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相关概念视频

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...
12.8K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.8K
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:
12.8K
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...
4.2K
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
Globular and Fibrous Proteins02:21

Globular and Fibrous Proteins

43.5K
Many proteins can be classified into two distinct subtypes - globular or fibrous. These two types differ in their shapes and solubilities.
Globular proteins are also known as spheroproteins and typically are approximately round in shape. They contain a mix of amino acid types and contain differing sequences in their primary structures. Globular proteins have many different functions, such as enzymes, cellular messengers, and molecular transporters. These roles often require the proteins to be...
43.5K
Gene Families01:57

Gene Families

8.8K
Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
8.8K

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相关实验视频

Updated: Jun 16, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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Published on: November 3, 2011

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AntiFormer:图形增强的大型语言模型用于绑定亲和力预测.

Qing Wang1, Yuzhou Feng2,3, Yanfei Wang1

  • 1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, FL 32611, USA.

Briefings in bioinformatics
|August 20, 2024
PubMed
概括
此摘要是机器生成的。

新的人工智能工具AntiFormer准确地预测了抗体结合亲和力,加速了基于抗体的治疗和诊断的发展. 它识别了针对SARS-CoV-2的强效抗体,并揭示了流感疫苗免疫反应的年龄相关差异.

关键词:
抗体结合亲和力 抗体结合亲和力抗体成熟的过程大型语言模型一个单细胞的BCR.

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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相关实验视频

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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科学领域:

  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 抗体对于免疫防御和治疗至关重要.
  • 评估抗体-抗原结合亲和力至关重要,但目前的方法具有挑战性.
  • 亲和力成熟通过体变异来改进抗体的特异性和结合强度.

研究的目的:

  • 介绍AntiFormer,一个基于图形的大型语言模型,用于预测抗体结合亲和力.
  • 评估AntiFormer的性能与现有的抗体亲和力预测方法相比.
  • 应用AntiFormer以识别中和抗体并了解免疫反应变异.

主要方法:

  • 开发了AntiFormer,这是一个基于图形的大型语言模型,集成了抗体序列信息.
  • 评估了AntiFormer的预测准确性和计算效率.
  • 应用AntiFormer来分析严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 患者和流感疫苗接种者的抗体反应.

主要成果:

  • 与现有方法相比,AntiFormer在预测抗体结合亲和力方面表现优越.
  • 该模型在减少计算时间的情况下实现了准确的预测.
  • 确定了针对SARS-CoV-2的强效中和抗体,并阐明了与年龄相关的抗体对流感疫苗接种反应的差异,特别是在年轻人中.

结论:

  • AntiFormer提供了一种强大的计算方法,以加速抗体的发现和开发.
  • 这些发现强调了大型克隆型类别在亲和力成熟和免疫调节中的重要性.
  • AntiFormer为治疗策略,疫苗开发和了解免疫反应动态提供了宝贵的见解.