Goodswen Stephen J, Kennedy Paul J, Ellis John T
School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia.
School of Computer Science, Faculty of Engineering and Information Technology and the Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia.
Front Genet. 2021 Jul 23;12:716132. doi: 10.3389/fgene.2021.716132. eCollection 2021.
Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus in the phylum Apicomplexa. Principal species are , and There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein's local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome proteins with an 86-92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for , and two other haemoprotozoan species, , and
牛巴贝斯虫病每年给全球肉牛和奶牛养殖业造成巨大的经济损失。它是由顶复门巴贝斯属血原生动物寄生虫感染红细胞引发的一种疾病。主要种类有牛巴贝斯虫、分歧巴贝斯虫和双芽巴贝斯虫。目前尚无亚单位疫苗。针对巴贝斯虫病的潜在治疗靶点包括输出蛋白组的成员。本研究探讨了利用蛋白质二级结构特征和机器学习算法预测输出蛋白组成员概率的新方法。该方法的前提是检测有助于区分不同蛋白质类型的特征差异。本文探索了诸如蛋白质的局部构象分类状态、主链扭转角ϕ（phi）和ψ（psi）、溶剂可及表面积、接触数和半球暴露等结构特性，将其作为潜在的区分蛋白质特征。通过机器学习利用这些结构特性的方法显示，能够以86% - 92%的准确率（基于10折交叉验证和独立测试）从非输出蛋白中检测出输出蛋白。这些方法封装在免费提供的用于自动化高通量处理的Linux管道中。此外，还为牛巴贝斯虫、分歧巴贝斯虫和双芽巴贝斯虫以及另外两种血原生动物物种提供了供实验室研究的候选治疗药物。
