ReviewNature protocols2023
Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins.
Review in Nature protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 40 citations in OpenAlex.
- Uncovering viral protein acquisition events and human-specific folds with pairwise comparisons of predicted protein structures.Molecular biology and evolution · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- Computational Characterization of Pathogenic LMNA Missense Variants: Structural Instability, Altered Binding, and Conformational Dynamics.Human mutation · 2026Article
- Prediction of Intrinsically Disordered Regions That Bind RNA with DisoRDPbind.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Structural characterization of plum pox virus by cryo-electron microscopy.Archives of virology · 2025Article
- Programming cellular condensates for living materials using an intrinsically disordered protein display platform.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Comparative assessment of binding residue predictions in intrinsically disordered regions.Protein science : a publication of the Protein Society · 2025Article
- Assessing variant effect predictors and disease mechanisms in intrinsically disordered proteins.PLoS computational biology · 2025Article
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- LoRA-DR-suite: adapted embeddings predict intrinsic and soft disorder from protein sequences.Bioinformatics (Oxford, England) · 2025Article
- Proteomimetic Strategy for the Modulation of Intrinsically Disordered Protein MYC.Journal of the American Chemical Society · 2025Article
- Mechanisms underlining Kelp (Saccharina japonica) adaptation to relative high seawater temperature.BMC genomics · 2025Article
- Evaluation of predictions of disordered binding regions in the CAID2 experiment.Computational and structural biotechnology journal · 2025Review
- Comprehensive assessment of AlphaFold's predictions of secondary structure and solvent accessibility at the amino acid-level in eukaryotic, bacterial and archaeal proteins.Computational and structural biotechnology journal · 2025Article
- Navigating the unstructured by evaluating alphafold's efficacy in predicting missing residues and structural disorder in proteins.PloS one · 2025Article
- Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Disordered Linear Interacting Peptides with CLIP.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Intrinsic Disorder Functions with DEPICTER2.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses.Computational and structural biotechnology journal · 2024Article
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Authors and funding
10 authors at 5 institutions in 5 countries.
Funding
Abstract
Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.
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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.