CAT-Site : predicting protein binding sites using a convolutional neural network

Hafner Petrovski, Žan (author), Hribar-Lee, Barbara (author), Bosnić, Zoran (author)

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.

• Language: English
• Keywords: protein binding site prediction, ligands, molecular docking, machine learning, convolutional neural network
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science ; FKKT - Faculty of Chemistry and Chemical Technology
• Publication status: Published
• Publication version: Version of Record
• Year: 2022
• Number of pages: 21 str.
• Numbering: Vol. 15, iss. 1, art. 119
• PID: 20.500.12556/RUL-154114
• UDC: 004.8:615
• ISSN on article: 1999-4923
• DOI: 10.3390/pharmaceutics15010119
• COBISS.SI-ID: 135845635
• Publication date in RUL: 25.01.2024

Record is a part of a journal

• Title: Pharmaceutics
• Shortened title: Pharmaceutics
• Publisher: MDPI
• ISSN: 1999-4923
• COBISS.SI-ID: 517949977

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: napovedovanje veznih mest proteinov, ligandi, molekulsko sidranje, strojno učenje, konvolucijska nevronska mreža