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相关概念视频

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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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 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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相关实验视频

Updated: Jun 26, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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APTAnet:一个原子级别的-TCR相互作用亲和力预测模型.

Peng Xiong1, Anyi Liang1, Xunhui Cai2

  • 1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

Biophysics reports
|May 13, 2024
PubMed
概括

我们开发了APTAnet,这是预测-TCR相互作用的先进模型,对于瘤透淋巴细胞 (TIL) 免疫疗法至关重要. 这种方法有效地识别瘤和选瘤特异性T细胞受体 (TCRs).

关键词:
抗原是一种抗原.免疫治疗是一种免疫疗法.自然语言处理自然语言处理.在TCR中,可以使用TCR.转移学习转移学习

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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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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Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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科学领域:

  • 计算生物学 计算生物学
  • 免疫信息学是指免疫信息学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 预测T细胞受体 (TCR) 和亲和力对于推进瘤透淋巴细胞 (TIL) 免疫治疗至关重要.
  • 现有的药物蛋白相互作用 (DPI) 研究方法激发了-TCR相互作用 (PTI) 分析的新方法.

研究的目的:

  • 提出APTAnet,一个用于预测-TCR相互作用 (PTI) 亲和力的原子级模型.
  • 利用自然语言处理 (NLP) 技术进行增强的亲和力预测.

主要方法:

  • 开发了APTAnet,这是一个利用NLP方法进行-TCR相互作用 (PTI) 亲和力预测的原子级模型.
  • 对25675个PTI数据对进行了十倍交叉验证.
  • 在McPAS数据库和真实瘤患者数据的独立测试集上验证了模型.

主要成果:

  • 在交叉验证中,APTAnet实现了0.893的平均ROC-AUC和0.877的PR-AUC.
  • 该模型在McPAS数据库上的当前主流模型相比显示出更高的性能.
  • 在11例真实瘤患者中,APTAnet成功识别了瘤和选了瘤特异性TCR.

结论:

  • APTAnet提供了一个强大的和有效的计算方法来预测-TCR亲和力.
  • 该模型具有显著的潜力,可以改善TIL免疫疗法的开发和应用.
  • APTAnet可以帮助识别瘤特异性点和查相关的TCR,以进行个性化癌症治疗.