Deep learning models for unbiased sequence-based PPI prediction plateau at an accuracy of 0.65.

ABSTRACT:

Motivation

As most proteins interact with other proteins to perform their respective functions, methods to computationally predict these interactions have been developed. However, flawed evaluation schemes and data leakage in test sets have obscured the fact that sequence-based protein-protein interaction (PPI) prediction is still an open problem. Recently, methods achieving better-than-random performance on leakage-reduced PPI data have been proposed.

Results

Here, we show that the use of ESM-2 protein embeddings explains this performance gain irrespective of model architecture. We compared the performance of models with varying complexity, per-protein, and per-token embeddings, as well as the influence of self- or cross-attention, where all models plateaued at an accuracy

SUBMITTER: Reim T 

PROVIDER: S-EPMC12261406 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature