Modeling Protein-Ligand Cryo-EM Structures with Neural Network Potentials

使用神经网络势对蛋白质配体冷冻电镜结构进行建模

• 批准号: NE/X007472/1
• 负责人: Jeremy Skipper
• 金额: $ 1.33万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX007472%2F1
• 关键词: Modeling; Protein; Ligand; Cryo; EM

BBSRC : Regan Harle : BB/T008709/1Drugs act by binding to a target protein. Determining the three-dimensional shape the complex between a drug and a protein is challenging, particularly when the protein spans the membrane separating the interior and exterior of a cell. Cryo-electron microscopy allows these structures to be measured experimentally, but often the resolution is too low to aid the development of new drugs. We will combine this technique with artificial intelligence protein structure determination and computer simulations to determine the structures of drug-bound structures with minimal direction by the user but with a level of resolution that had not been possible in the past. I will be trained in machine learning, molecular simulation, and artificial-intelligence protein structure prediction.

药物通过与靶蛋白结合而起作用。确定药物和蛋白质之间复合物的三维形状是具有挑战性的，特别是当蛋白质跨越分隔细胞内外的膜时。低温电子显微镜可以在实验中测量这些结构，但通常分辨率太低，无法帮助开发新药。我们将这项技术与人工智能蛋白质结构测定和计算机模拟相结合，以确定药物结合结构的结构，用户只需最小的方向，但分辨率水平在过去是不可能的。我将接受机器学习、分子模拟和人工智能蛋白质结构预测方面的培训。