{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T04:10:21Z","timestamp":1783224621797,"version":"3.54.6"},"reference-count":113,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:p>\n                    Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of\n                    <jats:italic>AlphaFold2<\/jats:italic>\n                    results for UniProt. However, many proteins have so-called intrinsically disordered regions (IDRs) that do not adopt unique structures in isolation. These IDRs are associated with several diseases, including Alzheimer\u2019s Disease. We showed that three recent disorder measures of\n                    <jats:italic>AlphaFold2<\/jats:italic>\n                    predictions (pLDDT, \u201cexperimentally resolved\u201d prediction and \u201crelative solvent accessibility\u201d) correlated to some extent with IDRs. However, expert methods predict IDRs more reliably by combining complex machine learning models with expert-crafted input features and evolutionary information from multiple sequence alignments (MSAs). MSAs are not always available, especially for IDRs, and are computationally expensive to generate, limiting the scalability of the associated tools. Here, we present the novel method SETH that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input. Thereby, our method, relying on a relatively shallow convolutional neural network, outperformed much more complex solutions while being much faster, allowing to create predictions for the human proteome in about 1\u00a0hour on a consumer-grade PC with one NVIDIA GeForce RTX 3060. Trained on a continuous disorder scale (CheZOD scores), our method captured subtle variations in disorder, thereby providing important information beyond the binary classification of most methods. High performance paired with speed revealed that SETH\u2019s nuanced disorder predictions for entire proteomes capture aspects of the evolution of organisms. Additionally, SETH could also be used to filter out regions or proteins with probable low-quality\n                    <jats:italic>AlphaFold2<\/jats:italic>\n                    3D structures to prioritize running the compute-intensive predictions for large data sets. SETH is freely publicly available at:\n                    <jats:ext-link>https:\/\/github.com\/Rostlab\/SETH<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.3389\/fbinf.2022.1019597","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:09:25Z","timestamp":1665378565000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["SETH predicts nuances of residue disorder from protein embeddings"],"prefix":"10.3389","volume":"2","author":[{"given":"Dagmar","family":"Ilzh\u00f6fer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Heinzinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Burkhard","family":"Rost","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1101\/2021.09.26.461876","article-title":"A structural biology community assessment of AlphaFold 2 applications","author":"Akdel","year":"2021"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","article-title":"Unified rational protein engineering with sequence-based deep representation learning","volume":"16","author":"Alley","year":"2019","journal-title":"Nat. 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