{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["2"],"submitter":["Ilzhofer D"],"pubmed_abstract":["Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of <i>AlphaFold2</i> 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's Disease. We showed that three recent disorder measures of <i>AlphaFold2</i> predictions (pLDDT, \"experimentally resolved\" prediction and \"relative solvent accessibility\") 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, especial"],"journal":["Frontiers in bioinformatics"],"pagination":["1019597"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9580958"],"repository":["biostudies-literature"],"pubmed_title":["SETH predicts nuances of residue disorder from protein embeddings."],"pmcid":["PMC9580958"],"pubmed_authors":["Heinzinger M","Ilzhofer D","Rost B"],"additional_accession":[]},"is_claimable":false,"name":"SETH predicts nuances of residue disorder from protein embeddings.","description":"Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of <i>AlphaFold2</i> 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's Disease. We showed that three recent disorder measures of <i>AlphaFold2</i> predictions (pLDDT, \"experimentally resolved\" prediction and \"relative solvent accessibility\") 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, especial","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T11:29:27.962Z","creation":"2025-04-04T11:29:27.962Z"},"accession":"S-EPMC9580958","cross_references":{"pubmed":["36304335"],"doi":["10.3389/fbinf.2022.1019597"]}}