HOMOLOGY MODELING AND IN SILICO STUDY OF LISTERIOLYSIN O AS NSCLC VACCINE CANDIDATE

• Main Author: Amelia, Tasia
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/78781
• Institution: Institut Teknologi Bandung

Lung cancer is the most commonly diagnosed and also the most common cause of death in 2012. Non-Small Cell Lung Carcinoma (NSCLC), the subtype of lung cancer, is being diagnosed in approximately 85% patients, and overexpressed Epidermal Growth Factor (EGF) receptor is the main cause. However, EGF receptor inhibitor drug used by patients is ineffective due to the drug resistance against EGF receptor, thus it leads to low survival rate of NSCLC patients. Therefore, it would require a development of cancer treatments, such as cancer vaccine. After being tested in vivo and phase I clinical trial, it was found that listeriolysin O of L. monocytogenes is a potential pathogen for use as cancer vaccine. The purpose of the present study was to design peptides structures by homology modelling, to test these peptides as potentiakcancer vaccine candidate by in silico, and to predict the stability of the receptor and complex by molecular dynamics simulations. Epitope selection of listeriolysin O was performed using NetCTL@ VI .2 and IEDB , and it was then selected based on antigenicity score of VaxiJen . Three-dimensional model of epitops were designed using Modeller Rosetta , and I-TASSER@. The model was validated by ProSA@, QMEAN@, and MolProbity , and minimized using NAMD@. Stability test was done against EGF receptor T790M-L858R using NAMD for 30 ns at 310 K, and the receptor complexed with gefitinib and epitope using GROMACS for 10 ns at 300 K. Structures that have been validated was docked to H-2Kb and EGF receptors using DOCK@ v6. 13 epitopes was generated, and 9 epitopes mutant was used. Thus, 22 epitopes was obtained to be used for this study. The docking result showed that the epitopes were capable to bind with high affinity to MHC class I so that it can stimulate immune response. Epitopes also have a better affinity than gefitinib in wild-type and mutant EGF receptors. Epitope 7 and- la have the highest affinity to receptors by hidrogen bonding at the same amino acid residues as natural ligand. Three-dimensional models of 22 epitopes met all validation criteria of ProSA@, QMEAN@, Ramachandran plot, and MolProbity . All of the epitopes have higher affinity than natural ligands against H-2Kb and EGF receptors. Epitope 7 and I la is the most potential candidate to be lung cancer vaccine as both prophylactic and therapeutic. Molecular dynamics simulations showed that the receptor and complexes is stable under certain environment.