DataSheet_1_Drug Repositioning For Allosteric Modulation of VIP and PACAP Receptors.pdf

Vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating polypeptide (PACAP) are two neuropeptides that contribute to the regulation of intestinal motility and secretion, exocrine and endocrine secretions, and homeostasis of the immune system. Their biological effects are mediated by three receptors named VPAC1, VPAC2 and PAC1 that belong to class B GPCRs. VIP and PACAP receptors have been identified as potential therapeutic targets for the treatment of chronic inflammation, neurodegenerative diseases and cancer. However, pharmacological use of endogenous ligands for these receptors is limited by their lack of specificity (PACAP binds with high affinity to VPAC1, VPAC2 and PAC1 receptors while VIP recognizes both VPAC1 and VPAC2 receptors), their poor oral bioavailability (VIP and PACAP are 27- to 38-amino acid peptides) and their short half-life. Therefore, the development of non-peptidic small molecules or specific stabilized peptidic ligands is of high interest. Structural similarities between VIP and PACAP receptors are major causes of difficulties in the design of efficient and selective compounds that could be used as therapeutics. In this study we performed structure-based virtual screening against the subset of the ZINC15 drug library. This drug repositioning screen provided new applications for a known drug: ticagrelor, a P2Y12 purinergic receptor antagonist. Ticagrelor inhibits both VPAC1 and VPAC2 receptors which was confirmed in VIP-binding and calcium mobilization assays. A following analysis of detailed ticagrelor binding modes to all three VIP and PACAP receptors with molecular dynamics revealed its allosteric mechanism of action. Using a validated homology model of inactive VPAC1 and a recently released cryo-EM structure of active VPAC1 we described how ticagrelor could block conformational changes in the region of ‘tyrosine toggle switch’ required for the receptor activation. We also discuss possible modifications of ticagrelor comparing other P2Y12 antagonist – cangrelor, closely related to ticagrelor but not active for VPAC1/VPAC2. This comparison with inactive cangrelor could lead to further improvement of the ticagrelor activity and selectivity for VIP and PACAP receptor sub-types.</p

Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models-4

E scoring function was based on the contact data set from the best two predictors (Baker and PROFcon). The accuracy of the contact data used for scoring 5 models of each target is plotted against the RMSD of the model ranked as first in CASP6 by the Kolinski-Bujnicki group (green squares) and against the RMSD of the protein model ranked as first by the contact-based scoring function (red lines which join corresponding points). Results for NF and FR/A categories are presented separately. The most significant improvement is observed in the case of FR/A targets with the accuracy of the contact prediction range of 15–30%. In a similar way the results of the refinement simulations are presented in the right-hand panels (b). The refinement simulations performed better than the post-simulation ranking of the models.<p><b>Copyright information:</b></p><p>Taken from "Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models"</p><p>http://www.biomedcentral.com/1472-6807/8/36</p><p>BMC Structural Biology 2008;8():36-36.</p><p>Published online 11 Aug 2008</p><p>PMCID:PMC2527566.</p><p></p

Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models-1

Cts provided by the Baker group and PROFcon, was plotted as a function of GDT-TS. Although most of wrong or low quality models (with GDT-TS < 40) could be discarded by the contacts based scoring function, it seems inevitable to use some additional discriminating tools for assessing models with GDT-TS > 40.<p><b>Copyright information:</b></p><p>Taken from "Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models"</p><p>http://www.biomedcentral.com/1472-6807/8/36</p><p>BMC Structural Biology 2008;8():36-36.</p><p>Published online 11 Aug 2008</p><p>PMCID:PMC2527566.</p><p></p

Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models-5

Cts provided by the Baker group and PROFcon, was plotted as a function of GDT-TS. Although most of wrong or low quality models (with GDT-TS < 40) could be discarded by the contacts based scoring function, it seems inevitable to use some additional discriminating tools for assessing models with GDT-TS > 40.<p><b>Copyright information:</b></p><p>Taken from "Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models"</p><p>http://www.biomedcentral.com/1472-6807/8/36</p><p>BMC Structural Biology 2008;8():36-36.</p><p>Published online 11 Aug 2008</p><p>PMCID:PMC2527566.</p><p></p

Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models-3

Ed on the native structure (RMSD = 2.3 Å) and by the contact map with depicted real and quite accurate and precisely predicted contacts (upper triangle) and contacts of the best model obtained in the folding simulation and the first model of the Kolinski-Bujnicki group (lower triangle). Significant improvement of model quality and its contact map with respect to the native is observed. Results of the refinement simulations (b) are represented by the best model of the T0215 target (green) superimposed on the native structure (blue) (RMSD = 5.5 Å) and by the contact map constructed in the same fashion as in (a). Despite the low quality of the contact data predicted for the T0215 target the quality of the final refined model improved (but not significantly) in comparison to the original Kolinski-Bujnicki results (RMSD = 7.9 Å from the crystallographic structure Cα-trace).<p><b>Copyright information:</b></p><p>Taken from "Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models"</p><p>http://www.biomedcentral.com/1472-6807/8/36</p><p>BMC Structural Biology 2008;8():36-36.</p><p>Published online 11 Aug 2008</p><p>PMCID:PMC2527566.</p><p></p

Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models-0

E scoring function was based on the contact data set from the best two predictors (Baker and PROFcon). The accuracy of the contact data used for scoring 5 models of each target is plotted against the RMSD of the model ranked as first in CASP6 by the Kolinski-Bujnicki group (green squares) and against the RMSD of the protein model ranked as first by the contact-based scoring function (red lines which join corresponding points). Results for NF and FR/A categories are presented separately. The most significant improvement is observed in the case of FR/A targets with the accuracy of the contact prediction range of 15–30%. In a similar way the results of the refinement simulations are presented in the right-hand panels (b). The refinement simulations performed better than the post-simulation ranking of the models.<p><b>Copyright information:</b></p><p>Taken from "Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models"</p><p>http://www.biomedcentral.com/1472-6807/8/36</p><p>BMC Structural Biology 2008;8():36-36.</p><p>Published online 11 Aug 2008</p><p>PMCID:PMC2527566.</p><p></p

Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models-2

H contact map real (red) and predicted (green) contacts are compared. In the bottom triangle a contact map of Kolinski-Bujnicki's first model (blue) is superposed on a contact map of the final model obtained after the refinement simulations (grey). In most cases we observed improvement of the contact maps for models after the refinement. Some accurate contacts were rebuilt by the CABS despite not being preliminarily predicted (T0209-2). Some falsely predicted contacts in diffused clusters were not observed in the final model (T0281). Predicted contacts in dense and numerous clusters were observed almost in all cases (A and A' in the T0272-1 contact map), contrary to diffuse sparse contact clusters. (b) Lower triangles, contact maps of the T0272-1 models obtained after the simulations with restraints based on the data sets (upper triangles) with either the A or B group of contacts modified. (1) Reduction of the influence of restraints based on the A group of contacts on the simulation with respect to the original contact data in (a) by diffusing these contacts. Intensification of the effect of B contact-based restraints by increasing the number of these contacts (2) and by increasing the scaling factor in the restraint potential corresponding to these contacts(3).<p><b>Copyright information:</b></p><p>Taken from "Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models"</p><p>http://www.biomedcentral.com/1472-6807/8/36</p><p>BMC Structural Biology 2008;8():36-36.</p><p>Published online 11 Aug 2008</p><p>PMCID:PMC2527566.</p><p></p

A Hybrid Approach to Structure and Function Modeling of G Protein-Coupled Receptors

The recent GPCR Dock 2013 assessment of serotonin receptor 5-HT_1B and 5-HT_2B, and smoothened receptor SMO targets, exposed the strengths and weaknesses of the currently used computational approaches. The test cases of 5-HT_1B and 5-HT_2B demonstrated that both the receptor structure and the ligand binding mode can be predicted with the atomic-detail accuracy, as long as the target–template sequence similarity is relatively high. On the other hand, the observation of a low target–template sequence similarity, e.g., between SMO from the frizzled GPCR family and members of the rhodopsin family, hampers the GPCR structure prediction and ligand docking. Indeed, in GPCR Dock 2013, accurate prediction of the SMO target was still beyond the capabilities of most research groups. Another bottleneck in the current GPCR research, as demonstrated by the 5-HT_2B target, is the reliable prediction of global conformational changes induced by activation of GPCRs. In this work, we report details of our protocol used during GPCR Dock 2013. Our structure prediction and ligand docking protocol was especially successful in the case of 5-HT_1B and 5-HT_2B-ergotamine complexes for which we provide one of the most accurate predictions. In addition to a description of the GPCR Dock 2013 results, we propose a novel hybrid computational methodology to improve GPCR structure and function prediction. This computational methodology employs two separate rankings for filtering GPCR models. The first ranking is ligand-based while the second is based on the scoring scheme of the recently published BCL method. In this work, we prove that the use of knowledge-based potentials implemented in BCL is an efficient way to cope with major bottlenecks in the GPCR structure prediction. Thereby, we also demonstrate that the knowledge-based potentials for membrane proteins were significantly improved, because of the recent surge in available experimental structures

A gradient contact map for inactive C5aR1.

Distances were computed between Cα atoms of the receptor from every tenth ns of the 1.5 μ MD simulation started from the crystal structure of the inactive conformation of C5aR1. (GIF)</p

A gradient contact map for active C5aR1.

Distances were computed between Cα atoms of the receptor and the Gα subunit from every tenth ns of the 1.5 μ MD simulation started from the active conformation of C5aR1 based on FPR2. (GIF)</p