Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Elucidation of the Binding Mode of the Carboxyterminal Region of Peptide YY to the Human Y2 Receptor.

Understanding the agonist-receptor interactions in the neuropeptide Y (NPY)/peptide YY (PYY) signaling system is fundamental for the design of novel modulators of appetite regulation. We report here the results of a multidisciplinary approach to elucidate the binding mode of the native peptide agonist PYY to the human Y2 receptor, based on computational modeling, peptide chemistry and in vitro pharmacological analyses. The preserved binding orientation proposed for full-length PYY and five analogs, truncated at the amino terminus, explains our pharmacological results where truncations of the N-terminal proline helix showed little effect on peptide affinity. This was followed by receptor mutagenesis to investigate the roles of several receptor positions suggested by the modeling. As a complement, PYY-(3-36) analogs were synthesized with modifications at different positions in the common PYY/NPY C-terminal fragment (32TRQRY36-amide). The results were assessed and interpreted by molecular dynamics and Free Energy Perturbation (FEP) simulations of selected mutants, providing a detailed map of the interactions of the PYY/NPY C-terminal fragment with the transmembrane cavity of the Y2 receptor. The amidated C-terminus would be stabilized by polar interactions with Gln2886.55 and Tyr2195.39, while Gln1303.32 contributes to interactions with Q34 in the peptide and T32 is close to the tip of TM7 in the receptor. This leaves the core, α-helix of the peptide exposed to make potential interactions with the extracellular loops. This model agrees with most experimental data available for the Y2 system and can be used as a basis for optimization of Y2 receptor agonists.

Anion recognition by uranyl-salophen derivatives as probed by infrared multiple photon dissociation spectroscopy and ab initio modeling.

The vibrational features and molecular structures of complexes formed by a series of uranyl-salophen receptors with simple anions, such as Cl(-) , H(-) , and HCOO(-) , have been investigated in the gas phase. Spectra of the anionic complexes were studied in the $\tilde \nu $=800-1800 cm(-1) range by mass-selective infrared multiple photon dissociation (IRMPD) spectroscopy with a continuously tunable free-electron laser. The gas-phase decarboxylation of the formate adducts produces uranyl-salophen monohydride anions, which have been characterized for the first time and reveal a strong UH bond, the nature of which has been elucidated theoretically. The spectra are in excellent agreement with the results obtained from high-quality ab initio calculations, which provided the structure and binding features of the anion-receptor complexes.

Understanding the Structure-Activity Relationship through Density Functional Theory: A Simple Method Predicts Relative Binding Free Energies of Metalloenzyme Fragment-like Inhibitors.

Despite being involved in several human diseases, metalloenzymes are targeted by a small percentage of FDA-approved drugs. Development of novel and efficient inhibitors is required, as the chemical space of metal binding groups (MBGs) is currently limited to four main classes. The use of computational chemistry methods in drug discovery has gained momentum thanks to accurate estimates of binding modes and binding free energies of ligands to receptors. However, exact predictions of binding free energies in metalloenzymes are challenging due to the occurrence of nonclassical phenomena and interactions that common force field-based methods are unable to correctly describe. In this regard, we applied density functional theory (DFT) to predict the binding free energies and to understand the structure-activity relationship of metalloenzyme fragment-like inhibitors. We tested this method on a set of small-molecule inhibitors with different electronic properties and coordinating two Mn2+ ions in the binding site of the influenza RNA polymerase PAN endonuclease. We modeled the binding site using only atoms from the first coordination shell, hence reducing the computational cost. Thanks to the explicit treatment of electrons by DFT, we highlighted the main contributions to the binding free energies and the electronic features differentiating strong and weak inhibitors, achieving good qualitative correlation with the experimentally determined affinities. By introducing automated docking, we explored alternative ways to coordinate the metal centers and we identified 70% of the highest affinity inhibitors. This methodology provides a fast and predictive tool for the identification of key features of metalloenzyme MBGs, which can be useful for the design of new and efficient drugs targeting these ubiquitous proteins.

Evolution of Angiotensin Peptides and Peptidomimetics as Angiotensin II Receptor Type 2 (AT2) Receptor Agonists.

Angiotensin II receptor type 1 and 2 (AT1R and AT2R) are two G-protein coupled receptors that mediate most biological functions of the octapeptide Angiotensin II (Ang II). AT2R is upregulated upon tissue damage and its activation by selective AT2R agonists has become a promising approach in the search for new classes of pharmaceutical agents. We herein analyzed the chemical evolution of AT2R agonists starting from octapeptides, through shorter peptides and peptidomimetics to the first drug-like AT2R-selective agonist, C21, which is in Phase II clinical trials and aimed for idiopathic pulmonary fibrosis. Based on the recent crystal structures of AT1R and AT2R in complex with sarile, we identified a common binding model for a series of 11 selected AT2R agonists, consisting of peptides and peptidomimetics of different length, affinity towards AT2R and selectivity versus AT1R. Subsequent molecular dynamics simulations and free energy perturbation (FEP) calculations of binding affinities allowed the identification of the bioactive conformation and common pharmacophoric points, responsible for the key interactions with the receptor, which are maintained by the drug-like agonists. The results of this study should be helpful and facilitate the search for improved and even more potent AT2R-selective drug-like agonists.

