SG-ML-PLAP:一种基于结构导向机器学习的评分功能,用于蛋白质 - 配体结合亲缘关系预测
Sapna Pal1, Ankita Pal1, Debasisa Mohanty1
1Bioinformatics Center, National Institute of Immunology, New Delhi, India.
Protein science : a publication of the Protein Society
|December 11, 2024
概括
这项研究引入了一种新的基于机器学习的评分函数 (MLSF),用于预测蛋白质-连接体结合亲和力. 开发的MLSF,SG-ML-PLAP,比传统方法更准确,有助于药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 预测蛋白质 - 配体结合亲和力对于药物设计至关重要.
- 机器学习 (ML) 越来越多地被使用,但准确的复合排名仍然是一个挑战.
研究的目的:
- 开发一种新的基于ML的评分函数 (MLSF),用于准确的结合亲和力预测.
- 评估开发的MLSF与现有方法的性能.
主要方法:
- 利用PDBbind数据集中的扩展连接交互指纹 (ECIF) 来构建ML模型.
- 在评分函数比较评估 (CASF) 数据集和未见复合体上的基准性能.
- 研究了在训练套件中包括重制蛋白-配体复合物的影响.
主要成果:
- 开发的MLSF,SG-ML-PLAP,证明了更好的结合亲和力预测准确性.
- 补充晶体结构与重制复合物增强了MLSF的性能.
- 在具有ECIF和VINA特征的晶体结构上训练的MLSF显示了晶体和对接复合体的高精度.
结论:
- 拟议的MLSF与传统的评分功能和其他MLSF相比,提供了更高的性能.
- SG-ML-PLAP是基于结构的虚拟查和识别新型抑制剂的宝贵工具.
- 在SG-ML-PLAP网络服务器是免费可访问的,用于更广泛的研究使用.
关键词:
欧洲资金交易所 (ECIF) 是一个国际金融机构.停靠的对接方式梯度提升了树木的增长.机器学习的评分功能是机器学习的评分功能.神经网络的神经网络的神经网络蛋白联体结合亲和关系 蛋白联体结合亲和关系随机的森林随机的森林更多相关视频
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