G-protein coupled receptors (GPCRs) play an important role in the physiology and in the pathophysiology of many serious diseases. They form the largest superfamily of drug targets. Since GPCRs are membrane bound and are highly dynamic, obtaining three dimensional structural information for GPCRs is a feat and it requires a confluence of various biophysical techniques that include computational methods. The crystal structure is a snapshot in the conformational ensemble that the receptor samples in the absence of any stimulant. We are developing multiscale simulation method suite, GPCRSimKit, that integrates coarse grain simulation method with fine grain techniques. The GPCRSimKit will enable simulation of the dynamics of GPCR conformational ensemble starting from the inactive crystal structures or refine homology models for drug design. The GPCRSimkit will allow calculation of the modulation of the potential energy landscape by full, partial, and inverse agonists. This platform of computational techniques, will lay a theoretical basis and play a crucial role as more crystal structures of GPCRs get published.
A computational method for designing thermostable mutants for GPCRs
G-protein coupled receptors are membrane proteins and play an important part in cellular signal transduction. Solving the three dimensional structures of these proteins is critical and is becoming viable lately. However the biggest bottleneck in obtaining sufficient quantities of the pure protein is that GPCRs are conformationally flexible, and hence aggregate at higher concentrations during purification. A solution to this challenge is to derive thermostable mutants of GPCRs that are amenable to purification techniques. However the experiments involved in identifying the residue positions that lead to thermostability as well as the thermostable mutants is both expensive and time consuming. There are about 300 mutations that need to be done just to be able to identify positions that lead to thermostability. Our goal is to develop a fast computational screening method, LITiConDesign to design and thermally stable mutants of several GPCRs. We will target class A GPCRs in their agonist and antagonist bound structures. This project is in collaboration with Dr. Chris Tate (MRC, Cambridge, UK) and Dr. Reinhard Grisshammer (NINDS).
Development of computational method to identify allosteric sites for drug design in protein-protein complexes
G-protein-coupled receptors (GPCRs) are membrane proteins that allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to proteins in the intracellular (IC) domain. However, the complete pathway of allosteric communication from the EC to the IC domain, including the role of individual amino acids in the pathway is not known. Using the correlation in torsion angle movements calculated from microseconds-long molecular-dynamics simulations, we have developed a computational analysis method based on graph theory to elucidate the allosteric pathways in GPCRs. This method is generic and applicable to all proteins. In addition, our analysis shows that mutations that affect the ligand efficacy, but not the binding affinity, are located in the allosteric pipelines. This clarifies the role of such mutations, which has hitherto been unexplained. The residues involved in allosteric communication can be used as “allosteric hubs” that modulate the activity of the protein. We use this information on allosteric hub residues to identify druggable allosteric binding sites in proteins. These potential binding sites can be used to screen for small molecules that act as allosteric modulators or inhibitors to protein-protein interactions.
The dynamics of G-protein coupled receptors (GPCRs) and their relevance in drug design G-protein coupled receptors belong to a superfamily of seven helical transmembrane proteins that play a critical role in many physiological processes. They are implicated in the pathology of many diseases such as atherosclerosis, cancer, auto-immune and auto-inflammatory diseases, cancer metastasis, and hence form the biggest class of drug targets. One of the major complexities in drug design for GPCRs, however, is their conformational flexibility. This dynamic flexibility leads to GPCR conformations being in equilibrium between several active and inactive conformational states. Therefore a molecular level understanding of the dynamics is vital to designing functional selective drugs for GPCRs.
Computational methods for studying the dynamics of GPCR conformations: In my laboratory, we have developed novel computational methods to map the potential energy surface and the dynamics of GPCR conformational states and use them for drug design, as seen in the figure, which shows the potential energy surface of an antagonist bound GPCR (right) and the binding site of an antagonist bound to a GPCR used for drug design (left). We have applied these techniques to design drugs for β adrenergic receptors (targets for hypertension and asthma) and chemokine receptors. Using these methods, we also design thermally stable mutant GPCRs for several class A GPCRs that would strongly aid the crystallization of these receptors.