SENSE-PPI reconstructs protein-protein interactions of various complexities, within, across, and between species, with sequence-based evolutionary scale modeling and deep learning

Abstract

Ab initio computational reconstructions of protein-protein interaction networks will bring invaluable information on cellular systems leading to the discovery of potential molecular interactions and to the unraveling of new biological mechanisms. Crossed with large-scale experimental data, they will reduce the complexity of the set of proteins involved in a biological mechanism that should be experimentally tested. The high confidence of the network is a prerequisite of these advancements. We combine advances in protein language models and recurrent neural networks to recover interaction signals in datasets of biological sequences with increasing complexity. SENSE-PPI (Sequence-based EvolutioNary ScalE Protein-Protein Interaction) is a deep learning model that predicts physical interactions between pairs of proteins using only their amino acid sequences. SENSE-PPI is highly accurate, demands limited training, keeps high accuracy levels when trained on one species and validated on another, possibly a non-model organism, and it can predict protein interactions between human and viruses. It determines protein partners that might have known structures or be intrinsically disordered, it reconstructs ab initio protein interaction networks by discriminating partners among tens of thousands of proteins and identifies partners between sets of proteins sharing similar functions, like enzymes vs inhibitors. SENSE-PPI is very fast and can test a few hundred proteins against themselves in less than a few minutes. SENSE-PPI learns better than existing deep learning methods addressing the problem of PPI reconstruction and it generalizes to different related problems, such as human-viral and intrinsically disordered protein interaction. A comparative analysis of the sequence embeddings generated by different versions of ESM2 in SENSE-PPI leads to recognizing the importance of a large number of parameters in protein language models used for PPI network reconstruction. The user will have access to two versions of SENSE-PPI one favoring precision and the other recall, as well as to a newly designed dataset of almost 1 million positive and negative human interactions to use for testing and validation.