Due to the involvement of G protein-coupled receptors (GPCRs) in most of the physiological and pathological processes in humans they have been attracting a lot of attention from pharmaceutical industry as well as from scientific community. Therefore, the need for new, high quality structures of GPCRs is enormous. The updated homology modeling service GPCRM (http://gpcrm.biomodellab.eu/) meets those expectations by greatly reducing the execution time of submissions (from days to hours/minutes) with nearly the same average quality of obtained models. Additionally, due to three different scoring functions (Rosetta, Rosetta-MP, BCL::Score) it is possible to select accurate models for the required purposes: the structure of the binding site, the transmembrane domain or the overall shape of the receptor. Currently, no other web service for GPCR modeling provides this possibility. GPCRM is continually upgraded in a semi-automatic way and the number of template structures has increased from 20 in 2013 to over 90 including structures the same receptor with different ligands which can influence the structure not only in the on/off manner. Two types of protein viewers can be used for visual inspection of obtained models. The extended sortable tables with available templates provide links to external databases and display ligand–receptor interactions in visual form.

Aquaporins superfamily of hydrophobic integral membrane proteins constitute water channels essential to the movement of water across the cell membrane maintaining homeostatic equilibrium. During the passage of water between the extracellular and intracellular sides of the cell, aquaporins act as ultra-sensitive filters. Due to their hydrophobic nature, aquaporins self-assemble in phospholipids and if a proper choice of lipids are made then the aquaporin biomimetic membrane can be used in the design of artificial kidney. In combination with graphene, aquaporin biomimetic membrane finds practical application in desalination and water recycling using mostly E.coli AqpZ. Recently, human aquaporin 1 has emerged as an important biomarker in renal cell carcinoma. At present the ultrasensitive sensing of renal cell carcinoma is cumbersome and hence we are discussing usage of epitopes from monoclonal antibody as a probe for Point-ofCare device for sensing renal cell carcinoma by immobilizing the antibody on the surface of a single layer graphene as a microfluidic device for sensing renal cell carcinoma which is pursued in our laboratories.

It has been reported that some hydrophobic ligands of G-protein-coupled receptors access the receptor's binding site from the membrane rather than from bulk water. In order to identify the most probable ligand entrance pathway into the CB1 receptor, we performed several steered molecular dynamics (SMD) simulations of two CB1 agonists, THC and anandamide, pulling them from the receptor's binding site with constant velocity. The four main directions of ligand pulling were probed: between helices TM4 and TM5, between TM5 and TM6, between TM7 and TM1/TM2, and toward the bulk water. The smallest forces were measured during pulling between TM7 and TM1/TM2. We also performed supervised molecular dynamics (SuMD) simulations for both anandamide and THC entering the CB1 receptor's binding site and found the same pathway as in the pulling simulations. The residues F174 and F177 (both on the TM2 helix) are involved in the gating mechanism and, by forming π-π interactions with ligand molecules, facilitated the ligand orientation required for passage. Using SuMD we also found an alternative binding site for THC. The results of mutagenesis studies evidencing that residues F174 and F177 are important for CB1 ligand binding are in agreement with our observations.

The CB1 cannabinoid receptor (CB1R) contains one of the longest N termini among class A G protein-coupled receptors. Mutagenesis studies suggest that the allosteric binding site of cannabidiol (CBD) involves residues from the N terminal domain. In order to study the allosteric binding of CBD to CB1R we modeled the whole N-terminus of this receptor using the replica exchange molecular dynamics with solute tempering (REST2) approach. Then, the obtained structures of CB1R with the N terminus were used for ligand docking. A natural cannabinoid receptor agonist, Δ9-THC, was docked to the orthosteric site and a negative allosteric modulator, CBD, to the allosteric site positioned between extracellular ends of helices TM1 and TM2. The molecular dynamics simulations were then performed for CB1R with ligands: (i) CBD together with THC, and (ii) THC-only. Analyses of the differences in the residue-residue interaction patterns between those two cases allowed us to elucidate the allosteric network responsible for the modulation of the CB1R by CBD. In addition, we identified the changes in the orthosteric binding mode of Δ9-THC, as well as the changes in its binding energy, caused by the CBD allosteric binding. We have also found that the presence of a complete N-terminal domain is essential for a stable binding of CBD in the allosteric site of CB1R as well as for the allosteric-orthosteric coupling mechanism.

Predicting the functional preferences of the ligands was always a highly demanding task, much harder that predicting whether a ligand can bind to the receptor. This is because of significant similarities of agonists, antagonists and inverse agonists which are binding usually in the same binding site of the receptor and only small structural changes can push receptor toward a particular activation state. For G protein-coupled receptors, due to a large progress in crystallization techniques and also in receptor thermal stabilization, it was possible to obtain a large number of high-quality structures of complexes of these receptors with agonists and non-agonists. Additionally, the long-time-scale molecular dynamics simulations revealed how the activation processes of GPCRs can take place. Using both theoretical and experimental knowledge it was possible to employ many clever and sophisticated methods which can help to differentiate agonists and non-agonists, so one can interconvert them in search of the optimal drug.