Integrated Machine Learning, Molecular Docking, 3D-QSAR Based Approach for Identification of Potential Inhibitors of Trypanosomal N-Myristoyltransferase - Molecular BioSystems (RSC Publishing)

N-myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking of substrate proteins to membrane locations, stabilization and protein-protein interactions. It has been studied and validated pre-clinical drug target for fungal and parasitic infections. In the present study, machine learning approach, docking studies and CoMFA analysis has been integrated with the objective of translation of knowledge into pipelined workflow towards the identification of putative hits through screening of large compound libraries. In the proposed pipeline, the reported parasitic NMT inhibitors have been used to develop predictive machine learning classification models. Simultaneously, TbNMT complex model was generated to establish relationship between binding mode of inhibitors for LmNMT and TbNMT through molecular dynamics simulation studies.