CAT-Site

predicting protein binding sites using a convolutional neural network

Žan Hafner Petrovski (Author), Barbara Hribar (Author), Zoran Bosnić (Author)

Abstract

Identifying binding sites on the protein surface is an important part of computer-assisted drug design processes. Reliable prediction of binding sites not only assists with docking algorithms, but it can also explain the possible side-effects of a potential drug as well as its efficiency. In this work, we propose a novel workflow for predicting possible binding sites of a ligand on a protein surface. We use proteins from the PDBbind and sc-PDB databases, from which we combine available ligand information for similar proteins using all the possible ligands rather than only a special sub-selection to generalize the work of existing research. After performing protein clustering and merging of ligands of similar proteins, we use a three-dimensional convolutional neural network that takes into account the spatial structure of a protein. Lastly, we combine ligandability predictions for points on protein surfaces into joint binding sites. Analysis of our model’s performance shows that its achieved sensitivity is 0.829, specificity is 0.98, and F$_1$ score is 0.517, and that for 54% of larger and pharmacologically relevant binding sites, the distance between their real and predicted centers amounts to less than 4 Å.

Keywords

napovedovanje veznih mest proteinov;ligandi;molekulsko sidranje;strojno učenje;konvolucijska nevronska mreža;protein binding site prediction;ligands;molecular docking;machine learning;convolutional neural network;

Data

Language:	English
Year of publishing:	2023
Typology:	1.01 - Original Scientific Article
Organization:	UL FRI - Faculty of Computer and Information Science
UDC:	004.8:615
COBISS:	135845635
ISSN:	1999-4923
Views:	53
Downloads:	7
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	napovedovanje veznih mest proteinov;ligandi;molekulsko sidranje;strojno učenje;konvolucijska nevronska mreža;
Type (COBISS):	Article
Pages:	str. 1-21
Volume:	ǂVol. ǂ15
Issue:	ǂiss. ǂ1
Chronology:	Jan. 2023
DOI:	10.3390/pharmaceutics15010119
ID:	22635409