Abstract

In silico approaches for predicting the functional impact of missense mutations are critical for interpreting personal genomes and identifying disease-related biomarkers. Existing methods largely rely on sequence-based information or intuitive structural features, but often overlook the complex biophysical patterns encoded in protein 3D structures. Here, we present G-SPRI, a multilevel framework built on a novel alpha shape protein graph that accurately captures residue connectivity from atomic-resolution geometry and enables precise message passing around mutation sites. Using this graph representation, G-SPRI integrates wild-type structural properties and mutation-specific perturbation signals derived from the Protein Data Bank (PDB) universe to support graph-based learning for distinguishing pathogenic from benign missense variants. G-SPRI performs strongly across multiple key tasks. On the binary prediction benchmark, G-SPRI delivers improved pathogenicity prediction for individual mutations. By integrating mutation recurrence across the pan-cancer cohort, G-SPRI recovers more known cancer driver genes than state-of-the-art methods from more than 2.3 million mutations. Furthermore, by jointly quantifying site-specific pathogenicity and co-clustering influence within higher order structural organization units, G-SPRI provides comprehensive evidence for pinpointing likely driver mutations and structurally susceptible regions within disease genes.