Proceedings of the National Academy of Sciences · 2020 · 16 citations · 56 references
Biophysical ModelingMolecular BiologyHidden Dynamic SignaturesKinase Specificity MotifsProtein FoldingProteomicsBiophysicsConformational EquilibriumProtein ModelingProtein Structure PredictionBiochemical InteractionBiomolecular InteractionProtein BioinformaticsBiophysical AspectStructural BiologyProtein PhosphorylationSignal TransductionConformational FluctuationsNatural SciencesComputational BiologyCellular BiochemistrySystems BiologyMedicine
Phosphorylation sites are hyperabundant in the eukaryotic disordered proteome, suggesting that conformational fluctuations play a major role in determining to what extent a kinase interacts with a particular substrate. In biophysical terms, substrate selectivity may be determined not just by the structural-chemical complementarity between the kinase and its protein substrates but also by the free energy difference between the conformational ensembles that are, or are not, recognized by the kinase. To test this hypothesis, we developed a statistical-thermodynamics-based informatics framework, which allows us to probe for the contribution of equilibrium fluctuations to phosphorylation, as evaluated by the ability to predict Ser/Thr/Tyr phosphorylation sites in the disordered proteome. Essential to this framework is a decomposition of substrate sequence information into two types: vertical information encoding conserved kinase specificity motifs and horizontal information encoding substrate conformational equilibrium that is embedded, but often not apparent, within position-specific conservation patterns. We find not only that conformational fluctuations play a major role but also that they are the dominant contribution to substrate selectivity. In fact, the main substrate classifier distinguishing selectivity is the magnitude of change in local compaction of the disordered chain upon phosphorylation of these mostly singly phosphorylated sites. In addition to providing fundamental insights into the consequences of phosphorylation across the proteome, our approach provides a statistical-thermodynamic strategy for partitioning any sequence-based search into contributions from structural-chemical complementarity and those from changes in conformational equilibrium.
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.3K citations · Full text
Amino Acid Difference Formula to Help Explain Protein Evolution
Richard Grantham · Science · 1974 · 2.4K citations
Explain Protein Evolution, Protein Chemistry, Biochemistry +12