来自MBIS的非结合力场参数 分解分子电子密度 改善T4-溶酶双突变的结合亲和力预测
Luis Macaya1, Duván González1, Esteban Vöhringer-Martinez1
1Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile.
Journal of chemical information and modeling
|March 28, 2024
概括
计算机模拟通过改进结合亲和性预测来增强药物发现. 我们的新工作流使用最小基础代股东 (MBIS) 方法来准确的力场和验证的模拟,实现化学精度.
科学领域:
- 计算化学是一种计算化学.
- 分子建模分子建模
- 药物发现 药物发现
背景情况:
- 计算机模拟对于预测药物发现中的结合亲和关系至关重要.
- 免费能源计算中的精度限制阻碍了更广泛的采用.
- 错误源于力场和分子配置的样本采样不足.
研究的目的:
- 通过改进的力场和模拟方法,提高结合亲和力预测的准确性.
- 开发一个计算工作流来生成准确的分子力场参数.
- 为了验证模拟程序,准确的有约束力的自由能量计算.
主要方法:
- 开发了一个基于Python的工作流程,使用最小基础代存量持有者 (MBIS) 方法用于原子电荷和列纳德-斯参数导出.
- 在各种连接体配置 (结合和不结合) 上执行电子结构计算.
- 通过比较自我调整的混合物采样和非平衡热力学集成方法来进行具有约束力的自由能量计算,验证了模拟程序.
主要成果:
- 通过MBIS方法,从极化分子密度中获得准确的力场参数.
- 经过验证的模拟程序准确地计算了蛋白质和连接体自由度.
- 对于八种芳香配体,达到0.7 kcal/mol的平方根平均误差,超过了化学精度.
结论:
- 开发的计算工作流显著提高了绑定亲和度预测的准确性.
- 使用MBIS衍生的力场参数和验证的模拟程序可以提高预测质量.
- 这种方法为计算药物发现提供了更精确,更可靠的方法.
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