Filsinger Interrante Maria V, Tang Shaogeng, Kim Soohyun, Shanker Varun R, Hie Brian L, Bruun Theodora U J, Wu Wesley, Pak John E, Fernandez Daniel, Kim Peter S
Stanford Biophysics Program, Stanford University School of Medicine, Stanford, California 94305, United States.
Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford California 94305, United States.
ACS Chem Biol. 2025 Jul 18;20(7):1470-1480. doi: 10.1021/acschembio.5c00035. Epub 2025 Jun 20.
The N-heptad repeat (NHR) of the HIV-1 gp41 prehairpin intermediate (PHI) is an attractive potential vaccine target with high sequence conservation across diverse strains. However, despite the potency of NHR-targeting peptides and clinical efficacy of the NHR-targeting entry inhibitor enfuvirtide, no potently neutralizing NHR-directed monoclonal antibodies (mAbs) nor antisera have been identified or elicited to date. The lack of potent NHR-binding mAbs both dampens enthusiasm for vaccine development efforts at this target and presents a barrier to performing passive immunization experiments with NHR-targeting antibodies. To address this challenge, we previously developed an improved variant of the NHR-directed mAb D5, called D5_AR, which is capable of neutralizing diverse tier-2 viruses. Building on that work, here we present the 2.7Å-crystal structure of D5_AR bound to NHR mimetic peptide IQN17. We then utilize protein language models and supervised machine learning to generate small ( < 100) libraries of D5_AR variants that are subsequently screened for improved neutralization potency. We identify a variant with 5-fold improved neutralization potency, D5_FI, which is the most potent NHR-directed monoclonal antibody characterized to date and exhibits broad neutralization of tier-2 and -3 pseudoviruses as well as replicating R5 and X4 challenge strains. Additionally, our work highlights the ability of protein language models to efficiently identify improved mAb variants from relatively small libraries.
HIV-1 gp41前发夹中间体（PHI）的N-七肽重复序列（NHR）是一个具有吸引力的潜在疫苗靶点，在不同毒株中具有高度的序列保守性。然而，尽管靶向NHR的肽具有效力，且靶向NHR的进入抑制剂恩夫韦肽具有临床疗效，但迄今为止，尚未鉴定出或诱导出具有强效中和作用的靶向NHR的单克隆抗体（mAb）或抗血清。缺乏强效结合NHR的mAb既削弱了针对该靶点进行疫苗开发工作的热情，也为使用靶向NHR的抗体进行被动免疫实验设置了障碍。为应对这一挑战，我们之前开发了一种改良版的靶向NHR的mAb D5，称为D5_AR，它能够中和多种2级病毒。在此基础上，我们展示了与NHR模拟肽IQN17结合的D5_AR的2.7Å晶体结构。然后，我们利用蛋白质语言模型和监督机器学习来生成D5_AR变体的小型（<100个）文库，随后对其进行筛选以提高中和效力。我们鉴定出一种中和效力提高了5倍的变体D5_FI，它是迄今为止表征的最有效的靶向NHR的单克隆抗体，对2级和3级假病毒以及复制型R5和X4攻击株具有广泛的中和作用。此外，我们的工作突出了蛋白质语言模型从相对较小的文库中有效鉴定改良mAb变体的能力。
