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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Acknowledgements
This research was supported in part by the National Science Foundation (2146027 and 2125218 to L.K.), Robert J. Mattauch Endowment funds to L.K., and National Natural Science Foundation of China (31970649 to G.H. and K.W.). Z.D. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme (778247) and support from ELIXIR Hungary (www.elixir‐hungary.org) and ELIXIR Implementations Studies. E.G. acknowledges support from the ÚNKP-22-1 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. J.G. acknowledges support from the National Sciences and Engineering Research Council of Canada.
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L.K. conceptualized and coordinated the study, and collected and analyzed data. G.H., K.W. S.G., B.Z. and L.K. contributed to the development and to the description of the flDPnn tool. N.M. and J.G. contributed to the development and the description of the MoRFchibi tool. G.E. and Z.D. contributed to the development and the description of the IUPred and ANCHOR tools. V.N.U. contributed to formulation and discussion of the examples. L.K., V.N.U. and Z.D. contributed to writing the article.
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Kurgan, L., Hu, G., Wang, K. et al. Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins. Nat Protoc 18, 3157–3172 (2023). https://doi.org/10.1038/s41596-023-00876-x
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DOI: https://doi.org/10.1038/s41596-023-00876-x
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