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相关概念视频

Protein Folding01:22

Protein Folding

117.2K
Overview
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Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

6.8K
Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
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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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相关实验视频

Updated: May 11, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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CovCysPredictor:使用蛋白质结构和可解释机器学习预测选择性共价可修改的囊蛋白.

Bryn Marie Reimer1,2, Ernest Awoonor-Williams1, Andrei A Golosov1

  • 1Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Biomedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

Journal of chemical information and modeling
|January 9, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了CovCysPredictor,这是一种机器学习工具,用于识别"可链接"的囊蛋白,用于向的共价药物发现. 它准确地预测了可能被选择性修饰的囊蛋白,推动了癌症药物开发.

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相关实验视频

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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科学领域:

  • 药物发现和开发 药物发现和开发
  • 计算化学的计算化学
  • 结构生物学 结构生物学

背景情况:

  • 有针对性的共价抑制是药物发现的关键策略,特别是对于具有挑战性的目标,如癌症中的突变KRAS.
  • 鉴定合适的氨酸残留物用于共价修饰仍然是一个重大障碍,因为目前的实验和计算工具的局限性.

研究的目的:

  • 开发和验证一种机器学习模型,用于准确预测"可链接"的半氨酸,用于向的共价药物发现.
  • 为了确定更有可能被选择性修饰的囊蛋白,增强药物特异性并减少非目标效应.

主要方法:

  • 利用CovPDB和CovBinderInPDB数据库来训练和测试可解释的机器学习模型.
  • 探索了各种物理化学特征 (例如,pKa,溶剂暴露,静电) 和蛋白质 - 连接体口袋描述器.
  • 开发了一个后勤回归模型,并使用F1分数对持有测试集来评估其性能.

主要成果:

  • 最终的后勤回归模型在独立的测试组中获得了0.73的F1中位数.
  • 该模型在全息蛋白上表现出合理的性能,在大多数情况下正确地识别出最容易被粘合的氨酸.
  • 开发的工具,CovCysPredictor,可以准确地预测潜在的可结合的囊蛋白,用于向的共价药物发现.

结论:

  • 机器学习模型可以有效地预测可结合的囊蛋白,优先考虑选择性而不是仅仅是反应性.
  • "CovCysPredictor为科学界提供了一种有价值的工具,以推动有针对性的共价药物发现.
  • 这种方法有望开发针对难以治疗的疾病的新疗法,包括各种人类癌症.