Downstream Processing and Structure Confirmation of Chemoenzymatically Produced Macrocycles

化学酶法产生的大环化合物的下游加工和结构确认

• 批准号: 2617528
• 金额: --
• 依托单位: University of Aberdeen
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2617528
• 关键词: Downstream; Processing; Structure; Confirmation; Chemoenzymatically

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.

大环化合物，特别是修饰的环肽（CP），是有前途的化合物，具有很大的潜力击中人类疾病中的复杂靶点，例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这些靶点不能使用传统的小分子化学进行调节，而是通过使用计算设计和合成和/或合成的药物来调节。或酶化学方法，CP可以被设计和制备为抑制这些PPI。CP生产可以使用自动化并行进行，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在工艺的每个阶段使用色谱和光谱数据的计算和数据挖掘方法，开发预测氯化石蜡理化参数的工具，以帮助其分离并自动确认其成功合成。目标是：1.）准确计算线性前体和CP的理化参数（例如logP和PSA），并使用这些参数优化化合物分离工艺。2.）的情况。分析和了解线性前体和CP中发生的独特MS碎片化，并使用这些碎片化来开发预测最可能的碎片化的工具。3.）第三章开发一种工具，可以分析线性前体和CP的MS碎片数据，并将其与预测的片段进行比较，以确认成功合成。