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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

13.6K
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...
13.6K
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
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...
4.4K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.9K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.9K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
13.9K
Allosteric Proteins-ATCase01:19

Allosteric Proteins-ATCase

5.9K
Binding sites linkages can regulate a protein's function.  For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
Aspartate transcarbamoylase (ATCase) is a cytosolic enzyme that catalyzes the condensation of L-aspartate and carbamoyl phosphate to  N-carbamoyl-L-aspartate. This reaction is the first step in pyrimidine biosynthesis. UTP and CTP, the end products of the pyrimidine synthesis...
5.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.8K
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...
13.8K

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相关实验视频

Updated: Sep 19, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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CAML:交换式代数机器学习─一个关于蛋白质 - 配体结合亲和力预测的案例研究.

Hongsong Feng1, Faisal Suwayyid2,3, Mushal Zia3

  • 1Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States.

Journal of chemical information and modeling
|June 16, 2025
PubMed
概括
此摘要是机器生成的。

交换代数机器学习 (CAML) 使用持久的斯坦利-赖斯纳理论预测蛋白质-连接体结合亲缘关系. 这种新的方法优于现有的方法,用于预测蛋白质 - 连接体和金属蛋白 - 连接体复合体的结合亲缘关系.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 机器学习 机器学习
  • 代数拓学是一种代数拓学.

背景情况:

  • 机器学习和数据科学越来越多地利用先进的数学概念.
  • 交换代数是抽象代数的一个分支,为数据分析提供了新的框架.
  • 预测蛋白质 - 配体结合亲和关系对于药物发现和开发至关重要.

研究的目的:

  • 引入交换代数机器学习 (CAML) 用于预测蛋白质 - 连接体结合亲缘关系.
  • 应用从组合式交换代数到绑定亲和力预测的持久斯坦利-赖斯纳理论.
  • 开发用于分析复杂 (金属) 蛋白质 - 连接体相互作用的新算法.

主要方法:

  • 开发了三种新的算法:元素特异性换算代数,类别特异性换算代数,以及对二元复合体的换算代数.
  • 应用持久的斯坦利-赖斯纳理论来建模蛋白质-连接体和金属蛋白-连接体结合数据.
  • 对CAML的比较分析与现有的最先进的亲和力预测方法进行比较.

主要成果:

  • 与当前的方法相比,CAML在预测蛋白质 - 连接体结合亲缘关系方面表现优越.
  • 拟议的算法有效地处理 (金属) 蛋白质-连接体复杂数据中固有的复杂性.
  • 持久的斯坦利-赖斯纳理论在亲和力预测任务中被证明是有效的.

结论:

  • 交换代数机器学习 (CAML) 为计算生物学和数据科学提供了一个强大的新范式.
  • 开发的CAML算法显示出对提高约束亲和力预测的准确性的重大承诺.
  • 这项工作突出了利用抽象代数结构来进行复杂的生物预测的潜力.