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

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Deep learning model can predict water binding sites on the surface of proteins using limited-resolution data Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Deep learning model can predict water binding sites on the surface of proteins using limited-resolution data
Creator	Popowicz, Grzegorz Sattler, Michael Softley, Charlotte Zaucha, Jan
source	BioRxiv
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 accurate positions of trapped waters) as well as low-resolution X-ray diffraction, NMR or CryoEM (where structure refinement does not yield water positions), we were able to show that the hotWater method is able to recover in the “water-free” structures many water binding sites known from the high-resolution structures. A blind test on a newly solved protein structure with waters removed from the PDB also showed good prediction of the crystal water positions. This was compared to two known algorithms that use electron density and was shown to have higher recall at resolutions >2.6 Å. We also show that the algorithm can be applied to novel proteins such as the RNA polymerase complex from SARS-CoV-2, which could be of use in drug discovery. The hotWater model is freely available at (https://pypi.org/project/hotWater/).
has issue date	2020-04-21(xsd:dateTime)
bibo:doi	10.1101/2020.04.20.050393
has license	biorxiv
sha1sum (hex)	4134b0773eaa08b2dad7b7fe735b395840c385f4
schema:url	https://doi.org/10.1101/2020.04.20.050393
resource representing a document's title	Deep learning model can predict water binding sites on the surface of proteins using limited-resolution data
schema:publication	bioRxiv
resource representing a document's body	covid:4134b0773eaa08b2dad7b7fe735b395840c385f4#body_text
is schema:about of	named entity 'unit cell' named entity 'efficient' named entity 'water molecules' named entity 'presence' named entity 'determine' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software