In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis

Abstract

Background Leprosy is caused by Mycobacterium leprae and Mycobacterium lepromatosis. Most of the affected population lives in low-income countries and may take up to 10 years to show any clinical signs, which is how physicians diagnose it. However, due to progressive cell damage, early diagnosis is very important. The best way to confirm leprosy is through bacilloscopic, which only confirms the diagnosis and has low accuracy or PCR, that requires specialized operators and is expensive. Since the bacteria are fastidious and do not grow in any culture media, therefore, diagnosing leprosy in the lab is still a challenge. In this concern, a recombinant multi-epitope protein can be a beneficial strategy in the management of the diagnosis, as diverse immunogenic epitopes are precisely selected to detect specific antibodies. Therefore, the purposes of the present study were to select immunogenic epitopes from different relevant proteins, with immunogenic properties, and then to construct a recombinant multi-epitope protein that accuses the presence of the antibodies in the early stages of the disease, making it more than appropriate to be applied as a diagnostic tool. Results We selected 22 common proteins from both species and, using bioinformatics tools, predicted B and T cell epitopes. After multiple filtering and analyzing, we ended up with 29 epitopes {MHC-I (total 18) and MHC-II (total 11)} from 10 proteins, which were then merged into one construct. Its secondary and tertiary structures were also predicted and refined to comprise the amino acid residues in the best conformation possible. The multi-epitope protein construct was stable, non-host homologous, non-allergic, non-toxic, and elicit humoral and cellular responses. It has conformational B cell epitopes and potential to elicit IFN-γ, IL-4, and IL-10 secretion. Conclusions This novel recombinant multi-epitope protein constructed using the common epitopes from M. leprae and M. lepromatosis has a huge immunological potential, is stable, and can be lyophilized to be used in ELISA plates or even in biosensors, which are user-friendly diagnosis tools, facilitating translation into human sample tests.

Immunogenic mapping of rDyn-1 and rKDDR-plus proteins and selection of oligopeptides by immunoblotting for the diagnosis of Leishmania infantum-infected dogs

“…With this, it is possible to build a molecule, composed of different sequences with specific characteristics that, when used together, provide higher diagnostic accuracy than that obtained with their individual use. This approach has been gaining strength in the diagnosis of CanL with the use of chimeric antigens or antigenic mixtures composed of multiple epitopes to be used as antigens in serological tests [ 50 ].…”

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

“…To form a new RMP, the first step involves selecting the epitopes to be used. This can be performed in several ways, such as choosing epitopes that have already been characterized as immunodominant in the literature [ 9 – 11 ], and through bioinformatics analyses [ 12 – 14 ]. Bioinformatics analysis identifies a pathogen’s antigens using computational analyses of its genome, without the need to manipulate the microorganism [ 15 ].…”

“…Bioinformatics analysis identifies a pathogen’s antigens using computational analyses of its genome, without the need to manipulate the microorganism [ 15 ]. This implies a reduction in costs and research time, and minimizes the use of animals, proving to be an effective method for better targeting in in vitro and in vivo experiments [ 14 , 16 – 19 ]. As much as it is an already consolidated area, and its importance demonstrated by several studies, the importance of bioinformatics became evident during the COVID-19 pandemic, during which it was necessary to select epitopes as quickly and inexpensively as possible to develop vaccines and diagnostic tests, increasing the number of studies using these analyses [ 20 – 24 ].…”