AI-driven approaches to developing a 'nylonase' enzyme for recycling waste plastics

要实现循环经济，就需要在开发回收废塑料的节能新技术方面取得迅速进展。在这个项目中，我们采用一种新的方法来开发尼龙降解酶，使用计算生物学和定向进化的结合。使用主要的蛋白质序列数据，我们将建立和筛选3D计算模型（AlphFold2）已知的和以前未开发的水解酶适当的底物结合位点和催化架构。尼龙低聚物与这些模型的对接将允许识别新酶的最佳起始点，所述新酶结合尼龙并稳定酰胺键断裂的过渡态。分子动力学模拟将有助于评估底物结合和活性位点中关键相互作用的空间排列，以促进有效的催化。预测的酶将在模型聚酰胺和消费后尼龙上表达和测试，以提供模型的验证和进一步完善。成功的变体将鉴定可以进一步突变的关键残基，以找到突变的最佳组合，从而提供所需的“尼龙酶”活性。

Moving towards a circular economy requires rapid progress to develop energy efficient new technologies for recycling waste plastics. In this project we take a new approach to develop enzymes for nylon degradation, using a combination of computational biology and directed evolution. Using primary protein sequence data we will build and screen 3D-computational models (AlphFold2) of known and previously unexplored hydrolase enzymes for appropriate substrate binding sites and catalytic architecture. Docking of nylon oligomers with these models will allow identification of the best starting points for new enzymes that bind nylon and stabilise the transition state for amide bond cleavage. Molecular dynamics simulations will help assess substrate binding and the spacial arrangement for key interactions in the active site to facilitate efficient catalysis. The predicted enzymes will be expressed and tested on model polyamides and on post-consumer nylon to provide validation and further refinement of the models. Successful variants will identify key residues that may be further mutated to find optimal combinations of mutations to provide the required 'nylonase' activity.