ArticleScientific reports2020
ODiNPred: comprehensive prediction of protein order and disorder.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed.
- A dynamic displacement mechanism drives protein import into mitochondria.bioRxiv : the preprint server for biology · 2026Article
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- PEGASUS: Prediction of MD-derived protein flexibility from sequence.Protein science : a publication of the Protein Society · 2025Article
- Specificity in clustering of gene-specific transcription factors is encoded in the genome.Nucleic acids research · 2025Article
- Assignment of the N-terminal domain of mouse cGAS.Biomolecular NMR assignments · 2025Article
- Prediction of secreted uncharacterized protein structures from Beauveria bassiana ARSEF 2860 unravels novel toxins-like families.Scientific reports · 2025Article
- A computational Evo-Devo approach for elucidating the roles of PLETHORA transcription factors in regulating root development.PloS one · 2025Article
- Human introns contain conserved tissue-specific cryptic poison exons.NAR genomics and bioinformatics · 2024Article
- Assessing the role of evolutionary information for enhancing protein language model embeddings.Scientific reports · 2024Article
- Fine-tuning protein language models boosts predictions across diverse tasks.Nature communications · 2024Article
- Transient Structural Properties of the Rho GDP-Dissociation Inhibitor.Angewandte Chemie (International ed. in English) · 2024Article
- Article
- Protein feature engineering framework for AMPylation site prediction.Scientific reports · 2024Article
- Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- In Silico Evaluation, Phylogenetic Analysis, and Structural Modeling of the Class II Hydrophobin Family from Different Fungal Phytopathogens.Microorganisms · 2023Article
- ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers.NAR genomics and bioinformatics · 2023Article
- The influence of random-coil chemical shifts on the assessment of structural propensities in folded proteins and IDPs.RSC advances · 2023Review
- Structural evolution of Delta lineage of SARS-CoV-2.International journal of biological macromolecules · 2023Article
- Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins.Computational and structural biotechnology journal · 2023Article
- LambdaPP: Fast and accessible protein-specific phenotype predictions.Protein science : a publication of the Protein Society · 2023Article
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3 authors.
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Abstract
Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however, notoriously difficult to predict the degree of local flexibility within structured domains and the presence and nuances of localized rigidity within intrinsically disordered regions. To identify such instances, we used the CheZOD database, which encompasses accurate, balanced, and continuous-valued quantification of protein (dis)order at amino acid resolution based on NMR chemical shifts. To computationally forecast the spectrum of protein disorder in the most comprehensive manner possible, we constructed the sequence-based protein order/disorder predictor ODiNPred, trained on an expanded version of CheZOD. ODiNPred applies a deep neural network comprising 157 unique sequence features to 1325 protein sequences together with the experimental NMR chemical shift data. Cross-validation for 117 protein sequences shows that ODiNPred better predicts the continuous variation in order along the protein sequence, suggesting that contemporary predictors are limited by the quality of training data. The inclusion of evolutionary features reduces the performance gap between ODiNPred and its peers, but analysis shows that it retains greater accuracy for the more challenging prediction of intermediate disorder.
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