Dorota Latek

Action of Molecular Switches in GPCRs - Theoretical and Experimental Studies

G protein coupled receptors (GPCRs), also called 7TM receptors, form a huge superfamily of membrane proteins that, upon activation by extracellular agonists, pass the signal to the cell interior. Ligands can bind either to extracellular N-terminus and loops (e.g. glutamate receptors) or to the binding site within transmembrane helices (Rhodopsin-like family). They are all activated by agonists although a spontaneous auto-activation of an empty receptor can also be observed. Biochemical and crystallographic methods together with molecular dynamics simulations and other theoretical techniques provided models of the receptor activation based on the action of so-called “molecular switches” buried in the receptor structure. They are changed by agonists but also by inverse agonists evoking an ensemble of activation states leading toward different activation pathways. Switches discovered so far include the ionic lock switch, the 3-7 lock switch, the tyrosine toggle switch linked with the nPxxy motif in TM7, and the transmission switch. The latter one was proposed instead of the tryptophan rotamer toggle switch because no change of the rotamer was observed in structures of activated receptors. The global toggle switch suggested earlier consisting of a vertical rigid motion of TM6, seems also to be implausible based on the recent crystal structures of GPCRs with agonists. Theoretical and experimental methods (crystallography, NMR, specific spectroscopic methods like FRET/BRET but also single-molecule-force-spectroscopy) are currently used to study the effect of ligands on the receptor structure, location of stable structural segments/domains of GPCRs, and to answer the still open question on how ligands are binding: either via ensemble of conformational receptor states or rather via induced fit mechanisms. On the other hand the structural investigations of homo- and heterodimers and higher oligomers revealed the mechanism of allosteric signal transmission and receptor activation that could lead to design highly effective and selective allosteric or ago-allosteric drugs.

Towards improved quality of GPCR models by usage of multiple templates and profile-profile comparison.

G-protein coupled receptors (GPCRs) are targets of nearly one third of the drugs at the current pharmaceutical market. Despite their importance in many cellular processes the crystal structures are available for less than 20 unique GPCRs of the Rhodopsin-like class. Fortunately, even though involved in different signaling cascades, this large group of membrane proteins has preserved a uniform structure comprising seven transmembrane helices that allows quite reliable comparative modeling. Nevertheless, low sequence similarity between the GPCR family members is still a serious obstacle not only in template selection but also in providing theoretical models of acceptable quality. An additional level of difficulty is the prediction of kinks and bulges in transmembrane helices. Usage of multiple templates and generation of alignments based on sequence profiles may increase the rate of success in difficult cases of comparative modeling in which the sequence similarity between GPCRs is exceptionally low. Here, we present GPCRM, a novel method for fast and accurate generation of GPCR models using averaging of multiple template structures and profile-profile comparison. In particular, GPCRM is the first GPCR structure predictor incorporating two distinct loop modeling techniques: Modeller and Rosetta together with the filtering of models based on the Z-coordinate. We tested our approach on all unique GPCR structures determined to date and report its performance in comparison with other computational methods targeting the Rhodopsin-like class. We also provide a database of precomputed GPCR models of the human receptors from that class. Availability GPCRM server and database: http://gpcrm.biomodellab.eu

The Role of Water in Activation Mechanism of Human N-Formyl Peptide Receptor 1 (FPR1) Based on Molecular Dynamics Simulations

The Formyl Peptide Receptor 1 (FPR1) is an important chemotaxis receptor involved in various aspects of host defense and inflammatory processes. We constructed a model of FPR1 using as a novel template the chemokine receptor CXCR4 from the same branch of the phylogenetic tree of G-protein-coupled receptors. The previously employed template of rhodopsin contained a bulge at the extracellular part of TM2 which directly influenced binding of ligands. We also conducted molecular dynamics (MD) simulations of FPR1 in the apo form as well as in a form complexed with the agonist fMLF and the antagonist tBocMLF in the model membrane. During all MD simulation of the fMLF-FPR1 complex a water molecule transiently bridged the hydrogen bond between W2546.48 and N1083.35 in the middle of the receptor. We also observed a change in the cytoplasmic part of FPR1 of a rotamer of the Y3017.53 residue (tyrosine rotamer switch). This effect facilitated movement of more water molecules toward the receptor center. Such rotamer of Y3017.53 was not observed in any crystal structures of GPCRs which can suggest that this state is temporarily formed to pass the water molecules during the activation process. The presence of a distance between agonist and residues R2015.38 and R2055.42 on helix TM5 may suggest that the activation of FPR1 is similar to the activation of β-adrenergic receptors since their agonists are separated from serine residues on helix TM5. The removal of water molecules bridging these interactions in FPR1 can result in shrinking of the binding site during activation similarly to the shrinking observed in β-ARs. The number of GPCR crystal structures with agonists is still scarce so the designing of new ligands with agonistic properties is hampered, therefore homology modeling and docking can provide suitable models. Additionally, the MD simulations can be beneficial to outline the mechanisms of receptor activation and the agonist/antagonist sensing.

