Abstract
Deep learning methods have played an increasingly pivotal role in advancing side-chain packing and mutation effect prediction (ΔΔG) for protein complexes. Although these two tasks are inherently closely related, they are typically treated separately in practice. Furthermore, the lack of effective post-processing in most approaches results in sub-optimal refinement of generated conformations, limiting the plausibility of the predicted conformations. In this study, we introduce an integrated framework, PackPPI, which employs a diffusion model and a proximal optimization algorithm to improve side-chain prediction for protein complexes while using learned representations to predict ΔΔG. The results demonstrate that PackPPI achieved the lowest atom RMSD (0.9822) on the CASP15 dataset. The proximal optimization algorithm effectively reduces spatial clashes between side-chain atoms while maintaining a low-energy landscape. Furthermore, PackPPI achieves state-of-the-art performance in predicting binding affinity changes induced by multi-point mutations on the SKEMPI v2.0 dataset. These findings underscore the potential of PackPPI as a robust and versatile computational tool for protein design and engineering. The implementation of PackPPI is available at https://github.com/Jackz915/PackPPI.