Functional characterization in vitro of twelve naturally occurring variants of the human pancreatic polypeptide receptor NPY4R.

Obesity has become a global health problem and therefore understanding of the mechanisms regulating hunger and satiety is of utmost importance for the development of new treatment strategies. The Y4 receptor, encoded by the NPY4R gene, and its ligand pancreatic polypeptide (PP) have been reported to mediate a satiety signal. Multiple genetic studies have reported an association between NPY4R copy number and body weight. The gene also displays several SNP variants, many of which lead to amino acid differences, making it interesting to study. We have investigated the functional properties of 12 naturally occurring amino acid sequence variants of the Y4 and interpret the results in relation to sequence conservation and our structural model of the human Y4 receptor protein. Three receptor variants, Cys201ECL2Tyr, Val2716.41Leu and Asn3187.49Asp, were found to completely lose functional response, measured as inositol phosphate turnover, while retaining membrane expression. They display high sequence conservation and have important roles in the receptor structure. For two receptor variants the potency of PP was significantly decreased, Cys34NTSer (EC50 = 2.9 nM, p < .001) and Val1353.46Met (EC50 = 3.0 nM, p < .01), compared to wild-type Y4 (EC50 = 0.68 nM). Cys34 forms a disulphide bond with Cys298, linking the N-terminal part to ECL3. The Val1353.46Met variant has an amino acid replacement located in the TM3 helix, one helix turn above the highly conserved ERH motif. This position has influence on the network of residues involved in receptor activation and subsequent inactivation. Sequence conservation and the structural model are consistent with these results. The remaining seven positions had no significant effect on the receptor's functional response compared to wild-type Y4. These positions display more variation during evolution. Understanding of the interactions between the Y4 receptor and its native PP agonist and the effects of amino acid variation on its functional response will hopefully lead to future therapeutic possibilities.

QresFEP: An Automated Protocol for Free Energy Calculations of Protein Mutations in Q.

Predicting the effect of single-point mutations on protein stability or protein-ligand binding is a major challenge in computational biology. Free energy calculations constitute the most rigorous approach to this problem, though the estimation of converged values for amino acid mutations remains challenging. To overcome this limitation, we developed tailored protocols to calculate free energy shifts associated with single-point mutations. We herein describe the QresFEP protocol, which includes an extension of our recent protocols to cover all amino acids mutations, based on the latest versions of the OPLS-AA force field. QresFEP is implemented in an application programming interface framework and the graphic interface QGui, for the molecular dynamics software Q. The complete protocol is benchmarked in several model systems, optimizing a number of sampling parameters and the implementation of Zwanzig's exponential formula and Bennet's acceptance ratio methods. QresFEP shows an excellent performance on estimating the hydration free energies of amino acid side-chain mimics, including their charged analogues. We also examined its performance on a protein-ligand binding problem of pharmaceutical relevance, the antagonism of neuropeptide Y1 G protein-coupled receptor. Here, the calculations show very good agreement with the experimental effect of 16 mutations on the binding of antagonists BIBP3226, in line with our recent applications in this field. Finally, the characterization of 43 mutations of T4-lysozyme reveals the capacity of our protocol to assess variations of the thermal stability of proteins, achieving a similar performance to alternative free energy perturbation (FEP) approaches. In summary, QresFEP is a robust, versatile, and user-friendly computational FEP protocol to examine biochemical effects of single-point mutations with high accuracy.

Characterization of Ligand Binding to GPCRs Through Computational Methods.

The recent increase in available G protein-coupled receptor structures now contributes decisively to the structure-based ligand design. In this context, computational approaches in combination with medicinal chemistry and pharmacology are extremely helpful. Here, we provide an update on our structure-based computational protocols, used to answer key questions related to GPCR-ligand binding. All combined, these techniques can shed light on ligand binding modes, determine the molecular basis of conformational selection, for agonists and antagonists, as well as of subtype selectivity. To illustrate each of these questions, we will consider examples from existing projects on three families of class A (rhodopsin-like) GPCRs: one small-molecule (nucleotide-like) family, i.e., the adenosine receptors, and two peptide-binding receptors: neuropeptide-Y and angiotensin II receptors. The successful application of the same computational protocols to investigate this diverse group of receptor families gives an idea of the general applicability of our methodology in the characterization of GPCR-ligand binding.