Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
J Immunol. 2024 Jan 15;212(2):235-243. doi: 10.4049/jimmunol.2300492.
Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.
抗体是多功能分子，具有与靶抗原实现卓越结合的潜力，同时还具有适合治疗药物开发的生物物理特性。蛋白质展示和定向进化系统改变了合成抗体的发现、工程和优化，大大增加了能够进行实验筛选的抗体克隆数量。此外，高通量筛选、深度测序和机器学习的蓬勃发展进一步增强了体外抗体优化，有望加速设计过程并大规模扩展所研究的抗体序列空间。在这篇简短的综述中，我们讨论了用于合成抗体工程和优化的实验和计算工具。我们还探讨了为传染病开发抗体所带来的治疗挑战，以及利用机器学习指导的蛋白质工程有希望前瞻性地设计对病毒逃逸具有抗性的抗体的前景。
