College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 South Puzhu Road, Jiangbei New District, Nanjing 211816, China.
Int J Mol Sci. 2024 Aug 22;25(16):9114. doi: 10.3390/ijms25169114.
The relationship between amino acid mutations and enzyme bioactivity is a significant challenge in modern bio-industrial applications. Despite many successful designs relying on complex correlations among mutations at different enzyme sites, the underlying mechanisms of these correlations still need to be explored. In this study, we introduced a revised version of the residual-contact network clique model to investigate the additive effect of double mutations based on the mutation occurrence topology, secondary structures, and physicochemical properties. The model was applied to a set of 182 double mutations reported in three extensively studied enzymes, and it successfully identified over 90% of additive double mutations and a majority of non-additive double mutations. The calculations revealed that the mutation additivity depends intensely on the studied mutation sites' topology and physicochemical properties. For example, double mutations on irregular secondary structure regions tend to be non-additive. Our method provides valuable tools for facilitating enzyme design and optimization. The code and relevant data are available at Github.
氨基酸突变与酶生物活性之间的关系是现代生物工业应用中的一个重大挑战。尽管许多成功的设计依赖于不同酶位点突变之间的复杂相关性,但这些相关性的潜在机制仍需要探索。在这项研究中,我们引入了改进后的残差接触网络团簇模型,基于突变发生拓扑、二级结构和物理化学性质来研究双突变的加性效应。该模型应用于三组广泛研究的酶中报告的 182 个双突变集,成功识别了超过 90%的加性双突变和大多数非加性双突变。计算结果表明,突变的加性强烈依赖于所研究的突变位点的拓扑和物理化学性质。例如,不规则二级结构区域的双突变往往是非加性的。我们的方法为促进酶设计和优化提供了有价值的工具。代码和相关数据可在 Github 上获得。