Accounting for Conformational Variability in Protein-Ligand Docking with NMR-Guided Rescoring

A key component to success in structure-based drug design is reliable information on protein-ligand interactions. Recent development in NMR techniques has accelerated this process by overcoming some of the limitations of X-ray crystallography and computational protein-ligand docking. In this work we present a new scoring protocol based on NMR-derived interligand INPHARMA NOEs to guide the selection of computationally generated docking modes. We demonstrate the performance in a range of scenarios, encompassing traditionally difficult cases such as docking to homology models and ligand dependent domain rearrangements. Ambiguities associated with sparse experimental information are lifted by searching a consensus solution based on simultaneously fitting multiple ligand pairs. This study provides a previously unexplored integration between molecular modeling and experimental data, in which interligand NOEs represent the key element in the rescoring algorithm. The presented protocol should be widely applicable for protein-ligand docking also in a different context from drug design and highlights the important role of NMR-based approaches to describe intermolecular ligand-receptor interactions.

Protein structure prediction: Combining de novo modeling with sparse experimental data

Routine structure prediction of new folds is still a challenging task for computational biology. The challenge is not only in the proper determination of overall fold but also in building models of acceptable resolution, useful for modeling the drug interactions and protein–protein complexes. In this work we propose and test a comprehensive approach to protein structure modeling supported by sparse, and relatively easy to obtain, experimental data. We focus on chemical shift‐based restraints from NMR, although other sparse restraints could be easily included. In particular, we demonstrate that combining the typical NMR software with artificial intelligence‐based prediction of secondary structure enhances significantly the accuracy of the restraints for molecular modeling. The computational procedure is based on the reduced representation approach implemented in the CABS modeling software, which proved to be a versatile tool for protein structure prediction during the CASP (CASP stands for critical assessment of techniques for protein structure prediction) experiments (see http://predictioncenter/CASP6/org). The method is successfully tested on a small set of representative globular proteins of different size and topology, including the two CASP6 targets, for which the required NMR data already exist. The method is implemented in a semi‐automated pipeline applicable to a large scale structural annotation of genomic data. Here, we limit the computations to relatively small set. This enabled, without a loss of generality, a detailed discussion of various factors determining accuracy of the proposed approach to the protein structure prediction.

Lipid Receptor S1P1 Activation Scheme Concluded from Microsecond All-Atom Molecular Dynamics Simulations

Sphingosine 1-phosphate (S1P) is a lysophospholipid mediator which activates G protein–coupled sphingosine 1-phosphate receptors and thus evokes a variety of cell and tissue responses including lymphocyte trafficking, endothelial development, integrity, and maturation. We performed five all-atom 700 ns molecular dynamics simulations of the sphingosine 1-phosphate receptor 1 (S1P1) based on recently released crystal structure of that receptor with an antagonist. We found that the initial movements of amino acid residues occurred in the area of highly conserved W2696.48 in TM6 which is close to the ligand binding location. Those residues located in the central part of the receptor and adjacent to kinks of TM helices comprise of a transmission switch. Side chains movements of those residues were coupled to the movements of water molecules inside the receptor which helped in the gradual opening of intracellular part of the receptor. The most stable parts of the protein were helices TM1 and TM2, while the largest movement was observed for TM7, possibly due to the short intracellular part starting with a helix kink at P7.50, which might be the first helix to move at the intracellular side. We show for the first time the detailed view of the concerted action of the transmission switch and Trp (W6.48) rotamer toggle switch leading to redirection of water molecules flow in the central part of the receptor. That event is a prerequisite for subsequent changes in intracellular part of the receptor involving water influx and opening of the receptor structure.

