在模型蛋白质:蛋白质复合体的相对约束自由能量计算中识别和克服采样挑战
Ivy Zhang1,2, Dominic A Rufa1,3, Iván Pulido1
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.
这项研究提高了使用Perses包对蛋白质结合突变的自由能量计算. 它确定了采样挑战,并证明了蛋白质:蛋白质相互作用的准确预测.
科学领域:
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
- 药物发现 药物发现
背景情况:
- 相对化学结合的自由能量计算优化了小分子亲和力.
- 估计突变对蛋白质的影响:由于复杂的接口,蛋白质结合带来了采样挑战.
研究的目的:
- 调查和解决蛋白质:蛋白质结合自由能量计算中的采样挑战.
- 扩展Perses包,用于预测氨基酸突变对蛋白质:蛋白质结合的影响.
主要方法:
- 使用 GPU 加速的 Perses 包进行相对自由能计算.
- 采用了 barnase:barstar 模型系统来分析采样问题.
- 探索化学复制品交换和溶液炼方法.
主要成果:
- 鉴定了电荷变异突变和缓慢的自由度作为采样问题的来源.
- 在实验值的1kcal/mol范围内达到86%的准确预测.
- 具有约束力的自由能量变化标志的100%正确分类.
结论:
- 开发了一种工作流程,用于复合体中蛋白质突变的自由能量计算.
- 在这些化学转化过程中,编目采样面临着固有的挑战.
- Perses 包为这些计算提供了一个开源解决方案.
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