Background: Host-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases.

Objective: Many studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose.

Methods: The various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed.

Result: We discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information.

Conclusion: This review presents useful knowledge and future directions on the subject matter.

背景：宿主-寄生虫蛋白相互作用 (HPPI) 是寄生虫与其宿主之间发生的相互作用。宿主-寄生虫蛋白相互作用增强了对寄生虫如何感染宿主的理解。尽管并非所有宿主-寄生虫相互作用都会导致感染，但这种相互作用在引发感染方面起着重要作用。识别允许寄生虫感染其宿主的蛋白质-蛋白质相互作用 (PPI) 在发现可能的药物靶点方面有很大的作用。这种 PPI 在改变时会防止宿主被寄生虫感染，在某些情况下，会导致寄生虫无法完成其生命周期的特定阶段，并且总是会导致这种寄生虫死亡。

目的：文献中已经进行了许多预测 HPPI 的研究，主要是使用计算方法，很少有实验方法。计算方法已被证明在处理和分析现实生活数据方面更快、更有效。本研究着眼于文献中用于宿主-寄生虫/物种间蛋白质-蛋白质相互作用预测的各种计算方法，希望能够更好地了解所使用的计算方法并确定机器学习方法是否已广泛用于相同目的。

方法：回顾了涉及宿主-寄生虫蛋白相互作用的各种方法，并根据它们各自的优势进行了综述。进行了进行宿主-寄生虫/物种间蛋白质相互作用预测的研究列表，分析了它们的预测方法、使用的过滤器、在这些研究中发现的潜在蛋白质-蛋白质相互作用以及视情况使用的各种验证测量。突出显示此类研究的常用测量指标，显示各种公式。最后，提出了针对人类恶性疟原虫 PPI 预测的研究的未来前景。

结果：我们发现，与序列同源性搜索、蛋白质结构和域基序等方法相比，相当多的研究回顾了用于 HPPI 预测的机器学习方法。HPPI 预测中明确指出的关键挑战是获取相关信息。

结论：本综述介绍了有关该主题的有用知识和未来方向。