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相关概念视频

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 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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Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

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Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
7.9K
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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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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

12.5K
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...
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相关实验视频

Updated: Jun 13, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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DeepAllo:使用蛋白语言模型 (pLM) 进行全位预测,并进行多任务学习.

Moaaz Khokhar1,2, Ozlem Keskin3, Attila Gursoy1,2

  • 1Department of Computer Engineering, Koç University, 34450 Istanbul, Turkey.

Bioinformatics (Oxford, England)
|May 15, 2025
PubMed
概括
此摘要是机器生成的。

DeepAllo通过将蛋白质语言模型与口袋功能相结合,改善了全位预测. 这种新的方法通过准确识别关键的全囊提升了药物开发.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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相关实验视频

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科学领域:

  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • ,对于蛋白质功能至关重要,在药物开发中提出了重大挑战.
  • 识别全囊是复杂的,机器学习提供了有前途的预测策略.

研究的目的:

  • 开发一种先进的方法来预测全囊口袋.
  • 为了提高蛋白质中的全位点识别的准确性和性能.

主要方法:

  • 在AlloSteric数据库 (ASD) 上使用多任务学习微调蛋白语言模型 (pLM).
  • 将pLM功能与FPocket功能集成在一起,以训练XGBoost和AutoML模型.
  • 视觉化pLM的注意力机制,以提供可解释性.

主要成果:

  • DeepAllo获得了89.66%的F1得分和90.5%的前三名预测准确度.
  • 这种方法在全位预测方面优于以前的方法.
  • 一个案例研究验证了该方法对已知的全蛋白的有效性.

结论:

  • DeepAllo代表了在预测全囊口袋方面取得的重大进展.
  • 在pLM和口袋功能的组合提供了卓越的性能.
  • 这种方法对加速药物发现和开发有着强烈的影响.