Computational study on the origin of the cancer immunotherapeutic potential of B and T cell epitope peptides†

IF 3.743 Q2 Biochemistry, Genetics and Molecular Biology Molecular BioSystems Pub Date : 2017-09-07 DOI:10.1039/C7MB00219J
Hao Li, Nalini Schaduangrat, Saw Simeon and Chanin Nantasenamat
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引用次数: 2

Abstract

Immune therapy is generally seen as the future of cancer treatment. The discovery of tumor-associated antigens and cytotoxic T lymphocyte epitope peptides spurned intensive research into effective peptide-based cancer vaccines. One of the major obstacles hindering the development of peptide-based cancer vaccines is the lack of humoral response induction. As of now, very limited work has been performed to identify epitope peptides capable of inducing both cellular and humoral anticancer responses. In addition, no research has been carried out to analyze the structure and properties of peptides responsible for such immunological activities. This study utilizes a machine learning method together with interpretable descriptors in an attempt to identify parameters determining the immunotherapeutic activity of cancer epitope peptides.

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B细胞和T细胞表位肽肿瘤免疫治疗潜能起源的计算研究
免疫疗法通常被视为癌症治疗的未来。肿瘤相关抗原和细胞毒性T淋巴细胞表位肽的发现,推动了对有效的基于肽的癌症疫苗的深入研究。阻碍以肽为基础的癌症疫苗发展的主要障碍之一是缺乏体液反应诱导。到目前为止,鉴定能够诱导细胞和体液抗癌反应的表位肽的工作非常有限。此外,还没有研究分析负责这种免疫活性的肽的结构和性质。本研究利用机器学习方法和可解释的描述符,试图识别确定癌症表位肽免疫治疗活性的参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular BioSystems
Molecular BioSystems 生物-生化与分子生物学
CiteScore
2.94
自引率
0.00%
发文量
0
审稿时长
2.6 months
期刊介绍: Molecular Omics publishes molecular level experimental and bioinformatics research in the -omics sciences, including genomics, proteomics, transcriptomics and metabolomics. We will also welcome multidisciplinary papers presenting studies combining different types of omics, or the interface of omics and other fields such as systems biology or chemical biology.
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