Accurate prediction of changes in protein-protein binding affinity influenced by mutations (i.e.,$\Delta\Delta G)$is essential for understanding the structure and function of proteins and elucidating the underlying mechanisms of complex diseases. We introduce a novel multi-scale feature fusion network for protein-protein$\Delta\Delta G$prediction, aiming to reduce the dependency of previous methods on intricate biological features and expert-driven knowledge, and demonstrate its effectiveness in the challenging and important domain of protein data. Specifically, we employ multi-scale modeling of protein complexes, and introduce self-attention mechanisms and various extraction modules to comprehensively capture the features. The proposed method effectively learns the complete biological regulation of complexes and incorporates interactions between amino acids. Extensive experiments on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art methods for$\Delta\Delta G$prediction while also providing excellent performance in the domain of membrane protein design.

Cloud networks continuously upgrade softwarized virtual network devices (VNDs) to meet evolving tenant demands. However, such upgrades may result in unexpected failures. An intuitive idea to prevent upgrade failures is to resolve all compatibility issues before deployment, but it is impractical to replicate all deployed VND cases and test them with lots of replayed real traffic for the VND developers. As a result, the operations team takes upgrade risk to test upgrades by gradually deploying them. Although careful upgrade schedule planning is the most common method to minimize upgrade risk, to the best of our knowledge, no VND upgrade schedule planning scheme has been adequately studied for large-scale cloud networks. To fill this gap, we propose CloudPlanner, the first VND upgrade schedule planning scheme aiming to minimize the VND upgrade risk for large-scale cloud networks. CloudPlanner prioritizes upgrading VNDs that are more likely to trigger failures based on expert knowledge and historical failure-trigger VND properties and limits the number of tenants associated with simultaneously upgraded VNDs. We also propose a heuristic solver which can quickly and greedily plan schedules. Using real-world data from production environments, we demonstrate the benefits of CloudPlanner through extensive experiments.