{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T21:10:44Z","timestamp":1778274644299,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013590","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000}}],"reference-count":70,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35 GM141818"],"award-info":[{"award-number":["R35 GM141818"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["GM122518"],"award-info":[{"award-number":["GM122518"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007092","name":"Rensselaer Polytechnic Institute","doi-asserted-by":"publisher","award":["Bio-computing and Bio-informatics Constellation Chair Fund"],"award-info":[{"award-number":["Bio-computing and Bio-informatics Constellation Chair Fund"]}],"id":[{"id":"10.13039\/100007092","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The Solute Carrier (SLC) superfamily of integral membrane proteins transport a wide array of small molecules across plasma and organelle membranes, and\u00a0function as important drug transporters and as viral receptors. They populate different conformational states during the solute transport process, including outward-open, intermediate (occluded), and inward-open conformational states. For some SLC proteins this structural \u201cflipping\u201d corresponds to swapping between conformations of their N-terminal and C-terminal symmetry-related sub-structures. Conventional AlphaFold2, AlphaFold3, or Evolutionary Scale Modeling methods typically generate models for only one of these multiple conformational states of SLC proteins. While several AI-based protocols for modeling multiple conformational states of proteins have been described recently, these methods are often impacted by \u201cmemorization\u201d of one of the alternative conformational states, and do not always provide both the inward- and outward-open conformations of SLC proteins. Here we assess the impact of memorization in modeling SLC proteins with AlphaFold2\/3, and describe a combined ESM \u2013 template-based-modeling process, based on a previously described template-based modeling method that relies on the internal pseudo-symmetry of many SLC proteins, to consistently model the alternate conformational states of SLC proteins. We also demonstrate how the resulting multi-state models can be validated by comparison with sequence-based evolutionary co-variance data (ECs) that encode information about contacts present in the various conformational states adopted by the protein. This simple, rapid, and robust approach for modeling conformational landscapes of pseudo-symmetric SLC proteins is demonstrated for several integral membrane protein transporters, including SLC35F2 the receptor of a feline leukemia virus envelope protein required for viral entry into eukaryotic cells.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013590","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T17:43:54Z","timestamp":1760723034000},"page":"e1013590","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Memorization bias impacts modeling of alternative conformational states of solute carrier membrane proteins with methods from deep learning"],"prefix":"10.1371","volume":"21","author":[{"given":"G.V.T.","family":"Swapna","sequence":"first","affiliation":[]},{"given":"Namita","family":"Dube","sequence":"additional","affiliation":[]},{"given":"Monica 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