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Proteins are dynamic units with conformations that are constantly changing. This can make it difficult to accurately select a drug molecule that selectively binds to an enzyme of one species rather than another, a trait very helpful in antibiotic/drug design. Prolyl tRNA Synthetase (ProRS) is an enzyme responsible for attaching proline to corresponding tRNA molecules in protein synthesis, along with regulating protein synthesis. Inhibition of a ProRS molecule in a targeted species can very effectively cure disease by stopping replication processes of that species. However, computationally finding the sites of selective recognition is quite challenging, especially for enzymes, where species-specific differences are very small. Thus, we are using an artificial intelligence–based tool combining neural networks and computational chemistry, to screen potential inhibitors of these enzymes. A deep-learning fingerprinting tool with a published protein–ligand interaction fingerprinting technique is being used along with traditional molecular dynamics simulations to identify enzyme-specific recognition features. The results of the simulations and the analysis of fingerprinting are expected to reveal distinct molecular characteristics of ligands and active-site elements that significantly influence enzyme inhibition.
