Protein structure prediction by deep long-range learning

通过深度远程学习预测蛋白质结构

• 批准号: DP180102060
• 负责人: Prof Yaoqi Zhou
• 金额: $ 23.65万
• 依托单位: Griffith University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2018
• 资助国家: 澳大利亚
• 起止时间: 2018-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP180102060
• 关键词: Protein; structure; prediction; deep; long

This project aims to address the challenging problem of protein structure prediction by developing deep long-range learning methods. The project expects to advance protein structure prediction by capturing the long-range interactions through whole sequence learning, rather than short window-based learning. Expected outcomes include next-generation machine-learning techniques for predicting one, two and three-dimensional protein structures from their sequences. The expected outcomes should provide significant benefits by computationally determining protein structures beyond homologous sequences, and enabling structure-based drug discovery to disease-causing protein targets previously inaccessible to biotech and pharmaceutical companies.

该项目旨在通过开发深度远程学习方法来解决蛋白质结构预测的挑战性问题。该项目希望通过全序列学习而不是基于短窗口的学习来捕获长程相互作用，从而推进蛋白质结构预测。预期成果包括下一代机器学习技术，用于从序列中预测一维、二维和三维蛋白质结构。预期的结果应该通过计算确定同源序列之外的蛋白质结构，并使基于结构的药物发现能够达到生物技术和制药公司以前无法达到的致病蛋白质靶点，从而提供显着的好处。