Alejandro Giorgetti

Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation.

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers.

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.

Chemosensorial G-proteins-Coupled Receptors: A Perspective from Computational Methods

G-protein coupled receptors (GPCRs) constitute the targets of about 40 % of all the pharmaceutical drugs in the market and, among other functions, a large portion of the family detects odorants and a variety of tastant molecules. Computational techniques are instrumental to understand structure, dynamics and function of the cascades triggered by these receptors. As an example, here we report our own computational work aimed to dissect GPCR molecular mechanisms for chemical senses. The implications of our work for systems biology and for pharmacology are discussed.

Protein Aggregation and Molecular Crowding

Cells are extremely crowded environments, thus the use of diluted salted aqueous solutions containing a single protein is too simplistic to mimic the real situation. Macromolecular crowding might affect protein structure, folding, shape, conformational stability, binding of small molecules, enzymatic activity, interactions with cognate biomolecules, and pathological aggregation. The latter phenomenon typically leads to the formation of amyloid fibrils that are linked to several lethal neurodegenerative diseases, but that can also play a functional role in certain organisms. The majority of molecular simulations performed before the last few years were conducted in diluted solutions and were restricted both in the timescales and in the system dimensions by the available computational resources. In recent years, several computational solutions were developed to get close to physiological conditions. In this review we summarize the main computational techniques used to tackle the issue of protein aggregation both in a diluted and in a crowded environment.

Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins.

G protein coupled receptors (GPCRs) and intrinsic disordered proteins (IDPs) are key players for neuronal function and dysfunction. Unfortunately, their structural characterization is lacking in most cases. From one hand, no experimental structure has been determined for the two largest GPCRs subfamilies, both key proteins in neuronal pathways. These are the odorant (450 members out of 900 human GPCRs) and the bitter taste receptors (25 members) subfamilies. On the other hand, also IDPs structural characterization is highly non-trivial. They exist as dynamic, highly flexible structural ensembles that undergo conformational conversions on a wide range of timescales, spanning from picoseconds to milliseconds. Computational methods may be of great help to characterize these neuronal proteins. Here we review recent progress from our lab and other groups to develop and apply in silico methods for structural predictions of these highly relevant, fascinating and challenging systems.

Allosteric sodium binding cavity in GPR3: a novel player in modulation of Aβ production

The orphan G-protein coupled receptor 3 (GPR3) belongs to class A G-protein coupled receptors (GPCRs) and is highly expressed in central nervous system neurons. Among other functions, it is likely associated with neuron differentiation and maturation. Recently, GPR3 has also been linked to the production of Aβ peptides in neurons. Unfortunately, the lack of experimental structural information for this receptor hampers a deep characterization of its function. Here, using an in-silico and in-vitro combined approach, we describe, for the first time, structural characteristics of GPR3 receptor underlying its function: the agonist binding site and the allosteric sodium binding cavity. We identified and validated by alanine-scanning mutagenesis the role of three functionally relevant residues: Cys2676.55, Phe1203.36 and Asp2.50. The latter, when mutated into alanine, completely abolished the constitutive and agonist-stimulated adenylate cyclase activity of GPR3 receptor by disrupting its sodium binding cavity. Interestingly, this is correlated with a decrease in Aβ production in a model cell line. Taken together, these results suggest an important role of the allosteric sodium binding site for GPR3 activity and open a possible avenue for the modulation of Aβ production in the Alzheimer’s Disease.

Orthosteric and benzodiazepine cavities of the α1β2γ2 GABAA receptor: insights from experimentally validated in silico methods

γ-aminobutyric acid-type A (GABAA) receptors mediate fast synaptic inhibition in the central nervous system of mammals. They are modulated via several sites by numerous compounds, which include GABA, benzodiazepines, ethanol, neurosteroids and anaesthetics among others. Due to their potential as targets of novel drugs, a detailed knowledge of their structure–function relationships is needed. Here, we present the model of the α1β2γ2 subtype GABAA receptor in the APO state and in complex with selected ligands, including agonists, antagonists and allosteric modulators. The model is based on the crystallographic structure of the human β3 homopentamer GABAA receptor. The complexes were refined using atomistic molecular dynamics simulations. This allowed a broad description of the binding modes and the detection of important interactions in agreement with experimental information. From the best of our knowledge, this is the only model of the α1β2γ2 GABAA receptor that represents altogether the desensitized state of the channel and comprehensively describes the interactions of ligands of the orthosteric and benzodiazepines binding sites in agreement with the available experimental data. Furthermore, it is able to explain small differences regarding the binding of a variety of chemically divergent ligands. Finally, this new model may pave the way for the design of focused experimental studies that will allow a deeper description of the receptor.

Predicting ligand binding poses for low-resolution membrane protein models: Perspectives from multiscale simulations

Membrane receptors constitute major targets for pharmaceutical intervention. Drug design efforts rely on the identification of ligand binding poses. However, the limited experimental structural information available may make this extremely challenging, especially when only low-resolution homology models are accessible. In these cases, the predictions may be improved by molecular dynamics simulation approaches. Here we review recent developments of multiscale, hybrid molecular mechanics/coarse-grained (MM/CG) methods applied to membrane proteins. In particular, we focus on our in-house MM/CG approach. It is especially tailored for G-protein coupled receptors, the largest membrane receptor family in humans. We show that our MM/CG approach is able to capture the atomistic details of the receptor/ligand binding interactions, while keeping the computational cost low by representing the protein frame and the membrane environment in a highly simplified manner. We close this review by discussing ongoing improvements and challenges of the current implementation of our MM/CG code.