Abstract
INTRODUCTION: Several studies have explored the design of antimicrobial peptides (AMPs) for the development of therapeutic and diagnostic molecules for the treatment and identification of pathogenic diseases as well as cancer. Human cadherin-1 protein has been identified to be involved in adhesion-mediated signalling pathways in normal cells and its loss through genetic and epigenetic alterations can result in an enhanced invasion and metastasis of malignancy in tumours. Therefore, the identification of cadherin during treatment of cancer can be used as prognostic biomarker to establish the responsiveness of patients to treatment regimen. Antimicrobial peptides (AMPs) offer several compensatory advantages in biomedical applications and have been used for treatment of diseases, dietary supplements and diagnosis of diseases. The aim of this research work was to use in silico approaches to analyse retrieved human cadherin-1 as prognostic targets in cancer treatments using modelled putative anticancer AMPs. METHODS: The structures of the putative AMPs and cadherin-1 were modelled using I-TASSER server and the protein overall quality was validated using PROCHECK. Thereafter, the protein motifs were predicted and the molecular interaction between the putative anticancer AMPs and protein was carried out using PatchDock. RESULTS: The results revealed that all the AMPs were good prognostic molecules for cancer with BOO1 having the highest binding affinity of 15,874. CONCLUSION: This study revealed that all the generated AMPs have good prognostic value for monitoring the progress of cancer treatment using human cadherin-1 as receptor. This is the first report where AMPs were used in prognostics of cancer using human cadherin-1.