» Articles » PMID: 35062669

Prediction of Novel Drug Targets and Vaccine Candidates Against Human Lice (Insecta), Acari (Arachnida), and Their Associated Pathogens

Overview
Date 2022 Jan 22
PMID 35062669
Authors
Affiliations
Soon will be listed here.
Abstract

The emergence of drug-resistant lice, acari, and their associated pathogens (APs) is associated with economic losses; thus, it is essential to find new appropriate therapeutic approaches. In the present study, a subtractive proteomics approach was used to predict suitable therapeutics against these vectors and their infectious agents. We found 9701 proteins in the lice ( var. ) and acari (, ), and 4822 proteins in the proteomes of their APs (, , , , , str. Boryong) that were non-homologous to host proteins. Among these non-homologous proteins, 365 proteins of lice and acari, and 630 proteins of APs, were predicted as essential proteins. Twelve unique essential proteins were predicted to be involved in four unique metabolic pathways of lice and acari, and 103 unique proteins were found to be involved in 75 unique metabolic pathways of APs. The sub cellular localization analysis of 115 unique essential proteins of lice and acari and their APs revealed that 61 proteins were cytoplasmic, 42 as membrane-bound proteins and 12 proteins with multiple localization. The druggability analysis of the identified 73 cytoplasmic and multiple localization essential proteins revealed 22 druggable targets and 51 novel drug targets that participate in unique pathways of lice and acari and their APs. Further, the predicted 42 membrane bound proteins could be potential vaccine candidates. Screening of useful inhibitors against these novel targets may result in finding novel compounds efficient for the control of these parasites.

Citing Articles

Prediction of potential drug targets and key inhibitors (ZINC67974679, ZINC67982856, and ZINC05668040) against using integrated computational approaches.

Rahman S, Liu H, Shah M, Almutairi M, Liaqat I, Tanaka T Front Vet Sci. 2025; 11:1507496.

PMID: 39885844 PMC: 11780677. DOI: 10.3389/fvets.2024.1507496.


Targeting Yezo Virus Structural Proteins for Multi-Epitope Vaccine Design Using Immunoinformatics Approach.

Rahman S, Chiou C, Almutairi M, Ajmal A, Batool S, Javed B Viruses. 2024; 16(9).

PMID: 39339884 PMC: 11437474. DOI: 10.3390/v16091408.


Subtractive Proteomics and Reverse-Vaccinology Approaches for Novel Drug Target Identification and Chimeric Vaccine Development against Strain Houston-1.

Rahman S, Chiou C, Ahmad S, Islam Z, Tanaka T, Alouffi A Bioengineering (Basel). 2024; 11(5).

PMID: 38790371 PMC: 11118080. DOI: 10.3390/bioengineering11050505.


In silico identification of drug targets and vaccine candidates against Bartonella quintana: a subtractive proteomics approach.

Ahmad S, Verli H Mem Inst Oswaldo Cruz. 2024; 119:e230040.

PMID: 38655925 PMC: 11034861. DOI: 10.1590/0074-02760230040.


Rabbits as Animal Models for Anti-Tick Vaccine Development: A Global Scenario.

Rodriguez-Duran A, Ullah S, Parizi L, Ali A, Vaz Junior I Pathogens. 2023; 12(9).

PMID: 37764925 PMC: 10536012. DOI: 10.3390/pathogens12091117.


References
1.
Onyiche T, Raileanu C, Fischer S, Silaghi C . Global Distribution of Species in Questing Ticks: A Systematic Review and Meta-Analysis Based on Published Literature. Pathogens. 2021; 10(2). PMC: 7926846. DOI: 10.3390/pathogens10020230. View

2.
Xu D, Zhang Y . Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011; 101(10):2525-34. PMC: 3218324. DOI: 10.1016/j.bpj.2011.10.024. View

3.
Oscherwitz J . The promise and challenge of epitope-focused vaccines. Hum Vaccin Immunother. 2016; 12(8):2113-2116. PMC: 4994726. DOI: 10.1080/21645515.2016.1160977. View

4.
Moriya Y, Itoh M, Okuda S, Yoshizawa A, Kanehisa M . KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007; 35(Web Server issue):W182-5. PMC: 1933193. DOI: 10.1093/nar/gkm321. View

5.
Kushwaha S, Shakya M . Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis. J Theor Biol. 2009; 262(2):284-94. DOI: 10.1016/j.jtbi.2009.09.029. View