We report the Structure-based Pathogenicity Relationship Identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

中文翻译：

---

SPRI：基于结构的致病性关系标识符，用于预测单个错义变异的影响和发现高阶癌症易感性突变簇

我们报告了基于结构的致病性关系标识符 (SPRI)，这是一种用于准确评估错义单突变的病理效应和预测突变簇的高阶空间组织单元的新型计算工具。SPRI 可以有效地提取确定蛋白质结构中编码的致病性的特性，并且可以识别与孟德尔疾病相关的种系起源的有害错义突变，以及与癌症驱动因素相关的体细胞起源的突变。它在预测有害突变方面优于其他方法。此外，SPRI 可以发现有害突变的空间组织致病性高阶空间簇 (patHOS)，包括那些低复发的突变，并可用于发现候选癌症驱动基因和驱动突变。