{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T03:47:55Z","timestamp":1793245675300,"version":"4.5.0"},"reference-count":35,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"6615","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Science"],"published-print":{"date-parts":[[2022,10,7]]},"abstract":"<jats:p>Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning\u2013based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo\u2013electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.<\/jats:p>","DOI":"10.1126\/science.add2187","type":"journal-article","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T14:00:15Z","timestamp":1663250415000},"page":"49-56","source":"Crossref","is-referenced-by-count":2338,"title":["Robust deep learning\u2013based protein sequence design using ProteinMPNN"],"prefix":"10.1126","volume":"378","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-144X","authenticated-orcid":true,"given":"J.","family":"Dauparas","sequence":"first","affiliation":[{"name":"Department of Biochemistry, University of Washington, Seattle, WA, USA."},{"name":"Institute for Protein Design, University of Washington, Seattle, WA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3645-2044","authenticated-orcid":true,"given":"I.","family":"Anishchenko","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, University of Washington, Seattle, WA, USA."},{"name":"Institute for Protein Design, University of Washington, Seattle, WA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8590-1454","authenticated-orcid":true,"given":"N.","family":"Bennett","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, University of Washington, Seattle, WA, USA."},{"name":"Institute for Protein Design, University of Washington, Seattle, WA, USA."},{"name":"Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0448-4052","authenticated-orcid":true,"given":"H.","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, University of Washington, Seattle, WA, USA."},{"name":"Institute for Protein Design, University of Washington, Seattle, WA, USA."},{"name":"Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6463-1595","authenticated-orcid":true,"given":"R. 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Y.","family":"Leung","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, University of Washington, Seattle, WA, USA."},{"name":"Institute for Protein Design, University of Washington, Seattle, WA, USA."},{"name":"Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5455-416X","authenticated-orcid":true,"given":"T. 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