ArticleNAR genomics and bioinformatics2023
ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 38 citations in OpenAlex.
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- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- From Cloudy to Clear: A Strategy to Resolve the Complex Structural Elucidation of Bicycle Drug Conjugate Zelenectide Pevedotin by NMR.Bioconjugate chemistry · 2026Article
- APOL1 plasma membrane pools resist rapid protein degradation.Scientific reports · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- AlphaFlex: Ensembles of the human proteome representing disordered regions.bioRxiv : the preprint server for biology · 2025Article
- Impact of intrinsically disordered regions and functional disorder hotspots in the human kinome.Briefings in bioinformatics · 2025Article
- Dynamic ensembles of SARS-CoV-2 N-protein reveal head-to-head coiled-coil-driven oligomerization and phase separation.Nucleic acids research · 2025Article
- Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2.International journal of molecular sciences · 2025Article
- Comparative Analysis of Drosophila Bam and Bgcn Sequences and Predicted Protein Structural Evolution.Journal of molecular evolution · 2025Article
- Easy and accurate protein structure prediction using ColabFold.Nature protocols · 2025Review
- The De Novo Emergence of Two Brain Genes in the Human Lineage Appears to be Unsupported.Journal of molecular evolution · 2025Article
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- Bilingual language model for protein sequence and structure.NAR genomics and bioinformatics · 2024Article
- ACAD10 and ACAD11 allow entry of 4-hydroxy fatty acids into β-oxidation.Cellular and molecular life sciences : CMLS · 2024Article
- Intrinsically disordered regions of the Msn2 transcription factor encode multiple functions using interwoven sequence grammars.Nucleic acids research · 2024Article
- The origin and structural evolution of de novo genes in Drosophila.Nature communications · 2024Article
- How the Intrinsically Disordered N-Terminus of Cancer/Testis Antigen MAGEA10 Is Responsible for Its Expression, Nuclear Localisation and Aberrant Migration.Biomolecules · 2023Article
- LambdaPP: Fast and accessible protein-specific phenotype predictions.Protein science : a publication of the Protein Society · 2023Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intrinsically disordered proteins (IDPs) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT (Attention DisOrder PredicTor), a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue-level representations from Facebook's Evolutionary Scale Modeling library. The latter uses a database of nuclear magnetic resonance chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and a test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features that are relevant for the prediction performance and show that good performance can already be gained with <100 features. ADOPT is available as a stand-alone package at https://github.com/PeptoneLtd/ADOPT and as a web server at https://adopt.peptone.io/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.