Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Institute for Advanced Study, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Nat Commun. 2019 May 6;10(1):2073. doi: 10.1038/s41467-019-09819-1.
Isolation of broadly neutralizing human monoclonal antibodies (HmAbs) targeting the E2 glycoprotein of Hepatitis C virus (HCV) has sparked hope for effective vaccine development. Nonetheless, escape mutations have been reported. Ideally, a potent vaccine should elicit HmAbs that target regions of E2 that are most difficult to escape. Here, aimed at addressing this challenge, we develop a predictive in-silico evolutionary model for E2 that identifies one such region, a specific antigenic domain, making it an attractive target for a robust antibody response. Specific broadly neutralizing HmAbs that appear difficult to escape from are also identified. By providing a framework for identifying vulnerable regions of E2 and for assessing the potency of specific antibodies, our results can aid the rational design of an effective prophylactic HCV vaccine.
分离针对丙型肝炎病毒 (HCV) E2 糖蛋白的广泛中和人源单克隆抗体 (HmAbs) 为有效疫苗的开发带来了希望。然而,已经报道了逃逸突变。理想情况下,有效的疫苗应该诱导针对 E2 中最难以逃逸的区域的 HmAbs。在这里,为了解决这一挑战,我们开发了一种针对 E2 的预测性计算进化模型,该模型确定了一个这样的区域,即一个特定的抗原结构域,使其成为产生强大抗体反应的有吸引力的靶标。还鉴定了一些似乎难以逃避的特定广泛中和的 HmAbs。通过为识别 E2 的脆弱区域和评估特定抗体的效力提供框架,我们的结果可以帮助合理设计有效的预防性 HCV 疫苗。