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相关概念视频

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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通过多层次的灵活动态轨迹预培训,建立一个基于图像的蛋白质-连接体结合表征学习框架.

Hongxin Xiang1, Mingquan Liu1, Linlin Hou1

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.

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概括

图像PLB是一个新的框架,用于学习蛋白质-连接体结合 (PLB) 表示,使用3D连接体图像和蛋白质图. 它在PLB预测任务中实现了竞争性改进,推动了药物发现.

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科学领域:

  • 计算化学和化学信息学
  • 结构生物学和生物信息学
  • 药物发现和药物化学

背景情况:

  • 准确预测蛋白质 - 连接体结合 (PLB) 关系对于药物发现至关重要,有助于识别针对特定蛋白质的药物.
  • 测量PLB的传统实验方法耗时且昂贵.
  • 现有的PLB预测计算模型需要更准确的表示来满足药物发现标准.

研究的目的:

  • 开发一种基于图像的先进框架,用于学习蛋白质-连接体结合表征.
  • 提高用于药物发现应用的蛋白质 - 连接体相互作用预测的准确性和效率.
  • 引入一种新的预培训策略,以加强互动信息的学习.

主要方法:

  • 提出了ImagePLB,这是一个基于图像的框架,具有连接体表示学习器 (LRL) 和蛋白质表示学习器 (PRL),可以接受3D连接体图像和蛋白质图.
  • 引入了一个多级下一个轨迹预测 (MLNTP) 任务,以预先训练ImagePLB在16972个复杂的4D灵活动态轨迹上.
  • 整合了轨迹规范化 (TR),以减轻相邻轨迹的特征相似性问题.

主要成果:

  • 与最先进的方法相比,ImagePLB在蛋白质 - 配体亲和力和疗效预测任务上表现出具有竞争力的改进.
  • 在MLNTP预训练任务中,有效地学习了连接体,蛋白质和复杂水平的轨迹相关信息.
  • TR成功地解决了邻近轨迹引起的高特征相似性问题.

结论:

  • 图像PLB为基于图像的蛋白质-连接体结合学习提供了一个有希望的新范式.
  • 该框架提高了PLB预测的准确性,支持更有效的药物发现.
  • 这种方法为未来在计算药物设计和开发方面的进步铺平了道路.