LOOPER: Machine learning for druggable protein conformations

LOOPER：可药物蛋白质构象的机器学习

• 批准号: 2825802
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2825802
• 关键词: LOOPER; Machine; learning; druggable; protein

This project aims at combining molecular dynamics (MD) simulation and machine learning (ML) techniques to predict how proteins move, and to improve the way drug-like molecules can target flexible protein regions. This builds upon the expertise of the Degiacomi group on generative neural networks (GNNs) and that of the Mey group in method development for MD simulations and drug design. The student will address the questions of: can a GNN be used for generating biologically relevant protein conformations? How can the ensemble of structures then be used for improved design of drug-like molecules that specifically target their IDRs? These questions are central to understanding neurodegenerative diseases such as Parkison's and Alzheimer's, as well as the molecular mechanisms behind antimicrobial resistance. A three-month placement at Redesign Science (www.redesignscience.com) will enable the student to apply their acquired MD and ML techniques to get insights into an active drug discovery project, and learn how to streamline their work in accordance with industry timescales.

该项目旨在结合分子动力学（MD）模拟和机器学习（ML）技术来预测蛋白质如何移动，并改进药物样分子靶向灵活蛋白质区域的方式。这建立在Degiacomi小组在生成神经网络（gnn）方面的专业知识和Mey小组在MD模拟和药物设计方法开发方面的专业知识的基础上。该学生将解决以下问题：GNN能否用于生成生物学相关的蛋白质构象？如何将这些结构集合用于改进药物样分子的设计，以特异性地靶向它们的idr ？这些问题对于理解神经退行性疾病，如帕金森氏症和阿尔茨海默氏症，以及抗菌素耐药性背后的分子机制至关重要。在重新设计科学（www.redesignscience.com）进行为期三个月的实习，将使学生能够应用他们获得的MD和ML技术来深入了解正在进行的药物发现项目，并学习如何根据行业时间表简化他们的工作。