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相关概念视频

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
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.8K
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:
13.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.5K
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...
13.5K
Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Ligand Binding Sites02:40

Ligand Binding Sites

13.4K
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...
13.4K
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

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相关实验视频

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A Protocol for Computer-Based Protein Structure and Function Prediction
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通过同源性预测亲和关系 (PATH):可解释的结合亲和关系预测与持久的同源性.

Yuxi Long1, Bruce R Donald1,2

  • 1Department of Computer Science, Department of Mathematics, Duke University, Durham, North Carolina, United States of America.

PLoS computational biology
|June 27, 2025
PubMed
概括

我们开发了PATH+,这是一种新的机器学习算法,用于准确的结合亲和力预测 (BAP),可以解释和通用. PATH+为药物设计提供了更好的速度和准确性,优于现有的方法.

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习 机器学习
  • 结构生物学是结构生物学.

背景情况:

  • 准确的结合亲和力预测 (BAP) 对于基于结构的药物设计至关重要.
  • 当前的BAP算法往往缺乏可概括性和可解释性.

研究的目的:

  • 为了介绍PATH+,BAP的新,可概括和可解释的机器学习算法.
  • 为了提高药物发现的BAP的速度和准确性.

主要方法:

  • 开发了PATH+,一种利用计算拓学的机器学习算法.
  • 通过PATH+捕获的蛋白质-配体复合体的可视化特征.
  • 创建了PATH- - 一个评分功能,用于绑定/非绑定区分.

主要成果:

  • 与现有的BAP算法相比,PATH+在数据集中表现出类似或更高的准确性和通用性.
  • PATH+ 是第一个内在可解释的 BAP 算法.
  • PATH+的速度比主流的计算拓算法快10倍以上.
  • PATH-在区分结合剂和非结合剂方面表现出卓越的准确性.

结论:

  • PATH+为BAP提供了一种独特的解释性,速度和准确性的组合.
  • 该算法能够对虚拟抑制器库进行拓选.
  • 开源代码可用,促进对药物设计的信任和应用.