Anand Mohit, Upadhyay Vikas, Maranas Costas D
The Pennsylvania State University, State College, Pennsylvania 16802, United States.
ACS Synth Biol. 2025 Mar 21;14(3):756-770. doi: 10.1021/acssynbio.4c00692. Epub 2025 Feb 14.
Chemo-enzymatic pathway design aims to combine the strengths of enzymatic with chemical synthesis to traverse biomolecular design space more efficiently. While chemical reactions often struggle with regioselectivity and stereoselectivity, enzymatic conversions often encounter limitations of low enzyme activity or availability. Optimally integrating both approaches provides an opportunity to identify efficient pathways beyond the capabilities of either modality. Recently, studies have shown the advantage of leveraging enzymatic steps into industrial-scale chemical processes, such as for the blood sugar regulator Sitagliptin (Merck) and the HIV protease inhibitor Darunavir (Prozomix). Designing optimal chemo-enzymatic pathways is a complex task. It requires navigating a high-dimensional search space of potential reactions that combine individual chemical and biochemical steps while at the same time minimizing transitions between chemical catalysis and bioreactions. Here, we introduce an algorithmic approach, minChemBio, that relies on solving a mixed-integer linear programming (MILP) problem by optimally searching through known chemical and enzymatic steps extracted from the United States Patent Office (USPTO) and MetaNetX databases, respectively. minChemBio allows for the minimization of transitions between chemical and biological reactions in the pathway, thus reducing the need for costly separation and purification steps required. minChemBio was benchmarked on three case studies involving the synthesis of 2-5-furandicarboxylic acid, terephthalate, and 3-hydroxybutyrate. Identified designs included both established literature pathways as well as unexplored ones which were compared against pathways identified by existing retrosynthetic tools. minChemBio fills a current gap in the space of pathway retrosynthesis tools by controlling and minimizing the transitions between chemical catalysis and biocatalytic steps. It is accessible to users through open-source code (https://github.com/maranasgroup/chemo-enz).
化学酶促途径设计旨在结合酶促反应与化学合成的优势,以更高效地探索生物分子设计空间。虽然化学反应常常在区域选择性和立体选择性方面面临挑战,但酶促转化往往受到低酶活性或可及性的限制。最佳地整合这两种方法为识别超越任何一种模式能力的高效途径提供了机会。最近,研究表明将酶促步骤应用于工业规模化学过程的优势,例如用于血糖调节剂西他列汀(默克公司)和HIV蛋白酶抑制剂达芦那韦(Prozomix)。设计最佳的化学酶促途径是一项复杂的任务。它需要在潜在反应的高维搜索空间中导航,这些反应结合了各个化学和生化步骤,同时还要尽量减少化学催化和生物反应之间的转换。在这里,我们介绍一种算法方法minChemBio,它通过分别从美国专利局(USPTO)和MetaNetX数据库中提取的已知化学和酶促步骤进行优化搜索,来解决混合整数线性规划(MILP)问题。minChemBio能够使途径中化学和生物反应之间的转换最小化,从而减少对所需昂贵分离和纯化步骤的需求。minChemBio在三个案例研究中进行了基准测试,这些研究涉及2-5-呋喃二甲酸、对苯二甲酸和3-羟基丁酸的合成。确定的设计包括已有的文献途径以及未探索的途径,并与现有逆合成工具确定的途径进行了比较。minChemBio通过控制和最小化化学催化和生物催化步骤之间的转换,填补了途径逆合成工具领域目前存在的空白。用户可以通过开源代码(https://github.com/maranasgroup/chemo-enz)访问它。