AIMD-Chig:探索一个166个原子的蛋白质Chignolin的结构空间与ab initio分子动力学
Tong Wang1, Xinheng He2,3,4,5, Mingyu Li2,3,6
1Microsoft Research AI4Science, Beijing, China. watong@microsoft.com.
Scientific data
|August 22, 2023
概括
本研究介绍了AIMD-Chig,这是一个使用精确的初始模拟生成的蛋白质构造的大数据集. 该资源可以更精确地建模蛋白质动态,并开发先进的机器学习潜力.
科学领域:
- 计算化学计算化学
- 生物物理学的生物物理.
- 材料科学 材料科学 材料科学
背景情况:
- 分子动力学 (MD) 模拟对于理解生物分子行为至关重要.
- 目前使用分子力学方法的准确性有限,忽略了电子效应.
- 最初的模拟提供了更高的准确性,但对于大型生物分子来说,它们在计算上昂贵.
研究的目的:
- 开发一个高精度的数据集,用于蛋白质结构动态.
- 为了在密度函数理论 (DFT) 层面上探索蛋白质构造空间.
- 为蛋白质开发机器学习潜力提供一个基准.
主要方法:
- 创建了AIMD-Chig,一个使用ab initio MD模拟的200万个蛋白质构造的数据集.
- 在166原子蛋白Chignolin上进行了模拟.
- 使用的M06-2X/6-31G*功能与340K的贝伦森恒温器,需要超过770万个CPU小时.
主要成果:
- 采集了1万个初始形状的样本,包括折叠,展开和转移稳定的状态.
- 报告了每个形状的坐标,能量和力.
- 从小分子到蛋白质,扩展了DFT级的构造探索.
结论:
- AIMD-Chig为蛋白质动力学提供了前所未有的DFT层次的洞察力.
- 该数据集作为机器学习潜力发展的基准.
- 有助于精确探索蛋白质动态.
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