MuLAN: Mutation-driven Light Attention Networks for investigating protein-protein interactions from sequences

Abstract

Understanding how proteins interact and how mutations affect these interactions is crucial for unraveling the complexities of biological systems and their evolution. Mutations can significantly alter protein behavior, impacting stability, interactions, and activity, thereby affecting cellular functions and influencing disease development and treatment effectiveness. Experimental methods for examining protein interactions are often slow and costly, highlighting the need for efficient computational strategies. We present MuLAN, a groundbreaking deep learning method that leverages light attention networks and the power of pre-trained protein language models to infer protein interactions, predict binding affinity changes, and reconstruct mutational landscapes for proteins involved in binary interactions, starting from mutational changes and directly using sequence data only. Unlike previous methods that depend heavily on structural information, MuLANs sequence-based approach offers faster and more accessible predictions. This innovation allows for variations in predictions based on specific partners, opening new possibilities for understanding protein behavior through their sequences. The potential implications for disease research and drug development mark a significant step forward in the computational analysis of protein interactions.