Modeling of ligand binding to G protein coupled receptors: cannabinoid CB1, CB2 and adrenergic β2AR

AbstractCannabinoid and adrenergic receptors belong to the class A (similar to rhodopsin) G protein coupled receptors. Docking of agonists and antagonists to CB1 and CB2 cannabinoid receptors revealed the importance of a centrally located rotamer toggle switch and its possible participation in the mechanism of agonist/antagonist recognition. The switch is composed of two residues, F3.36 and W6.48, located on opposite transmembrane helices TM3 and TM6 in the central part of the membranous domain of cannabinoid receptors. The CB1 and CB2 receptor models were constructed based on the adenosine A2A receptor template. The two best scored conformations of each receptor were used for the docking procedure. In all poses (ligand-receptor conformations) characterized by the lowest ligand-receptor intermolecular energy and free energy of binding the ligand type matched the state of the rotamer toggle switch: antagonists maintained an inactive state of the switch, whereas agonists changed it. In case of agonists of β2AR, the (R,R) and (S,S) stereoisomers of fenoterol, the molecular dynamics simulations provided evidence of different binding modes while preserving the same average position of ligands in the binding site. The (S,S) isomer was much more labile in the binding site and only one stable hydrogen bond was created. Such dynamical binding modes may also be valid for ligands of cannabinoid receptors because of the hydrophobic nature of their ligand-receptor interactions. However, only very long molecular dynamics simulations could verify the validity of such binding modes and how they affect the process of activation. FigureThe rotamer toggle switch in cannabinoid receptors is comprised of two residues, F3.36 and W6.48, which are located on transmembrane helices TM3 and TM6. Docking of agonists and antagonists to CB1 and CB2 cannabinoid receptors revealed the importance of this centrally located switch and its possible participation in the mechanism of agonist/antagonist sensing. The best scored poses (ligand-receptor conformations) were obtained for the ligands matching the switch state: antagonists maintained the state of the rotamer toggle switch, whereas agonists changed it

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

BackgroundSeveral different methods for contact prediction succeeded within the Sixth Critical Assessment of Techniques for Protein Structure Prediction (CASP6). The most relevant were non-local contact predictions for targets from the most difficult categories: fold recognition-analogy and new fold. Such contacts could provide valuable structural information in case a template structure cannot be found in the PDB.ResultsWe described comprehensive tests of the effectiveness of contact data in various aspects of de novo modeling with CABS, an algorithm which was used successfully in CASP6 by the Kolinski-Bujnicki group. We used the predicted contacts in a simple scoring function for the post-simulation ranking of protein models and as a soft bias in the folding simulations and in the fold-refinement procedure. The latter approach turned out to be the most successful. The CABS force field used in the Replica Exchange Monte Carlo simulations cooperated with the true contacts and discriminated the false ones, which resulted in an improvement of the majority of Kolinski-Bujnickis protein models. In the modeling we tested different sets of predicted contact data submitted to the CASP6 server. According to our results, the best performing were the contacts with the accuracy balanced with the coverage, obtained either from the best two predictors only or by a consensus from as many predictors as possible.ConclusionOur tests have shown that theoretically predicted contacts can be very beneficial for protein structure prediction. Depending on the protein modeling method, a contact data set applied should be prepared with differently balanced coverage and accuracy of predicted contacts. Namely, high coverage of contact data is important for the model ranking and high accuracy for the folding simulations.

Understanding the inhibitory effect of highly potent and selective archazolides binding to the vacuolar ATPase.

Vacuolar ATPases are a potential therapeutic target because of their involvement in a variety of severe diseases such as osteoporosis or cancer. Archazolide A (1) and related analogs have been previously identified as selective inhibitors of V-ATPases with potency down to the subnanomolar range. Herein we report on the determination of the ligand binding mode by a combination of molecular docking, molecular dynamics simulations, and biochemical experiments, resulting in a sound model for the inhibitory mechanism of this class of putative anticancer agents. The binding site of archazolides was confirmed to be located in the equatorial region of the membrane-embedded V(O)-rotor, as recently proposed on the basis of site-directed mutagenesis. Quantification of the bioactivity of a series of archazolide derivatives, together with the docking-derived binding mode of archazolides to the V-ATPase, revealed favorable ligand profiles, which can guide the development of a simplified archazolide analog with potential therapeutic relevance.

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

The recent GPCR Dock 2013 assessment of serotonin receptor 5-HT1B and 5-HT2B, and smoothened receptor SMO targets, exposed the strengths and weaknesses of the currently used computational approaches. The test cases of 5-HT1B and 5-HT2B 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-HT2B 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-HT1B and 5-HT2B-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.