USP-ddG: a unified structural paradigm with data efficacy and mixture-of-experts for predicting mutational effects on protein-protein interactions

作者：Yu G, Bi X, Zhao Q, Wang J · 发表于：Bioinformatics (Oxford, England) · 年份：2026 · DOI：10.1093/bioinformatics/btag249 · 被引用次数：49 · 研究领域：Proteins、Mutation、Computational Biology、Protein Interaction Mapping、Protein Binding、Thermodynamics、Deep Learning

MOTIVATION: Accurately estimating changes in binding free energy (ΔΔG) is critical for understanding protein-protein interactions (PPIs) and guiding rational protein design. Recent deep learning methods have achieved notable progress by pre-training on large-scale structural data. While some approaches explore structural flexibility through energy-based sampling or generative modeling, these strategies typically involve substantial computational cost and overlook data efficacy in terms of training data organization. RESULTS: We present USP-ddG, a unified structural paradigm for ΔΔG prediction built on a dual-channel architecture. The model incorporates three complementary components: (i) an inverse folding-based log-odds ratio, (ii) the empirical force field FoldX capturing side-chain packing energetics, and (iii) a geometric encoder that leverages Gaussian coordinate perturbation as a regularization strategy to improve robustness. To enhance representation capacity, we introduce a framework that integrates feed-forward network (FFN) and Mixture-of-Experts (MoE) to model domain-invariant and -specific features, respectively. We further propose CATH-guided Folding Ordering (CFO), a data efficacy strategy that organizes samples to mitigate catastrophic forgetting and data distribution bias. USP-ddG consistently outperforms existing state-of-the-art methods on the SKEMPI v2.0 benchmark, including the challenging hold-out CATH test set. It achieves superior accuracy on both sing...