Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Lukasz Kurgan, Gang Hu, Kui Wang, Sina Ghadermarzi, Bi Zhao, Nawar Malhis, Gábor Erdős, Jörg Gsponer, Vladimir N. Uversky, Zsuzsanna Dosztányi

Published: 2023-09-21 DOI: 10.1038/s41596-023-00876-x

Intrinsic Disorder

Protein Prediction

Computational Tools

flDPnn

IUPred

AI 解读

Extraction of nuclei from archived postmortem tissues for single-nucleus sequencing applications

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Longitudinal interrogation of sympathetic neural circuits and hemodynamics in preclinical models

Experimental modulation of physiological force application on leg joint neurons in intactDrosophila melanogaster

Combining surface-accessible Ag and Au colloidal nanomaterials with SERS for in situ analysis of molecule–metal interactions in complex solution environments

Generation of mouse–human chimeric embryos

Labeling and tracking of immune cells in ex vivo human skin

Large-scale preparation of fluorescence multiplex host cell reactivation (FM-HCR) reporters

Mutate-and-chemical-shift-fingerprint (MCSF) to characterize excited states in RNA using NMR spectroscopy

Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing

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References

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Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

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