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相关概念视频

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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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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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...
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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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Allosteric Proteins-ATCase01:19

Allosteric Proteins-ATCase

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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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Ligand Binding Sites02:40

Ligand Binding Sites

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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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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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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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相关实验视频

Updated: May 20, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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可解释的深度多层次注意力学习,用于预测蛋白质碳化位.

Jian Zhang1,2, Jingjing Qian1,2, Pei Wang3

  • 1School of Computer and Information Technology, Xinyang Normal University, Xinyang, 464000, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|March 27, 2025
PubMed
概括
此摘要是机器生成的。

预测蛋白质碳化位对于理解氧化应激和疾病至关重要. 一个新的深度学习框架,SCANS,准确地识别这些站点,最大限度地减少与连接体相互作用站点的重叠,以获得更好的生物见解.

关键词:
注意力机制注意力机制交叉预测 交叉预测连接体相互作用点的交互点.蛋白质碳化位是蛋白质的碳化位.

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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
  • 蛋白质组学是指蛋白质组学.

背景情况:

  • 蛋白质碳化,氧化应激的标志物,改变蛋白质功能和细胞过程.
  • 精确识别碳化位对于疾病机制研究至关重要.
  • 现有的计算方法常常将连接体相互作用位误认为碳化位点.

研究的目的:

  • 开发一种新的深度学习框架,用于准确预测蛋白质碳化位.
  • 为了应对碳化和配体相互作用站点之间的交叉预测的挑战.
  • 提高计算预测工具的特异性和性能.

主要方法:

  • 选择性碳化位 (SCANS) 的引入,这是一个深度学习框架.
  • 采用多层次的注意力策略,用于本地和全球特征提取.
  • 应用量身定制的损失函数和转移学习来提高预测准确性和特异性.

主要成果:

  • 与现有方法相比,SCANS显示出优越的预测性能.
  • 该框架实现了持续较低的假阳性率,包括减少交叉预测.
  • 动因分析为蛋白质碳化位点的特征提供了新的见解.

结论:

  • 在预测蛋白质碳化位方面,SCANS提供了显著的进步.
  • 该框架有效地区分了碳化位点和配体相互作用位点.
  • 这种工具为与氧化压力相关的疾病和蛋白质功能提供了宝贵的见解.