Human β defensin type 2 based novel antimicrobial peptide prediction using both molecular dynamics simulation and...

The global health crisis triggered by SARS-CoV-2 has highlighted the urgent need for effective treatments. Although several anti-viral drugs targeting SARS-CoV-2 have been developed, there remains considerable interest in exploring natural antimicrobial peptides (AMPs) secreted by the human innate immune system as alternative therapeutic agents. Human β defensin type 2 (hBD-2) has been found to bind to the receptor-binding domain (RBD) of SARS-CoV-2 and block the virus entry. In order to design effective anti-virial drugs based on hBD-2, 32 block-mutants (mutation at more than one site simultaneously) were designed, and their interaction with RBD were predicted in terms of structural stability and binding energy. In combination with data from a total of 247 hBD-2 point-mutants reported in our previous work, artificial intelligence (AI) models were trained to design more efficient hBD-2 variants. With that, advanced AI approaches, including convolutional neural network (CNN) and long short-term memory (LSTM) models were employed to predict hBD-2 mutant sequences with potentially enhanced binding affinity with RBD compared with the wildtype peptide. Based on the predicted sequences, the AlphaFold program was applied to predict the structures of AMPs, and then all-atom molecular dynamics (MD) simulations were performed to calculate the binding interaction energies between each hBD-2-based AMP and the RBD as a measure of binding affinity. Structural stability was also evaluated through analyses of Root Mean Square Deviation (RMSD), Root Mean Squared Fluctuation (RMSF), the number of hydrogen bonds formed, and buried surface area. The results indicate that several AMPs exhibited more stable binding with RBD than the wildtype, highlighting their potential as novel hBD-2-derived antiviral candidates against SARS-CoV-2. Furthermore, based on the obtained results, the LSTM model demonstrated greater effectiveness than the CNN model in designing novel hBD-2-based anti-virial drugs against CoV-2.