Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States.
National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae445.
Therapeutic antibody design has garnered widespread attention, highlighting its interdisciplinary importance. Advancements in technology emphasize the critical role of designing nanobodies and humanized antibodies in antibody engineering. However, current experimental methods are costly and time-consuming. Computational approaches, while progressing, faced limitations due to insufficient structural data and the absence of a standardized protocol. To tackle these challenges, our lab previously developed IsAb1.0, an in silico antibody design protocol. Yet, IsAb1.0 lacked accuracy, had a complex procedure, and required extensive antibody bioinformation. Moreover, it overlooked nanobody and humanized antibody design, hindering therapeutic antibody development. Building upon IsAb1.0, we enhanced our design protocol with artificial intelligence methods to create IsAb2.0. IsAb2.0 utilized AlphaFold-Multimer (2.3/3.0) for accurate modeling and complex construction without templates and employed the precise FlexddG method for in silico antibody optimization. Validated through optimization of a humanized nanobody J3 (HuJ3) targeting HIV-1 gp120, IsAb2.0 predicted five mutations that can improve HuJ3-gp120 binding affinity. These predictions were confirmed by commercial software and validated through binding and neutralization assays. IsAb2.0 streamlined antibody design, offering insights into future techniques to accelerate immunotherapy development.
治疗性抗体设计受到广泛关注，突显其跨学科的重要性。技术的进步强调了在抗体工程中设计纳米抗体和人源化抗体的关键作用。然而，当前的实验方法既昂贵又耗时。尽管计算方法在不断发展，但由于结构数据不足和缺乏标准化协议，它们仍然存在局限性。为了应对这些挑战，我们实验室之前开发了 IsAb1.0，这是一种基于计算的抗体设计方案。然而，IsAb1.0 缺乏准确性，程序复杂，并且需要大量的抗体生物信息。此外，它忽略了纳米抗体和人源化抗体的设计，阻碍了治疗性抗体的开发。在 IsAb1.0 的基础上，我们利用人工智能方法增强了我们的设计方案，创建了 IsAb2.0。IsAb2.0 使用 AlphaFold-Multimer（2.3/3.0）进行准确建模和复杂构建，无需模板，并采用精确的 FlexddG 方法进行基于计算的抗体优化。通过优化针对 HIV-1 gp120 的人源化纳米抗体 J3（HuJ3）进行验证，IsAb2.0 预测了五个可以提高 HuJ3-gp120 结合亲和力的突变。这些预测得到了商业软件的证实，并通过结合和中和测定得到了验证。IsAb2.0 简化了抗体设计，为未来加速免疫疗法发展的技术提供了思路。
