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相关概念视频

Induced-fit Model01:13

Induced-fit Model

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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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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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冷启动CPI:诱导适合理论引导的DTI预测模型,具有改进的概括性能.

Qichang Zhao1,2,3, Haochen Zhao1,2,3, Linyuan Guo1,3

  • 1School of Computer Science and Engineering, Central South University, Changsha, China.

Nature communications
|July 11, 2025
PubMed
概括
此摘要是机器生成的。

冷启动CPI通过将分子视为灵活的来预测化合物-蛋白相互作用 (CPI),在新的目标和稀疏的数据上表现优于现有的方法. 这个框架通过学习分子特征来增强药物发现,以获得更好的预测准确性.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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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
  • 生物信息学是一种生物信息学.

背景情况:

  • 预测化合物-蛋白相互作用 (CPI) 对药物发现至关重要.
  • 传统的刚性对接方法与分子灵活性和稀疏数据作斗争.

研究的目的:

  • 介绍ColdstartCPI,这是一个用于预测CPI的新框架.
  • 解决现有方法的局限性,特别是在新型化合物和蛋白质方面.

主要方法:

  • 利用无监督的预训练功能和一个变压器模块.
  • 纳入诱导适合理论来建模分子灵活性.
  • 利用基于序列的学习来学习化合物和蛋白质.

主要成果:

  • 冷启动CPI的表现优于最先进的基于序列的模型,特别是对于看不见的目标.
  • 与基于结构的方法相比,在虚拟选中表现出强大的概括性.
  • 在稀疏和低相似性数据条件下表现出色.

结论:

  • 冷启动CPI提供了一种灵活的,基于序列的药物设计方法.
  • 为药物发现提供了一个有前途的工具,特别是在数据有限的场景中.
  • 通过分子对接和结合自由能量计算验证.