Apr 21, 2020

Deep learning model can predict water binding sites on the surface of proteins using limited-resolution data

BioRxiv : the Preprint Server for Biology
J. ZauchaGrzegorz M. Popowicz

Abstract

The surfaces of proteins are generally hydrophilic but there have been reports of sites that exhibit an exceptionally high affinity for individual water molecules. Not only do such molecules often fulfil critical biological functions, but also, they may alter the binding of newly designed drugs. In crystal structures, sites consistently occupied in each unit cell yield electron density clouds that represent water molecule presence. These are recorded in virtually all high-resolution structures obtained through X-ray diffraction. In this work, we utilized the wealth of data from the RCSB Protein Data Bank to train a residual deep learning model named 'hotWater' to identify sites on the surface of proteins that are most likely to bind water, the so-called water hot spots. The model can be used to score existing water molecules from a PDB file to provide their ranking according to the predicted binding strength or to scan the surface of a protein to determine the most likely water hot spots de novo. This is computationally much more efficient than currently used molecular dynamics simulations. Based on testing the model on three example proteins, which have been resolved using both high-resolution X-ray crystallography (providing ...Continue Reading

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