Chlamydia trachomatis, a gram-negative bacterium known to infect the genital sites mainly columnar epithelial cells of the cervix, urethra and rectum in women and causes acute epididymitis, urinary tract inflammation and DNA damage to the sperms in men, hence considered to be one of the major sexually transmitted infections. The infection is asymptomatic in many people and remains untreated leading to serious health complications, including pelvic inflammatory disease, ectopic pregnancy and infertility. The current treatment options include antibiotics, but the pathogen has gained resistance against many antibiotics. The present work involves an in silico reverse vaccinology approach for identifying the immunogens as vaccine candidates that can be effective against reinfections and should be capable of inducing long-term protective immunity against Chlamydial infections. This study identifies the putative vaccine candidates that are membrane bound with high antigenicity properties; antigenicity induces the immunogenicity which involves identification of T-cell and B-cell epitopes that induce both humoral and cell-mediated immunity. The epitopes ‘LSWEMELAY’, ‘LSNTEGYRY’, ‘TSDLGQMEY’, ‘FIDLLQAIY’ and ‘FSNNFSDIY’ were predicted as core sequences for class I MHC molecules. The identified epitopes showed promising ability to interact with the human leukocyte antigens (HLA). These epitopes showed maximum population coverage with epitope conservancy above 80%. Molecular docking was performed to test the binding affinities of the identified epitopes with the HLA molecule to study the binding cleft interactions. The vaccine candidate thus identified from this study showed to possess the potential to activate the B- and T-cell immune responses which are more specific and make the body stronger against infections and effective for reinfections.
In silico vaccine design against Chlamydia trachomatis infection
Shilpa Shiragannavar,Shivakumar B. Madagi,Joy H Hosakeri,Vandana Barot
Published 2020 in Network Modeling Analysis in Health Informatics and Bioinformatics
ABSTRACT
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- Publication year
2020
- Venue
Network Modeling Analysis in Health Informatics and Bioinformatics
- Publication date
2020-06-10
- Fields of study
Biology, Medicine, Computer Science
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- External record
- Source metadata
Semantic Scholar, PubMed
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