Article

SETH predicts nuances of residue disorder from protein embeddings

2022-06-26

Abstract excerpt

Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of AlphaFold2 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 AlphaFold2 pr...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 1bd358bb-8fd0-5ede-8ffa-f327ffad69f9
DOI: 10.1101/2022.06.23.497276

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

SETH predicts nuances of residue disorder from protein embeddingsDOI 10.1101/2022.06.23.497276Read

Embeddings from protein language models predict conservation and variant effectsSemantic proximity Read

DisoPatho: A Cross-View Feature-Adaptive Interaction Encoding Framework for Predicting Disease-Associated Variants in Intrinsically Disordered Regions.Semantic proximity Read

Decoding conformational heterogeneity across disordered proteomesSemantic proximity Read

E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.Semantic proximity Read

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework.Semantic proximity Read

Single-sequence protein structure prediction using supervised transformer protein language modelsSemantic proximity Read

Interpreting Protein Language Models: high attention sites predict functional regionsSemantic proximity Read

Towards a structurally resolved human protein interaction network.Semantic proximity Read

E-SNPs&GO: Embedding of protein sequence and function improves the annotation of human pathogenic variantsSemantic proximity Read

Protein Language Models Rescue Variant Pathogenicity Prediction in Intrinsically Disordered Regions Through Synergistic Integration with Structure-Based MethodsSemantic proximity Read

pLM-SAV: A Δ-Embedding Approach for Predicting Pathogenic Single Amino Acid VariantsSemantic proximity Read

Survey of Protein Sequence Embedding ModelsSemantic proximity Read