Boosting Protein-Protein Interaction Detection with AlphaFold Multimer and Transformers

Abstract

In 2021, DeepMind released AlphaFold 2, an AI-driven algorithm that revolutionized protein folding predictions by achieving error rates comparable to traditional experimental methods. Its remarkable performance has inspired researchers to extend its applications beyond its original purpose, leveraging AlphaFold 2 and its output features to tackle various unresolved biological challenges. In this paper, we discuss previous works that explored the repurposing of AlphaFold 2 for the detection of peptide- protein interactions (PPI). We propose a novel methodology where the outputs of a lightweight version of AlphaFold 2 Multimer are enhanced by a fine-tuned transformer encoder and benchmarked against classic machine learning algorithms. We study the performance of our approach on a curated version of Propedia, a peptide-protein dataset, and show that the boosting models significantly improve the detection of PPIs. Additionally, we provide HAPI (Hacky API), a freely available Python library designed to facilitate the seamless integration and use of AlphaFold 2 in computational workflows, further empowering the scientific community.