The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design.

IF 9 2区 医学 Q1 CELL BIOLOGY Journal of Biomedical Science Pub Date : 2024-03-16 DOI:10.1186/s12929-024-01018-5
Eugenio Gallo
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Abstract

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

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大数据的崛起:深度测序驱动的计算方法正在改变合成抗体设计的格局。
合成抗体(Abs)是一类能够近似天然抗体功能的人造蛋白质。它们在体外生产,无需免疫反应,简化了抗体的发现、工程和开发过程。这些人工合成的 Abs 为抗原识别、副配位位点操作和生化/生物物理增强提供了新的方法。因此,合成 Abs 正在从根本上重塑传统的 Ab 生产方法。这反映了分子生物学和基因组学因深度测序而发生的革命,深度测序可对 DNA 和 RNA 分子进行快速、低成本的大规模测序。在这一框架内,深度测序能够探索整个基因组和转录组,包括感兴趣的特定基因片段。值得注意的是,合成 Ab 发现与先进深度测序技术的融合正在重新定义当前的 Ab 设计和开发方法。这种结合提供了详尽探索抗体库的机会,可快速跟踪抗体发现过程,并加强合成抗体工程。此外,先进的计算算法还能有效挖掘大数据,帮助识别隐藏在深度测序 Ab 数据集中的 Ab 序列模式/特征。在这种情况下,可以利用这些方法预测新的序列特征,从而成功生成新的 Ab 分子。因此,合成 Ab 设计、深度测序技术和先进计算模型的融合预示着 Ab 发现将翻开新的篇章,拓宽我们对免疫学的理解,促进生物疗法的发展。
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
Journal of Biomedical Science
Journal of Biomedical Science 医学-医学:研究与实验
CiteScore
18.50
自引率
0.90%
发文量
95
审稿时长
1 months
期刊介绍: The Journal of Biomedical Science is an open access, peer-reviewed journal that focuses on fundamental and molecular aspects of basic medical sciences. It emphasizes molecular studies of biomedical problems and mechanisms. The National Science and Technology Council (NSTC), Taiwan supports the journal and covers the publication costs for accepted articles. The journal aims to provide an international platform for interdisciplinary discussions and contribute to the advancement of medicine. It benefits both readers and authors by accelerating the dissemination of research information and providing maximum access to scholarly communication. All articles published in the Journal of Biomedical Science are included in various databases such as Biological Abstracts, BIOSIS, CABI, CAS, Citebase, Current contents, DOAJ, Embase, EmBiology, and Global Health, among others.
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