Modelling protein covalent modifications with molecular simulation and machine learning

这个项目将专注于开发和验证FEP方法来模拟蛋白质结构改变对分子识别机制的影响。努力将集中在生物制品、模拟多肽的配体和共价配体上，这些都是目前缺乏可靠的模拟协议的。这类配体对为小分子量身定做的传统FEP方法提出了挑战，因为这些配体通常更大、更灵活，并且可以共价结合到目标蛋白质上。为了解决这些问题，这项研究将把开源软件中可用的最先进的FEP模拟方法与势能函数的机器学习的进步结合起来，以实现对结合能量学的准确建模。总体目标是提供一套足够快速和准确的蛋白质FEP协议，使其能够在工业研发中常规使用。这是开发下一代计算机辅助药物设计软件和方法的令人兴奋的机会。完成学习后，成功的申请者将在分子建模方面获得强大的技术专业知识，并与生命科学软件行业密切合作。

This project will focus on developing and validating FEP methodologies to model the effect of protein structure modifications on molecular recognition mechanisms. Efforts will focus on biologics, peptidomimetic ligands, and covalent ligands for which robust simulation protocols are currently lacking. These classes of ligands present challenges for conventional FEP approaches tailored for small molecules because the ligands are often larger, more flexible, and can covalently bind to their target proteins. To address these issues this research will couple state of the art FEP simulation methodologies available in open-source software with advances in machine learning of potential energy functions to enable accurate modelling of binding energetics. The overall aim is to deliver a suite of protein FEP protocols sufficiently rapid and accurate to enable routine use in industrial R&D. This is an exciting opportunity to develop next-generation computer-aided drug design software and methodologies. Upon completion of the studentship, the successful applicant will have gained strong technical expertise in molecular modelling and worked closely with the life sciences softtware industry sector.