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宿主基因型与病毒肽的时间依赖性抗原呈递:理论预测。

Host genotype and time dependent antigen presentation of viral peptides: predictions from theory.

机构信息

Centre for Computational Science, Department of Chemistry, University College London, London, WC1H 0AJ, UK.

CoMPLEX, University College London, London, WC1E 6BT, UK.

出版信息

Sci Rep. 2017 Oct 30;7(1):14367. doi: 10.1038/s41598-017-14415-8.

Abstract

The rate of progression of HIV infected individuals to AIDS is known to vary with the genotype of the host, and is linked to their allele of human leukocyte antigen (HLA) proteins, which present protein degradation products at the cell surface to circulating T-cells. HLA alleles are associated with Gag-specific T-cell responses that are protective against progression of the disease. While Pol is the most conserved HIV sequence, its association with immune control is not as strong. To gain a more thorough quantitative understanding of the factors that contribute to immunodominance, we have constructed a model of the recognition of HIV infection by the MHC class I pathway. Our model predicts surface presentation of HIV peptides over time, demonstrates the importance of viral protein kinetics, and provides evidence of the importance of Gag peptides in the long-term control of HIV infection. Furthermore, short-term dynamics are also predicted, with simulation of virion-derived peptides suggesting that efficient processing of Gag can lead to a 50% probability of presentation within 3 hours post-infection, as observed experimentally. In conjunction with epitope prediction algorithms, this modelling approach could be used to refine experimental targets for potential T-cell vaccines, both for HIV and other viruses.

摘要

已知 HIV 感染者向艾滋病发展的速度因宿主的基因型而异,并与人类白细胞抗原 (HLA) 蛋白的等位基因有关,这些等位基因将蛋白降解产物呈现在细胞表面,供循环 T 细胞识别。HLA 等位基因与 Gag 特异性 T 细胞反应有关,这些反应可以预防疾病的进展。虽然 Pol 是 HIV 序列中最保守的部分,但它与免疫控制的关联并不强。为了更全面地了解导致免疫优势的因素,我们构建了一种 MHC Ⅰ类途径识别 HIV 感染的模型。我们的模型预测了 HIV 肽随时间在表面的呈现,证明了病毒蛋白动力学的重要性,并提供了 Gag 肽在长期控制 HIV 感染中的重要性的证据。此外,还预测了短期动力学,模拟病毒衍生肽表明,Gag 的有效加工可导致感染后 3 小时内有 50%的概率呈递,这与实验观察结果一致。结合表位预测算法,这种建模方法可用于改进针对 HIV 和其他病毒的潜在 T 细胞疫苗的实验靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00e6/5662608/7ea3c03b002c/41598_2017_14415_Fig1_HTML.jpg

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