Genome Informatics Laboratory, Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
College of Public Health, Medical and Veterinary Science, James Cook University, Cairns, QLD, Australia.
Bioinformatics. 2022 Sep 2;38(17):4220-4222. doi: 10.1093/bioinformatics/btac465.
Missense mutations that change protein stability are strongly associated with human genetic disease. With the recent availability of predicted structures for all human proteins generated using the AlphaFold2 prediction model, genome-wide assessment of the stability effects of genetic variation can, for the first time, be easily performed. This facilitates the interrogation of personal genetic variation for potentially pathogenic effects through the application of stability metrics. Here, we present a novel tool to prioritize variants predicted to cause strong instability in essential proteins. We show that by filtering by ΔΔG values and then prioritizing by StabilitySort Z-scores, we are able to more accurately discriminate pathogenic, protein-destabilizing mutations from population variation, compared with other mutation effect predictors.
StabilitySort is available as a web service (https://www.stabilitysort.org), as a data download for integration with other tools (https://www.stabilitysort.org/download) or can be deployed as a standalone system from source code (https://gitlab.com/baaron/StabilitySort).
Supplementary data are available at Bioinformatics online.
改变蛋白质稳定性的错义突变与人类遗传疾病密切相关。随着最近可利用的所有使用 AlphaFold2 预测模型生成的人类蛋白质的预测结构，首次可以轻松地对遗传变异的稳定性影响进行全基因组评估。这通过应用稳定性指标促进了对个人遗传变异的潜在致病性影响的探究。在这里，我们提出了一种新的工具，用于优先考虑预测会导致必需蛋白质强烈不稳定性的变体。我们表明，通过根据 ΔΔG 值进行过滤，然后根据 StabilitySort Z 分数进行优先级排序，与其他突变效应预测器相比，我们能够更准确地区分致病性、导致蛋白质不稳定的突变与群体变异。
StabilitySort 可作为一个网络服务（https://www.stabilitysort.org）使用，也可作为数据下载，以便与其他工具集成（https://www.stabilitysort.org/download），或者可以从源代码（https://gitlab.com/baaron/StabilitySort）部署为独立系统。
补充数据可在“Bioinformatics”在线获取。
