Combined Ligand and Structure based studies to Modulate L-type Amino Acid Transporter 1 in Cancer

Abstract

The hallmark of cancerous cells is chronic proliferation for which they rely heavily on nutrients like essential amino acids. Membrane transporters strictly control the uptake of essential amino

acids across the cell membrane. Among many membrane transporters, LAT1 (SLC7A5), an L- type amino acid transporter, has been frequently reported overexpressed in a wide range of

malignancies. Many studies confirm that LAT1 modulation inhibits protein synthesis in cancer cells by downregulation of the mTORC1 signaling pathway and by the activation of General Amino Acid Control (GAAC) pathway. LAT1 is thus a potential molecular target for cancer diagnostics and treatment. This study aims to explore LAT1 as a potential drug target against variety of cancers and helps in the identification of most important features of LAT1 inhibitors. For this purpose, approaches like MD simulation has been used for the structural modeling of LAT1. Inhibitors data against LAT1 is collected from through literature study and chembl database, which leads to database of 72 inhibitors against LAT1. Most stable 3D binding conformation of the target protein after MD simulation was used for 3D Molecular modeling and predictive modeling. The docking experiments have been used to probe the best binding conformation of the ligands with the target protein and to formulate a binding hypothesis. To further investigate our binding hypothesis, pose analysis was performed which leads to the discovery of some important protein-ligand interactions. To validate this hypothesis, MD simulation of some ligand complexes was performed to evaluate the ligand-protein interaction profiles and to evaluate protein residues responsible for binding highly active compounds towards target protein such as Ser66, Lys204, Tyr259 and Phe252. The most stable complex after the MD simulations was selected as a template for the pharmacophore query building. The model was developed with the accuracy of 95% having one hydrogen bond donor (Ser66), and three hydrophobic features (Tyr259, Lys204, and Phe252), which might have the ability to inhibit LAT1 in variety of cancers. The LAT1 inhibitors dataset was used for screening the pharmacophore model. The resultant hits proposed that our model can differentiate between active and inactive compounds with up to 95% accuracy. In this research work, we outline recent breakthroughs in our understanding of LAT1's role in cancer, as well as preclinical studies. Because of LAT1 inhibitors' unique mode of action, it could help treat several cancers that are

16 resistant to conventional treatments, whether alone or in combination with other anti-tumor medications.