In silico methods evaluating the mutation effects of missense mutations are
providing an important approach for understanding mutations in personal genomes
and identifying disease-relevant biomarkers. However, existing methods,
including deep learning methods, heavily rely on sequence-aware information,
and do not fully leverage the potential of available 3D structural information.
In addition, these methods may exhibit an inability to predict mutations in
domains difficult to formulate sequence-based embeddings. In this study, we
introduce a novel rationale-guided graph neural network AlphaGMut to evaluate
mutation effects and to distinguish pathogenic mutations from neutral
mutations. We compute the alpha shapes of protein structures to obtain
atomic-resolution edge connectivities and map them to an accurate residue-level
graph representation. We then compute structural-, topological-, biophysical-,
and sequence properties of the mutation sites, which are assigned as node
attributes in the graph. These node attributes could effectively guide the
graph neural network to learn the difference between pathogenic and neutral
mutations using k-hop message passing with a short training period. We
demonstrate that AlphaGMut outperforms state-of-the-art methods, including
DeepMind's AlphaMissense, in many performance metrics. In addition, AlphaGMut
has the advantage of performing well in alignment-free settings, which provides
broader prediction coverage and better generalization compared to current
methods requiring deep sequence-aware information.

通过计算蛋白质结构的 α 形状，利用结构信息和序列属性来评估突变效应，区分有病原性突变和无病原性突变的基于图神经网络的 AlphaGMut 新方法在性能指标上优于 DeepMind 的 AlphaMissense 等现有方法。