THE ARTIFICIAL NEURAL NETWORK SELECTS SACCHARIDES FROM NATURAL SOURCES A PROMISE FOR POTENTIAL FIMH INHIBITOR TO PREVENT UTI INFECTIONS

Abstract

The major challenge in the development of affordable medicines from natural sources is the unavailability of logical protocols to explain their mechanism of action in biological targets. FimH (Type 1 fimbrin with D-mannose specific adhesion property), a lectin on E. coli cell surface is a promising target to combat the urinary tract infection (UTI). The present study aimed at predicting the inhibitory capacity of saccharides on FimH. As mannosides are considered FimH inhibitors, the readily accessible saccharides from the PubChem collection were utilized. The artificial neural networks (ANN)-based machine learning algorithm Self-organizing map (SOM) has been successfully employed in predicting active molecules as they could discover relationships through self-organization for the ligand-based virtual screening. Docking was used for the structure-based virtual screening and molecular dynamic simulation for validation. The result revealed that the predicted molecules malonyl hexose and mannosyl glucosyl glycerate exhibit exactly similar binding interactions and better docking scores as that of the reference bioassay active, heptyl mannose. The pharmacokinetic profile matches that of the selected bioflavonoids (quercetin malonyl hexose, kaempferol malonyl hexose) and has better values than the control drug bioflavonoid, monoxerutin. Thus, these two molecules can effectively inhibit type 1 fimbrial adhesin, as antibiotics against E. coli and can be explored as a prophylactic against UTIs. Moreover, this investigation can pave the way to the exploration of the potential benefits of plant-based treatments.