A structure-based model for the prediction of protein-RNA binding affinity

RNA
Chandran NithinRanjit Prasad Bahadur

Abstract

Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG val...Continue Reading

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Citations

Apr 27, 2021·Frontiers in Molecular Biosciences·Milan GerovacAlexandre Smirnov
Oct 5, 2021·Nucleic Acids Research·Kannan HariniM Michael Gromiha
Jul 13, 2021·Journal of Bacteriology·Amanda N D AdamsCarin K Vanderpool

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