Snake Toxin and Antivenom Binding Profiles – a novel tool for the investigation of interaction patterns and antivenom cross-reactivity

Background Snakebite envenoming is a major neglected tropical disease that affects millions of people every year. The only effective treatment against snakebite envenoming consists of unspecified cocktails of polyclonal antibodies purified from the plasma of immunized production animals. Currently, little data exists on the molecular interactions between venom toxin epitopes and antivenom antibody paratopes. To address this issue, high-density peptide microarray (hdpm) technology has recently been adapted to the field of toxinology. However, analysis of such valuable datasets requires expert understanding and, thus, complicates its broad application within the field. Results In the present study, we developed a user-friendly, and high-throughput web application named “Snake Toxin and Antivenom Binding Profiles” (STAB Profiles), to allow straight-forward analysis of hdpm datasets. To test our tool and evaluate its performance with a large dataset, we conducted hdpm assays using all African snake toxin protein sequences available in the UniProt database at the time of study design, together with eight commercial antivenoms in clinical use in Africa, thus representing the largest venom-antivenom dataset to date. Furthermore, we introduced a novel method for evaluating raw signals from a peptide microarray experiment and a data normalization protocol enabling inter-microarray and even intra-microarray chip comparisons. Finally, these data, alongside all the data from previous similar studies by Engmark et al., were preprocessed according to our newly developed protocol and made publicly available for download through the STAB Profiles web application (https://snake.shinyapps.io/STAB_Profiles/). With these data and our tool, we were able to gain key insights into toxin-antivenom interactions and were able to differentiate the ability of different antivenoms to interact with certain toxins of interest. Conclusions The data, as well as the web application, we present in this article should be of significant value to the venom-antivenom research community. Insights gained from our current and future analyses of this dataset carry the potential to guide the improvement and optimization of current antivenoms for maximum patient benefit, as well as aid the development of next generation antivenoms.背景 蛇咬伤中毒是一种重大的被忽视热带病，每年影响数百万人。目前针对蛇咬伤中毒的唯一有效治疗手段，是从免疫生产动物血浆中纯化得到的多克隆抗体（polyclonal antibody）混合制剂，但具体组分未明确。当前，关于蛇毒毒素表位（epitope）与抗蛇毒血清抗体互补位（paratope）之间的分子相互作用，相关数据仍十分匮乏。为解决这一问题，高密度肽微阵列（high-density peptide microarray, HDPM）技术近来被应用于毒素学领域。然而，对这类高价值数据集的分析需要专业知识储备，因此限制了其在该领域的广泛推广应用。 结果 本研究开发了一款操作简便、高通量的网络应用程序，命名为「蛇毒毒素与抗蛇毒血清结合谱」（Snake Toxin and Antivenom Binding Profiles，简称STAB Profiles），以实现高密度肽微阵列数据集的直观分析。为验证该工具并通过大型数据集评估其性能，本研究在研究设计阶段，利用通用蛋白质数据库（UniProt）中收录的所有非洲蛇毒毒素蛋白质序列，结合非洲临床常用的8种商用抗蛇毒血清，开展了高密度肽微阵列实验，由此构建了目前规模最大的蛇毒-抗蛇毒血清数据集。此外，本研究提出了一种全新的肽微阵列实验原始信号评估方法，以及一套数据标准化流程，可实现微阵列间乃至同一块微阵列内不同芯片的信号对比。最后，本研究依据新开发的标准化流程，对本次实验数据以及Engmark等人此前同类研究的全部数据进行了预处理，并通过STAB Profiles网络应用程序公开提供下载（链接：https://snake.shinyapps.io/STAB_Profiles/）。借助该数据集与本工具，本研究得以深入解析毒素与抗蛇毒血清的相互作用机制，并可区分不同抗蛇毒血清与特定目标毒素的结合能力差异。 结论 本文所公布的数据集与网络应用程序，对蛇毒-抗蛇毒血清研究领域具有重要的科研价值。通过对本数据集当前及未来的分析所获得的研究结论，有望指导现有抗蛇毒血清的改良与优化，以最大化患者获益，同时也可为新一代抗蛇毒血清的研发提供助力。