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相关概念视频

Inhibition of Cdk Activity02:34

Inhibition of Cdk Activity

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The orderly progression of the cell cycle depends on the activation of Cdk protein by binding to its cyclin partner. However, the cell cycle must be restricted when undergoing abnormal changes. Most cancers correlate to the deregulated cell cycle, and since Cdks are a central component of the cell cycle, Cdk inhibitors are extensively studied to develop anticancer agents. For instance, cyclin D associates with several Cdks, such as Cdk 4/6, to form an active complex. The cyclin D-Cdk4/6 complex...
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M-Cdk Drives Transition Into Mitosis02:15

M-Cdk Drives Transition Into Mitosis

6.2K
Checkpoints throughout the cell cycle serve as safeguards and gatekeepers, allowing the cell cycle to progress in favorable conditions and slow or halt it in problematic ones. This regulation is known as the cell cycle control system.
Cyclin-dependent kinases, or Cdks, work in concert with cyclins to control cell cycle transitions. M-Cdk, a complex of Cdk1 bound to M cyclin, is a well-known example of this coordinated control that drives the transition from the G2 to the M phase.
M cyclin...
6.2K
Positive Regulator Molecules02:39

Positive Regulator Molecules

6.4K
Mitotic cell division results in daughter cells that exactly resemble the parent cell. However, errors in the DNA replication or distribution of genetic material may lead to genetic mutations that may be passed down to every new cell formed from the resulting abnormal cell. Propagation of such mutant cells is restricted through checkpoint mechanisms present at different stages of the cell cycle. These checkpoints involve regulator molecules that either promote or demote cell cycle events.
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Negative Regulator Molecules01:23

Negative Regulator Molecules

38.2K
Positive regulators allow a cell to advance through cell cycle checkpoints. Negative regulators have an equally important role as they terminate a cell’s progression through the cell cycle—or pause it—until the cell meets specific criteria.
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相关实验视频

Updated: Jan 6, 2026

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
10:33

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

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弹性净回归用于预测CDK2抑制.

Amauri Duarte da Silva1, Walter Filgueira de Azevedo2

  • 1Graduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre, Porto Alegre, RS, Brazil.

Methods in molecular biology (Clifton, N.J.)
|October 11, 2025
PubMed
概括
此摘要是机器生成的。

弹性网回归模型复杂的蛋白质系统,如蛋白质与药物相互作用. 这项研究详细介绍了其用于预测酶抑制的用途,其性能优于传统的评分功能.

关键词:
人工智能的人工智能是人工智能.在CDK2中使用CDK2.复杂的系统复杂的系统.弹性网是一种弹性网.机器学习是机器学习.桑德里斯 2.0 的版本评分功能的空间空间.

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Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay
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Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

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Experimental Approaches to Study Mitochondrial Localization and Function of a Nuclear Cell Cycle Kinase, Cdk1
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Experimental Approaches to Study Mitochondrial Localization and Function of a Nuclear Cell Cycle Kinase, Cdk1

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相关实验视频

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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay
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Experimental Approaches to Study Mitochondrial Localization and Function of a Nuclear Cell Cycle Kinase, Cdk1
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科学领域:

  • 计算生物学是一种计算生物学.
  • 化学信息学 化学信息学
  • 药物发现 药物发现

背景情况:

  • 弹性网回归是一种强大的工具,用于构建复杂生物系统中的预测模型.
  • 蛋白与药物相互作用和酶抑制是药物发现和开发中的关键领域.
  • 预测酶抑制的现有计算方法可以得到改进.

研究的目的:

  • 解释弹性网回归方法及其在蛋白质系统建模中的应用.
  • 引入SAnDReS 2.0程序,用于构建回归模型来预测酶抑制.
  • 开发和评估一个弹性网回归模型,用对接模拟数据来预测蛋白质向抑制.

主要方法:

  • 使用了Sikit-Learn实现弹性网回归的方法.
  • 采用SAnDReS 2.0程序进行回归模型开发.
  • 从对接模拟中应用弹性网回归到原子坐标,以预测酶抑制.
  • 探索了评分函数的概念及其在Elastic Net.的实现.

主要成果:

  • 开发了一种弹性网回归模型来计算蛋白质标抑制.
  • 与经典评分函数相比,弹性网回归模型的预测性能明显优越.
  • 成功预测了循环林依赖激酶2的抑制.

结论:

  • 弹性网回归提供了一个强大的方法来建模蛋白与药物相互作用,并预测酶抑制.
  • 开发的弹性网模型显示了比传统的评分函数更高的预测准确性.
  • 像SAnDReS 2.0和Scikit-Learn这样的开源工具促进了在计算药物发现中应用先进的回归技术.