International Journal of Molecular Sciences · 2022 · 170 citations · 71 references
Life SciencesBioinformaticsBiologyProtein Structure PredictionStructural BioinformaticsStructural BiologyIntrinsic DisorderIntrinsically Disordered ProteinsProtein DynamicsProtein StructureProtein Structure ModelingResidue-residue Distance PredictionComputational Modeling Of Disordered ProteinsDisordered Protein Structure PredictionDisordered Region ModelingIntrinsic Disorder AnalysisIntrinsic Disorder PredictionIntrinsically Disordered RegionsProtein Disorder PredictionProtein Disorder Propensity
The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs).
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
Highly accurate protein structure prediction with AlphaFold
John Jumper, Alexander Pritzel, Tim Green et al. · Nature · 2021 · 45.2K citations · Full text