Computational Approaches in Molecular and Systems Pharmacology: Application to Neurosignaling Membrane Proteins
Chang Liu · D-Scholarship@Pitt (University of Pittsburgh) · 2014
Computer-aided drug discovery methods have played a major role in the development of therapeutically important molecules for decades, and some more advanced and effective methods have been introduced in recent years. Those methods are generally classified as either molecular pharmacology methods or quantitative systems pharmacology methods. In this thesis, with regard to molecular pharmacology computations, we assess the druggability of ionotropic glutamate receptors (iGluRs) N-terminal domains (NTDs) using molecular dynamics (MD) simulations. The simulations are performed in the presence of probe molecules that contain fragments shared by drug-like molecules. iGluRs are ligand-gated ion channels that mediate excitatory neurotransmission events in the central nervous system. Alterations in those receptors, especially in AMPA receptors (AMPARs) and NMDA receptors (NMDARs), are responsible for many neuron diseases like Huntington’s diseases and Parkinson’s diseases. Our study provides insights into the ligand-binding landscape of iGluR NTD dimers and monomers. Moreover, we build PMs for AMPARs and NMDARs, which are then used in a virtual screening scheme to identify lead compounds. Our quantitative systems pharmacology studies focus on drug repurposing upon computational analysis of known drug-target interactions. We use the probabilistic matrix factorization (PMF) method for this purpose, which is particularly useful for analyzing large interaction networks. Our method is shown to outperform those recently introduced for identifying new drug-target associations. Finally, we integrate the results from our druggability simulations and PMF calculations by comparing the drug candidates predicted to bind AMPARs or NMDARs by either of those methods. In addition, we analyzed the structure and dynamics of sodium-coupled neurotransmitter transporters that share the leucine transporter (LeuT) fold. We explore how the collective motions predicted for LeuT using the elastic network models agree with the structural changes experimentally observed upon ligand binding.