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相关概念视频

Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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BigBind:从非结构性数据中学习基于结构的虚拟选.

Michael Brocidiacono1, Paul Francoeur2, Rishal Aggarwal2

  • 1Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.

Journal of chemical information and modeling
|December 19, 2023
PubMed
概括
此摘要是机器生成的。

一个新的数据集,BigBind,和一个深度学习模型,香,改善预测蛋白质-连接体结合的药物发现. 与现有方法相比,这种方法提高了虚拟查的效率和准确性.

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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:

  • 深度学习模型用于蛋白质 - 配体结合的预测对于基于结构的虚拟查至关重要.
  • 由于数据集大小的限制,在PDBBind数据上训练的现有模型与一般化作斗争.
  • 切姆布尔数据库提供了广泛的活动数据,但缺乏具有约束力的姿势信息.

研究的目的:

  • 介绍BigBind,这是一个新的数据集,将ChEMBL活动数据映射到蛋白质结构中.
  • 开发和评估使用BigBind分类活性/无活性化合物的神经网络模型Banana.
  • 提高药物发现虚拟查的效率和准确性.

主要方法:

  • 通过将ChEMBL活动数据与CrossDocked蛋白质结构 (包括583K连接体活动和3D结合口袋结构) 集成,创建BigBind数据集.
  • 用同等数量的假定无活性化合物增加BigBind对每个标蛋白的增加.
  • 开发香 (基本神经网络的结合亲和力) 模型,用于对联体活性的二元分类.

主要成果:

  • 香在BigBind测试集中获得了0.72的AUC,超过了仅依靠连接体的模型 (AUC0.59).
  • 香在LIT-PCBA基准指标上表现出竞争力 (中位数EF1% 1.81).
  • 香的运行速度是Gnina分子对接的16000倍,显示出显著的计算效率.

结论:

  • BigBind数据集为训练药物发现中的深度学习模型提供了宝贵的资源.
  • 香模型显示了提高虚拟选的准确性和速度的承诺.
  • 这些进展预计将大大提高未来虚拟选任务的结果.