Supplementary materials for Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data

Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of unlabeled proteins the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.

近二十年来，通过密集的疟疾控制与消除努力，疟疾的发病率已显著下降。然而，恶性疟原虫病例的减少导致某些地理区域的物种转变，非洲以外的许多地区以间日疟原虫为主。尽管间日疟原虫的地理分布广泛，但其疫苗的研发进度却远远落后于恶性疟原虫，部分原因在于无法在体外培养间日疟原虫，这阻碍了传统抗原识别方法。在先前的研究中，我们已采用正负样本随机森林（Positive-Una-labeled Random Forest，简称 PURF）机器学习方法，以识别恶性疟原虫抗原，以供疫苗研发参考。在本研究中，我们整合了恶性疟原虫（研究较为深入的一种）的系统数据，以优化 PURF 模型，预测潜在的间日疟原虫疫苗抗原候选者。此外，我们进一步表明，包含已知抗原对于模型性能至关重要，但包含其他物种的非标记蛋白质可能导致模型误导向物种分类的预测因子，而非抗原识别。在疟疾之外，纳入与密切相关的物种的抗原可能有助于新兴病原体疫苗的研发，这些病原体可能缺乏或几乎没有已知的抗原。