关于异质子图的多模式学习和LLMs对MHC-结系亲和力预测的表示.
Ruimeng Li1, Ying Wang1, Haozhou Li1
1Faculty of Electronic and Information Engineering, The Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, 710049, China.
BMC bioinformatics
|March 1, 2026
概括
预测MHC-的结合亲和力对于免疫治疗至关重要. 我们新的基于对比学习的多特征异构子图模型 (CMHS) 通过整合序列和结构数据来提高预测准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 免疫信息学是指免疫信息学.
- 机器学习 机器学习
背景情况:
- 准确预测主要基因相容综合体 (MHC) - 结亲和力对于开发有效的免疫疗法至关重要.
- 当前的计算方法在同时建模功能语义,进化约束和多态残留物的结构动态方面面临着挑战.
- 需要先进的模型来捕捉MHC分子和之间的复杂相互作用.
研究的目的:
- 开发一种新的计算模型,基于对比学习的多特征异构子图模型 (CMHS),用于增强的MHC-结合亲和力预测.
- 整合各种数据表示,包括序列和结构信息,以更全面地了解MHC-相互作用.
- 在预测超变性免疫相互作用方面建立一个新的基准.
主要方法:
- 使用ESM2和BLOSUM50的LoRA微调用于MHC专属的序列表示,捕获功能依赖和保存的残留物.
- 采用生物物理引导的异质图形网络进行结构表示,并采用由晶体学B因子引导的新型可训练的高斯噪声层.
- 实现了三阶段的消息传递框架,其中包含子图的聚合和提取,然后进行对比学习来对齐序列和图形表示空间.
主要成果:
- 在CMHS模型中,在16个HLA等位基准中,预测准确度显著提高.
- 与现有方法相比,获得了8.7%的平均斯皮尔曼等级相关系数 (SRCC) 改进.
- 平均曲线下的面积 (AUC) 改善了7.6%,这表明预测性能有所提高.
结论:
- CMHS模型代表了预测超变性免疫相互作用的新范式,特别是MHC-结合亲和力.
- 通过对比学习整合序列和结构数据显著提高了预测能力.
- 这种方法为通过改进的计算建模推进免疫治疗开发提供了一个有希望的方向.
相关概念视频
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
299
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
299
02:40Ligand Binding Sites
15.4K
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...
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...
15.4K
00:49Ligand Binding and Linkage
5.7K
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...
5.7K
00:49Ligand Binding and Linkage
4.2K
4.2K