Krishnan Sowmya Ramaswamy, Sharma Divya, Nazeer Yasin, Bose Mayilvahanan, Rajkumar Thangarajan, Jayaraman Guhan, Madaboosi Narayanan, Gromiha M Michael
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
Department of Molecular Oncology, Cancer Institute (WIA), Adyar, Chennai 600020, India.
Antib Ther. 2024 Jul 8;7(3):256-265. doi: 10.1093/abt/tbae018. eCollection 2024 Jul.
Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis. The process of antibody engineering involves complex and time-consuming laboratory techniques, which demand substantial resources and expertise. The success rate of obtaining desired antibody candidates through experimental approaches can be modest, necessitating iterative cycles of selection and optimization. With ongoing advancements in technology, design of diverse antibody libraries, screening and identification of potential candidates for validation can be accelerated. To meet this need, we have developed rAbDesFlow, a unified computational workflow for recombinant antibody engineering with open-source programs and tools for ease of implementation. The workflow encompasses five computational modules to perform antigen selection, antibody library generation, antigen and antibody structure modeling, antigen-antibody interaction modeling, structure analysis, and consensus ranking of potential antibody sequences for synthesis and experimental validation. The proposed workflow has been demonstrated through design of rAbs for the ovarian cancer antigen Mucin-16 (CA-125). This approach can serve as a blueprint for designing similar engineered molecules targeting other biomarkers, allowing for a simplified adaptation to different cancer types or disease-specific antigens.
重组抗体（rAbs）已成为一种很有前景的解决方案，可用于解决抗原特异性、增强免疫原性潜力以及用于治疗人类疾病的多功能功能化问题。单链可变片段的发展有助于加速癌症和病毒感染的治疗，这得益于其良好的药代动力学和人体兼容性。然而，传统上设计重组抗体被视为一个基因工程问题，噬菌体展示和无细胞系统在基因合成的序列选择中起主要作用。抗体工程过程涉及复杂且耗时的实验室技术，需要大量资源和专业知识。通过实验方法获得所需抗体候选物的成功率可能不高，这就需要进行反复的选择和优化循环。随着技术的不断进步，多样化抗体文库的设计、潜在候选物的筛选和鉴定以进行验证的过程可以加快。为满足这一需求，我们开发了rAbDesFlow，这是一种用于重组抗体工程的统一计算工作流程，它使用开源程序和工具以便于实施。该工作流程包括五个计算模块，用于进行抗原选择、抗体文库生成、抗原和抗体结构建模、抗原-抗体相互作用建模、结构分析以及对潜在抗体序列进行合成和实验验证的一致性排序。通过设计针对卵巢癌抗原粘蛋白-16（CA-125）的重组抗体，已证明了所提出的工作流程。这种方法可作为设计针对其他生物标志物的类似工程分子的蓝图，从而可以简化对不同癌症类型或疾病特异性抗原的适应性。
