ArticleProteins2026
Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models.
Article in Proteins, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- ProteoCast: a web server to predict, validate, and interpret missense variant effects.Journal of molecular biology · 2026Article
- Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.BMC bioinformatics · 2026Article
- DPLM: Dynamics-aware Protein Language Model via contrastive learning between sequence and molecular dynamics simulation trajectory.bioRxiv : the preprint server for biology · 2026Article
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- Proteome-wide prediction of the functional impact of missense variants with ProteoCast.Nature communications · 2026Article
- Disordered but Different: Functional and Evolutionary Divergence of Transcription Factor Intrinsically Disordered Regions.bioRxiv : the preprint server for biology · 2026Article
- Hybrid deep learning with protein language models and dual-path architecture for predicting IDP functions.Briefings in bioinformatics · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- ESMDisPred: A Structure-Aware CNN-Transformer Architecture for Intrinsically Disordered Protein Prediction.bioRxiv : the preprint server for biology · 2026Article
- DisProt in 2026: enhancing intrinsically disordered proteins accessibility, deposition, and annotation.Nucleic acids research · 2026Article
- Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models.Proteins · 2026Article
- Computational Characterization of Pathogenic LMNA Missense Variants: Structural Instability, Altered Binding, and Conformational Dynamics.Human mutation · 2026Article
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Authors and funding
6 authors.
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Abstract
Intrinsic disorder (ID) in proteins is a complex phenomenon, encompassing a continuum from entirely disordered regions to structured domains with flexible segments. The absence of a ground truth for all forms of disorder, combined with the possibility of structural transitions between ordered and disordered states under specific conditions, makes accurate prediction of ID especially challenging. The Critical Assessment of Protein Intrinsic Disorder (CAID) evaluates ID prediction methods using diverse benchmarks derived from DisProt, a manually curated database of experimentally validated annotations. This paper presents findings from the third round (CAID3), in which 24 new methods were assessed along with the predictors from previous rounds. Compared to CAID2, the top-performing methods in CAID3 demonstrated significant gains in average precision: over 31% improvement in predicting linker regions, and 15% in disorder prediction. This round introduces a new binding sub-challenge focused on identifying binding regions within known IDR boundaries. The results indicate that this task remains challenging, highlighting the potential for improvement. The top-performing methods in CAID3 are mostly new and commonly used embeddings from protein language models (pLMs), underscoring the growing impact of pLMs in tackling the complexities of disordered proteins and advancing ID prediction.
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