Department of Biochemistry and Molecular Biology, State University of New York (SUNY) Upstate Medical University, 4261 Weiskotten Hall, Syracuse, NY, 13210, USA.
Schulze Center for Novel Therapeutics, Division of Oncology Research, Mayo Clinic, Rochester, MN, USA.
Sci Rep. 2022 Nov 2;12(1):18487. doi: 10.1038/s41598-022-23229-2.
Using exome sequencing for biomarker discovery and precision medicine requires connecting nucleotide-level variation with functional changes in encoded proteins. However, for functionally annotating the thousands of cancer-associated missense mutations, or variants of uncertain significance (VUS), purifying variant proteins for biochemical and functional analysis is cost-prohibitive and inefficient. We describe parallel functional annotation (PFA) of large numbers of VUS using small cultures and crude extracts in 96-well plates. Using members of a histone methyltransferase family, we demonstrate high-throughput structural and functional annotation of cancer-associated mutations. By combining functional annotation of paralogs, we discovered two phylogenetic and clustering parameters that improve the accuracy of sequence-based functional predictions to over 90%. Our results demonstrate the value of PFA for defining oncogenic/tumor suppressor functions of histone methyltransferases as well as enhancing the accuracy of sequence-based algorithms in predicting the effects of cancer-associated mutations.
使用外显子组测序进行生物标志物发现和精准医学需要将核苷酸水平的变异与编码蛋白的功能变化联系起来。然而，对于功能注释数千个与癌症相关的错义突变或意义不明的变异（VUS），纯化变异蛋白进行生化和功能分析既昂贵又低效。我们描述了使用小培养物和 96 孔板中的粗提取物对大量 VUS 进行平行功能注释（PFA）。我们使用组蛋白甲基转移酶家族的成员，展示了对癌症相关突变的高通量结构和功能注释。通过组合平行物的功能注释，我们发现了两个进化和聚类参数，可将基于序列的功能预测的准确性提高到 90%以上。我们的结果证明了 PFA 在定义组蛋白甲基转移酶的致癌/肿瘤抑制功能以及提高基于序列的算法预测癌症相关突变影响的准确性方面的价值。
