通过利用序列组成部分的同质性来预测蛋白质 - 配体结合的亲缘关系
Abbi Abdel-Rehim1, Oghenejokpeme Orhobor2, Lou Hang3
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom.
Bioinformatics (Oxford, England)
|August 12, 2023
概括
这项研究表明,简单的蛋白质和配体描述器与梯度增强树可以准确地估计结合亲和力,甚至在不知道确切的结合姿势的情况下,在基准数据集上表现优于其他方法.
科学领域:
- 计算化学和化学信息学
- 机器学习在药物发现中的应用.
背景情况:
- 分子对接对于估计结合亲和关系至关重要,机器学习提高了准确性.
- 现有的方法通常依赖于来自蛋白质-连接体复合体的复杂的空间和分类信息.
- 评估通常使用PDBbind数据集,特别是评分函数比较评估 (CASF) 基准.
研究的目的:
- 开发一种简化但有效的方法来进行约束性亲和度估计.
- 为了证明最小描述符可以准确地预测结合亲和力.
- 在已建立的基准和缺乏空间信息的数据集上验证方法.
主要方法:
- 使用一小组连接体和蛋白质描述器.
- 采用梯度提升树来进行亲和预测.
- 在CASF 2007,2013和2016数据集以及大型ChEMBL衍生数据集上进行测试.
主要成果:
- 开发的方法在CASF数据集上显示出高性能,在CASF2016上表现出色.
- 与基准数据集的现有方法相比,实现了更高的性能.
- 即使确切的结合形状未知,也证明了有效性.
结论:
- 一组节的描述符与梯度增强树相结合,为绑定亲和力预测提供了一个强大的工具.
- 这种方法简化了亲和度估计,并扩大了对各种数据集的适用性.
- 该方法为计算药物发现提供了强大而高效的替代方案.
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