Data from: Mapping polyclonal HIV-1 antibody responses via next-generation neutralization fingerprinting

Computational neutralization fingerprinting, NFP, is an efficient and accurate method for predicting the epitope specificities of polyclonal antibody responses to HIV-1 infection. Here, we present next-generation NFP algorithms that substantially improve prediction accuracy for individual donors and enable serologic analysis for entire cohorts. Specifically, we developed algorithms for: (a) selection of optimized virus neutralization panels for NFP analysis, (b) estimation of NFP prediction confidence for each serum sample, and (c) identification of sera with potentially novel epitope specificities. At the individual donor level, the next-generation NFP algorithms particularly improved the ability to detect multiple epitope specificities in a sample, as confirmed both for computationally simulated polyclonal sera and for samples from HIV-infected donors. Specifically, the next-generation NFP algorithms detected multiple specificities in twice as many samples of simulated sera. Further, unlike the first-generation NFP, the new algorithms were able to detect both of the previously confirmed antibody specificities, VRC01-like and PG9-like, in donor CHAVI 0219. At the cohort level, analysis of ~150 broadly neutralizing HIV-infected donor samples suggested a potential connection between clade of infection and types of elicited epitope specificities. Most notably, while 10E8-like antibodies were observed in infections from different clades, an enrichment of such antibodies was predicted for clade B samples. Ultimately, such large-scale analyses of antibody responses to HIV-1 infection can help guide the design of epitope-specific vaccines that are tailored to take into account the prevalence of infecting clades within a specific geographic region. Overall, the next-generation NFP technology will be an important tool for the analysis of broadly neutralizing polyclonal antibody responses against HIV-1.计算中和指纹图谱（Computational neutralization fingerprinting，NFP）是一种高效精准的方法，可用于预测人类免疫缺陷病毒1型（HIV-1）感染后多克隆抗体（polyclonal antibody）应答的表位特异性。本研究提出了新一代NFP算法，其可大幅提升个体供体的预测准确性，并支持全队列的血清学分析。具体而言，我们开发了三类算法：(a) 为NFP分析筛选优化的病毒中和检测组合；(b) 估算每份血清样本的NFP预测置信度；(c) 鉴定具有潜在新型表位特异性的血清样本。在个体供体层面，新一代NFP算法显著提升了单一样本中多种表位特异性的检测能力，该结论已通过计算模拟多克隆血清以及HIV-1感染供体的样本得到验证。具体而言，新一代NFP算法在模拟血清样本中检测到多种特异性的样本数量提升了一倍。此外，与第一代NFP不同，新算法可在供体CHAVI 0219样本中同时检测到此前已证实的两种抗体特异性：VRC01样和PG9样抗体。在队列层面，对约150份广谱中和HIV-1感染供体样本的分析表明，感染病毒亚型与诱导产生的表位特异性类型之间存在潜在关联。最值得注意的是，尽管在不同亚型的感染中均检测到10E8样抗体，但预测显示B亚型样本中这类抗体的富集程度更高。最终，这类针对HIV-1感染抗体应答的大规模分析，可助力设计表位特异性疫苗，该疫苗可针对特定地理区域内感染病毒亚型的流行特征进行定制化优化。总体而言，新一代NFP技术将成为分析抗HIV-1广谱中和多克隆抗体应答的重要工具。