Variant interpretation remains a central challenge for precision medicine. Missense variants are particularly difficult to understand as they change only a single amino acid in a protein sequence yet can have large and varied effects on protein activity. Numerous tools have been developed to identify missense variants with putative disease consequences from protein sequence and structure. However, biological function arises through higher order interactions among proteins and molecules within cells. We therefore sought to capture information about the potential of missense mutations to perturb protein interaction networks by integrating protein structure and interaction data. We developed 16 network-based annotations for missense mutations that provide orthogonal information to features classically used to prioritize variants. We then evaluated them in the context of a proven machine-learning framework for variant effect prediction across multiple benchmark datasets to demonstrate their potential to improve variant classification. Interestingly, network features resulted in larger performance gains for classifying somatic mutations than for germline variants, possibly due to different constraints on what mutations are tolerated at the cellular versus organismal level. Our results suggest that modeling variant potential to perturb context-specific interactome networks is a fruitful strategy to advance in silico variant effect prediction.

变异解释仍然是精准医学的一个核心挑战。错义变体特别难以理解，因为它们仅改变蛋白质序列中的单个氨基酸，但却可以对蛋白质活性产生巨大且多样的影响。已经开发了许多工具来从蛋白质序列和结构中识别具有假定疾病后果的错义变异。然而，生物功能是通过细胞内蛋白质和分子之间的高级相互作用产生的。因此，我们试图通过整合蛋白质结构和相互作用数据来捕获有关错义突变扰乱蛋白质相互作用网络的潜力的信息。我们为错义突变开发了 16 个基于网络的注释，为传统上用于确定变体优先级的特征提供正交信息。然后，我们在经过验证的机器学习框架的背景下对它们进行了评估，用于跨多个基准数据集的变异效应预测，以证明它们改进变异分类的潜力。有趣的是，网络特征导致体细胞突变分类比种系变异获得更大的性能提升，这可能是由于细胞水平和有机体水平上耐受突变的限制不同。我们的结果表明，对扰乱特定上下文相互作用组网络的变异潜力进行建模是推进计算机变异效应预测的有效策略。