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Conserved Binding Sites

Conserved Binding Sites

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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Updated: Jan 10, 2026

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

对预测TCR-epitope绑定识别的计算方法的评估.

Yanping Lu^1,2,3, Yuyan Wang¹, Meng Xu^1,4,5

¹Guangzhou National Laboratory, Guangzhou, China.

|November 29, 2025

概括

预测T细胞受体 (TCR) -表位相互作用对于免疫学至关重要. 这项研究评估了50个模型,发现数据集质量和特征多样性显著影响TCR-epitope预测准确度.

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Published on: February 6, 2017

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

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Published on: October 30, 2015

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Last Updated: Jan 10, 2026

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

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Published on: March 25, 2014

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens

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Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

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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:

T细胞受体 (TCRs) 是适应性免疫的关键,识别特定的表位.
准确预测TCR-表皮质相互作用对于免疫学研究和治疗开发至关重要.
现有用于TCR-epitope预测的计算模型缺乏全面的性能评估.

研究的目的:

系统地评估最先进的TCR-epitope预测模型的性能.
识别影响模型准确性的因素,如数据集特征和特征包含.
为开发更强大,更普遍的预测工具提供见解.

主要方法:

使用21个不同的数据集评估了50个计算模型.
包括数十万个有约束力的TCR和762个表位.
分析了负TCR源,数据集大小和特征表示对模型性能的影响.

主要成果:

模型准确性对负TCR数据的来源非常敏感.
每个表位的更大,更多样化的TCR数据集与改进的模型性能相关.
使用多个特征的模型通常优于仅依赖于互补性决定区域3β (CDR3β) 序列的模型.
所有模型都在概括到未见的表征上表现出局限性.

结论:

选择负的TCR和数据集质量对于可靠的TCR-epitope预测至关重要.
未来的模型开发应该优先考虑多样化的特征和大型,精心策划的数据集.
独立测试对于不偏见的模型评估至关重要,特别是对新型表征的概括.