Improving antibody optimization ability of generative adversarial network through large language model

• Published in: Computational and structural biotechnology journal Vol. 21; pp. 5839 - 5850
• Main Authors: Zhao, Wenbin, Luo, Xiaowei, Tong, Fan, Zheng, Xiangwen, Li, Jing, Zhao, Guangyu, Zhao, Dongsheng
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 2023; Elsevier
• Subjects: Antibody optimization; Generative Adversarial Network; Language model; Antibody optimization; Language model; Generative Adversarial Network
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2023.11.041

Generative adversarial networks (GANs) have successfully generated functional protein sequences. However, traditional GANs often suffer from inherent randomness, resulting in a lower probability of obtaining desirable sequences. Due to the high cost of wet-lab experiments, the main goal of computer-aided antibody optimization is to identify high-quality candidate antibodies from a large range of possibilities, yet improving the ability of GANs to generate these desired antibodies is a challenge. In this study, we propose and evaluate a new GAN called the Language Model Guided Antibody Generative Adversarial Network (AbGAN-LMG). This GAN uses a language model as an input, harnessing such models’ powerful representational capabilities to improve the GAN’s generation of high-quality antibodies. We conducted a comprehensive evaluation of the antibody libraries and sequences generated by AbGAN-LMG for COVID-19 (SARS-CoV-2) and Middle East Respiratory Syndrome (MERS-CoV). Results indicate that AbGAN-LMG has learned the fundamental characteristics of antibodies and that it improved the diversity of the generated libraries. Additionally, when generating sequences using AZD-8895 as the target antibody for optimization, over 50% of the generated sequences exhibited better developability than AZD-8895 itself. Through molecular docking, we identified 70 antibodies that demonstrated higher affinity for the wild-type receptor-binding domain (RBD) of SARS-CoV-2 compared to AZD-8895. In conclusion, AbGAN-LMG demonstrates that language models used in conjunction with GANs can enable the generation of higher-quality libraries and candidate sequences, thereby improving the efficiency of antibody optimization. AbGAN-LMG is available at http://39.102.71.224:88/. [Display omitted]