Computational Strategies for Broad Spectrum Venom Phospholipase A2 Inhibitors

Snakebite envenoming is a persistent cause of mortality and morbidity worldwide due to the logistical challenges and costs of current antibody-based treatments. Their persistence motivates a broad interest in the discovery of inhibitors against multispecies venom phospholipase A2 (PLA2), which are underway as an alternative or supplemental treatment to improve health outcomes. Here, we present new computational strategies for improved inhibitor classification for challenging metalloenzyme targets across many species, including both a new method to utilize existing molecular docking, and subsequent data normalization. These methods were improved to support experimental screening efforts estimating the broader efficacy of candidate PLA2 inhibitors against diverse viper and elapid venoms.

蛇咬伤中毒（Snakebite envenoming）是全球范围内持续引发死亡与致残的重要病因，当前基于抗体的治疗手段受限于后勤保障难题与高昂成本。这一现状催生了针对多物种蛇毒磷脂酶A2（phospholipase A2，PLA2）抑制剂研发的广泛兴趣，相关研究正作为替代或辅助治疗手段推进，以期改善健康结局。本研究提出了全新的计算策略，用于优化多物种挑战性金属酶靶点的抑制剂分类任务，其中包含一种利用现有分子对接（molecular docking）的新方法，以及后续的数据标准化（data normalization）流程。上述方法经优化后可支撑实验筛选工作，用以评估候选PLA2抑制剂针对多种蝰科与眼镜蛇科蛇毒的广谱药效。