In Journal of biomolecular structure & dynamics
Drug-resistant Salmonella enteric serovar Typhi (S. Typhi) poses a vital public health issue. To overcome drug resistance issues, the development of effective drugs with novel mechanism(s) of action is required. In this regard, drug repurposing is a viable alternative approach to find novel drugs to overcome drug resistance. Therefore, a FDA-approved-drug-library containing 1930 drugs was analyzed against the dihydrofolate reductase (DHFR) of S. Typhi using deep learning regression algorithms. Initially, a total of 500 compounds were screened, followed by rescreening by molecular docking. Further, from screened compounds by molecular docking, the top eight compounds were subjected to molecular dynamics (MD) simulation. Analysis of MD simulation resulted in four potential compounds, namely; Duvelisib, Amenamevir, Lifitegrast and Nilotinib against the DHFR enzyme. During the MD simulation, these four drugs achieved good stability during the 100 ns trajectory period at 300 K. Further to know the insights of the complex's stability, we calculated RMSF, RG, SASA and interaction energy for the last 60 ns trajectory period because all complexes showed the stability after 40 ns trajectory period. MM-PBSA analysis of the last 10 ns of MD trajectories showed the stability of the complexes. From our results, we conclude that these drugs can also be useful for treating typhoid fever and can inhibit S. Typhi by interfering with the function of the DHFR enzyme. Communicated by Ramaswamy H. Sarma.
Joshi Tushar, Sharma Priyanka, Joshi Tanuja, Mathpal Shalini, Pande Veena, Chandra Subhash
** Salmonella enteric serovar Typhi, FDA approved drug, deep learning, dihydrofolate reductase, drug repurposing, molecular dynamic simulation**