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相关概念视频

Phosphoinositides and PIPs01:42

Phosphoinositides and PIPs

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Phosphoinositides are a group of phospholipids containing a glycerol backbone with two fatty acid chains and a phosphate attached to a myoinositol sugar ring. The inositol head group extends into the cytoplasm, where it is modified by adding phosphate groups to form phosphatidylinositol phosphates or PIPs.
Different phosphoinositides are synthesized and recruited on the cytosolic face of the plasma membrane. The localization of specific phosphoinositides concentrated in separate membrane...
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Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
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The mammalian target of rapamycin  (mTOR) is a serine/threonine kinase that regulates growth, proliferation, and cell survival in response to hormones, growth factors, or nutrient availability. This kinase exists in two structurally and functionally distinct forms: mTOR complex 1  (mTORC1) and mTOR complex 2  (mTORC2). The first form (mTORC1) is composed of a rapamycin-sensitive Raptor and proline-rich Akt substrate, PRAS40. In contrast,  mTORC2 consists of a...
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Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and...
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相关实验视频

Updated: Jun 1, 2025

A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors
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使用MolAnchor分析合理化对异形选择性酸3-激酶抑制剂的预测.

Alec Lamens1,2, Jürgen Bajorath1,2

  • 1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, Bonn D-53115, Germany.

Journal of chemical information and modeling
|January 17, 2025
PubMed
概括

一种新的可解释的人工智能方法MolAnchor识别了用于预测酸酸3-激酶 (PI3K) 抑制剂选择性的关键化学碎片. 这种方法提供了化学直观的解释,通过揭示分子结构和标选择性之间的因果关系来帮助药物发现.

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科学领域:

  • 药用化学 医学化学
  • 计算化学的计算化学
  • 人工智能在药物发现中的作用

背景情况:

  • 解释机器学习模型预测对于它们在药物发现中的采用至关重要.
  • 酸3-酶 (PI3K) 抑制剂是重要的治疗药物,但实现异型选择性是具有挑战性的.

研究的目的:

  • 开发和验证一种新的方法,MolAnchor,用于生成PI3K抑制剂异型选择性的机器学习预测的化学直观解释.
  • 为了确定负责预测抑制剂选择性的特定结构碎片.

主要方法:

  • 创建一个测试系统来预测PI3K抑制剂异型选择性.
  • 使用MolAnchor方法论对正确预测的系统分析,基于可解释的人工智能""概念.
  • 将MolAnchor解释与其他方法的特征重要性值进行比较.

主要成果:

  • 在大多数情况下,MolAnchor成功地识别了明确的结构碎片,通常是单个子结构,负责预测同型选择性.
  • 对于具有不同异型选择性的抑制剂,发现了明显的反复的亚结构.
  • 与特征重要性值相比,MolAnchor解释显示出更高的解释性.
  • 两个反复出现的子结构与PI3K异型选择性直接相关,这表明存在因果关系.

结论:

  • 该MolAnchor方法为药物发现中的机器学习预测提供了化学直观和可解释的解释.
  • 识别与选择性相关的特定子结构可以指导设计更有选择性的PI3K抑制剂.
  • 这种方法通过阐明化合物活性和选择性的基础,增强了预测建模在药物发现项目中的整合.