Improving protein–protein interaction modulator predictions via knowledge-fused language models

Abstract

Protein-protein interactions (PPIs) play key roles in numerous biological processes and their dysregulation can lead to various human diseases. Modulating these interactions with small molecule PPI modulators has emerged as a promising strategy for treating such diseases. However, current computational approaches for screening PPI modulators often fail to integrate biomolecular expertise and lack the elucidation of interaction mechanisms. Here, we propose a knowledge-fused modulator-PPI interaction prediction method (KFPPIMI) to alleviate these issues. KFPPIMI constructs separate representation models for modulators and proteins, each of which integrates external knowledge from textual and graph-based data sources via a language modeling framework. The fusion of the nuanced expression of natural language with the structural attributes of biomolecules provides KFPPIMI with a holistic view of modulator-PPI interactions. Extensive experiments are conducted to evaluate the effectiveness of KFPPIMI and its individual components. The results show that KFPPIMI outperforms existing methods in different scenarios. Moreover, the modulator and protein representation model can be successfully applied to their respective downstream tasks with comparable performance.