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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大数据的兴起：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) 代表一类能够密切模拟天然抗体功能的人工蛋白质。它们的体外生产消除了对免疫反应的需要，简化了抗体发现、工程和开发的过程。这些人工设计的抗体提供了抗原识别、互补位点操作和生物化学/生物物理增强的新方法。因此，合成抗体正在从根本上重塑传统的抗体生产方法。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，它允许对 DNA 和 RNA 分子进行快速且经济高效的大规模测序。在此框架内，深度测序使我们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，合成抗体发现与先进深度测序技术的融合正在重新定义当前的抗体设计和开发方法。这种组合提供了彻底探索抗体库、快速跟踪抗体发现过程和增强合成抗体工程的机会。此外，先进的计算算法能够有效挖掘大数据，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，这些方法可用于预测新的序列特征，从而能够成功生成从头抗体分子。因此，合成抗体设计、深度测序技术和先进计算模型的融合预示着抗体发现的新篇章，拓宽了我们对免疫学的理解并简化了生物治疗的进步。