Downstream Processing and Structure Confirmation of Chemoenzymatically Produced Macrocycles

大环，特别是修饰的环肽(CPS)是一种很有潜力的化合物，可以靶向人类疾病中的复杂靶点，如癌症和炎症等疾病中常见的蛋白质-蛋白质相互作用(PPI)。1这些靶标不能用传统的小分子化学来调节，但通过使用计算设计和合成和/或酶化学过程，CPS可以设计和制造出来抑制这些PPI。CP生产可以利用自动化并行进行，但工业上需要改进下游工艺，以隔离和进行结构确认，并为CP提供质量保证。因此，该项目的主要目的是使用计算和数据挖掘方法对过程每个阶段的色谱和光谱数据进行处理，以开发工具来预测CPS的物理化学参数，以帮助分离它们并自动确认它们的成功合成。目标是：1.)准确计算线性前体和CPS的物理化学参数(如logP和PSA)，并使用这些参数优化化合物分离工艺。2.)分析和理解发生在线性前体和CP中的独特的MS碎片，并利用这些来开发一种工具来预测最可能的碎片。3.)开发一种工具，可以分析来自线性前体和CP的MS碎片数据，并将其与预测片段进行比较，以确认合成成功。

Macrocycles, in particular modified cyclic peptides (CPs), are promising compounds that have great potential to hit complex targets in human disease such as protein-protein interactions (PPIs) which are common in diseases such as cancer and inflammation.1 These targets cannot be modulated using traditional small-molecule chemistry but by using computational design and synthetic and/or enzymatic chemistry processes CPs can be designed and made to inhibit these PPIs. CP production can be carried out in parallel using automation, but there is an industrial need to improve downstream processes to isolate and carry out structure confirmation and provide quality assurance for CPs. The main aim of this project is therefore to use computational and data mining methods on chromatographic and spectroscopic data at each stage of the process to develop tools to predict physicochemical parameters of the CPs to assist their isolation and to automatically confirm their successful synthesis. The objectives are: 1.) To accurately calculate physicochemical parameters (e.g. logP and PSA) of linear precursors and CPs and use these to optimise compound isolation processes. 2.) To analyse and understand unique MS fragmentations occurring in linear precursors and CPs and use these to develop a tool to predict the most likely fragmentations. 3.) To develop a tool that can analyse MS fragmentation data from linear precursors and CPs and compare this to the predicted fragments to confirm successful synthesis.