CAT-Site

predicting protein binding sites using a convolutional neural network

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

Povzetek

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.

Ključne besede

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

Podatki

Jezik:	Angleški jezik
Leto izida:	2023
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FRI - Fakulteta za računalništvo in informatiko
UDK:	004.8:615
COBISS:	135845635
ISSN:	1999-4923
Št. ogledov:	53
Št. prenosov:	7
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	napovedovanje veznih mest proteinov;ligandi;molekulsko sidranje;strojno učenje;konvolucijska nevronska mreža;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 1-21
Letnik:	ǂVol. ǂ15
Zvezek:	ǂiss. ǂ1
Čas izdaje:	Jan. 2023
DOI:	10.3390/pharmaceutics15010119
ID:	22635409