SGPocket: A New Graph Convolutional Neural Network for Ligand-protein Binding Site Prediction

Title:SGPocket: A New Graph Convolutional Neural Network for Ligand-protein Binding Site Prediction

Volume: 31

Author(s): Kevin Crampon*, Cedric Bourrasset, Stephanie Baud and Luiz Angelo Steffenel

Affiliation:

• Eviden, 38130 Echirolles, France
• UMR CNRS/URCA 7369 MEDyC, Université de Reims Champagne-Ardenne, 51687 Reims, France
• LICIIS, Université de Reims Champagne-Ardenne, 51687 Reims, France

Keywords: Molecular Docking, Artificial Intelligence, Deep Learning, Binding site prediction, Protein-Ligand, Graph Neural Network

Abstract: Background: Drug research is a long process, taking more than 10 years and requiring considerable financial resources. Therefore, researchers and industrials aim to reduce time and cost. Thus, they use computational simulations like molecular docking to explore huge databases of compounds and extract the most promising ones for further tests. Structure-based molecular docking is a complex process mixing surface exploration and energy computation to find the minimal free energy of binding corresponding to the best interaction location.

Objective: Our work is developed in the ligand-protein context, where ligands are small compounds like drugs. In most cases, no information is known about where on the protein surface the ligand will bind. Thus, the whole protein surface must be explored, which takes a huge amount of time.

Methods: We have developed SGPocket (meaning Spherical Graph Pocket), a binding site prediction method. Our method allows us to reduce the explored protein surface using deep learning without any information about a ligand. SGPocket uses the spherical graph convolutional operator working on a spherical relative positioning of amino acids in the protein. Then, a final step of clustering extracts the binding sites.

Results: Tested and compared (with well-known binding site prediction methods) on a hand-made dataset, our method performed well and can reduce the docking computing time.

Conclusion: Thus, SGPocket allows the reduction of the exploration surface in the molecular docking process by restricting the simulation only to the site(s) predicted to be interesting.

Cite this article as:

Crampon Kevin*, Bourrasset Cedric, Baud Stephanie and Steffenel Angelo Luiz, SGPocket: A New Graph Convolutional Neural Network for Ligand-protein Binding Site Prediction, Current Medicinal Chemistry 2024; 31 () . https://dx.doi.org/10.2174/0109298673289137240304165758

DOI https://dx.doi.org/10.2174/0109298673289137240304165758	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X