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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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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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...
12.6K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
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...
4.8K
Protein Networks02:26

Protein Networks

4.0K
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,...
4.0K
Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
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...
12.9K
Allosteric Proteins-ATCase01:19

Allosteric Proteins-ATCase

5.8K
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...
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相关实验视频

Updated: Jul 23, 2025

Identifying Protein-protein Interaction Sites Using Peptide Arrays
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Identifying Protein-protein Interaction Sites Using Peptide Arrays

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发现蛋白质中的功能性重要部位.

Matteo Cagiada1, Sandro Bottaro1, Søren Lindemose1

  • 1Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.

Nature communications
|July 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种机器学习方法,通过整合序列和稳定性数据来识别蛋白质中的关键功能位点. 这种方法有助于了解引起疾病的变体,并发现蛋白质的功能.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 遗传学 遗传学 是一个

背景情况:

  • 错误的变异是遗传疾病的主要原因.
  • 鉴定功能性重要的蛋白质位点是具有挑战性的,因为数据有限和结构稳定性等混因素.
  • 现有的方法难以区分功能重要性和结构约束.

研究的目的:

  • 开发一种机器学习模型,用于预测蛋白质中的功能位点.
  • 为了准确预测,将序列保护与生物物理稳定性模型集成在一起.
  • 验证该模型在发现活性,调节和结合部位以及理解疾病机制方面的实用性.

主要方法:

  • 开发了一种机器学习方法,将统计蛋白质序列模型与生物物理稳定模型相结合.
  • 通过对变异效应的多重化实验数据训练模型.
  • 在各种蛋白质功能和特定疾病背景下广泛验证了该模型.

主要成果:

  • 该模型成功预测了功能站点,包括活跃,监管和绑定站点.
  • 证明了该模型能够精确确定致病误解变体的分子机制的能力.
  • 未来的预测和实验验证证证了该模型对与莱什-尼汉综合征相关的HPRT1变异的实用性.

结论:

  • 这种新型机器学习方法有效地识别了功能性重要蛋白质位点.
  • 这种方法促进了对蛋白质功能,变异效应和疾病发病的理解.
  • 该工具对生物技术,药理学和遗传疾病研究有重大影响.