Prot-ΔΔG: Prediction of protein-protein binding affinity changes upon mutations with pre-training strategies

Protein-protein interactions are essential for diverse biological activities, but amino acid mutations can disrupt these interactions, leading to dysfunction and disease. Mutation impacts can be quantified via change in protein-protein binding affinity (ΔΔG) before and after mutation. Accurate prediction of ΔΔG is critical for understanding disease mechanisms, guiding drug discovery, and advancing protein engineering. Existing computational methods often exhibit reduced accuracy in the absence of high-resolution protein structural data, failing to fully capture sequence-embedded patterns and evolutionary information. To address this limitation, we introduce Prot-ΔΔG, a purely sequence-based deep learning framework that integrates large-scale pre-trained protein language models with a BiGRU-DBRNN encoder. By leveraging solely on wild-type and mutant amino acid sequences, Prot-ΔΔG effectively captures evolutionary and context-dependent patterns without relying on structural inputs. Comprehensive experiments demonstrate that Prot-ΔΔG achieves competitive performance across single-point, mixed, and multi-point mutation prediction scenarios, with the largest improvement observed in protein-level blind testing. This sequence-based approach eliminates the dependency on protein structural information, thereby broadening its applicability, especially in cases where protein structures are unavailable or unreliable.