University , 171 21 Solna, Sweden"}]}],"member":"286","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"2023020108523447200_btab760-B1","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1146\/annurev-biophys-083012-130253","article-title":"Advances, interactions, and future developments in the CNS, Phenix, and Rosetta structural biology software systems","volume":"42","author":"Adams","year":"2013","journal-title":"Annu. 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