Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning.

Autor:	Goodswen SJ; School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia., Kennedy PJ; School of Computer Science, Faculty of Engineering and Information Technology and the Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia., Ellis JT; School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia.
Jazyk:	angličtina
Zdroj:	Frontiers in genetics [Front Genet] 2021 Jul 23; Vol. 12, pp. 716132. Date of Electronic Publication: 2021 Jul 23 (Print Publication: 2021).
DOI:	10.3389/fgene.2021.716132
Abstrakt:	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 Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina , and Babesia divergens. 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 Babesia bovis 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 B. bovis, B. bigemina , and two other haemoprotozoan species, Babesia canis , and Plasmodium falciparum. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Goodswen, Kennedy and Ellis.)
Databáze:	MEDLINE
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