Ranking protein-protein models with large language models and graph neural networks

TL;DR

This paper introduces DeepRank-GNN-esm, a graph-based deep learning method that leverages protein language models and graph neural networks to effectively rank protein-protein interaction models, aiding in the identification of near-native conformations.

Contribution

The paper presents a novel application of graph neural networks combined with protein language models for ranking PPI models, improving model selection accuracy.

Findings

DeepRank-GNN-esm effectively ranks PPI models.

The method leverages protein language models and graph neural networks.

Open-source implementation available at GitHub.

Abstract

Protein-protein interactions (PPIs) are associated with various diseases, including cancer, infections, and neurodegenerative disorders. Obtaining three-dimensional structural information on these PPIs serves as a foundation to interfere with those or to guide drug design. Various strategies can be followed to model those complexes, all typically resulting in a large number of models. A challenging step in this process is the identification of good models (near-native PPI conformations) from the large pool of generated models. To address this challenge, we previously developed DeepRank-GNN-esm, a graph-based deep learning algorithm for ranking modelled PPI structures harnessing the power of protein language models. Here, we detail the use of our software with examples. DeepRank-GNN-esm is freely available at https://github.com/haddocking/DeepRank-GNN-esm