Mutational effects predictions continue to improve in accuracy as advanced artificial intelligence (AI) algorithms are trained on exhaustive experimental data. The next natural questions to ask are if it is now possible to gain insights into which attribute of the mutation contributes how much to the mutational effects, and if one can develop universal rules for mapping the descriptors to mutational effects. In this work, we mainly address the former aspect using a framework of interpretable AI. Relations between the physico-chemical descriptors and their contributions to the mutational effects are extracted by analyzing the data on 29,832 variants from 8 systematic deep-mutational scan studies. It is found that the intuitive dependences of fitness and solubility on the distance of the amino acid from active site could be extracted and quantified. The dependence of the mutational effect contributions on the number of contacts an amino acid has or the BLOSUM score descriptor of the change showed universal trends. Our attempts in the present work to explain the quantitative differences in the dependence on conservation and SASA across proteins were not successful. The work nevertheless brings transparency into the predictions, development of rules, and will hopefully lead to uncovering the universalities among these rules.

中文翻译：

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弄清每个突变的每个描述特征对其功能作用的贡献

随着先进的人工智能（AI）算法针对详尽的实验数据进行训练，变异效应预测的准确性将继续提高。接下来要问的自然问题是，现在是否有可能洞悉突变的哪个属性对突变效应有多大贡献，以及是否可以开发出将描述符映射到突变效应的通用规则。在这项工作中，我们主要使用可解释的AI框架解决前一个方面。通过分析来自8个系统的深层突变扫描研究的29,832个变体的数据，来提取理化描述符及其对突变效应的贡献之间的关系。发现可以提取和量化适合度和溶解度对氨基酸与活性位点的距离的直观依赖性。突变效应贡献对氨基酸接触次数或该变化的BLOSUM得分描述符的依赖性呈普遍趋势。我们在本工作中试图解释蛋白质之间对保守性和SASA依赖性的定量差异并不成功。然而，这项工作为规则的预测，规则的制定带来了透明度，并有望导致发现这些规则之间的普遍性。