Project description:The interaction between a protein and its ligands is one of the basic and most important processes in biological chemistry. Docking methods aim to predict the molecular 3D structure of protein-ligand complexes starting from coordinates of the protein and the ligand separately. They are widely used in both industry and academia, especially in the context of drug development projects. AutoDock4 is one of the most popular docking tools and, as for any docking method, its performance is highly system dependent. Knowledge about specific protein-ligand interactions on a particular target can be used to successfully overcome this limitation. Here, we describe how to apply the AutoDock Bias protocol, a simple and elegant strategy that allows users to incorporate target-specific information through a modified scoring function that biases the ligand structure towards those poses (or conformations) that establish selected interactions. We discuss two examples using different bias sources. In the first, we show how to steer dockings towards interactions derived from crystal structures of the receptor with different ligands; in the second example, we define and apply hydrophobic biases derived from Molecular Dynamics simulations in mixed solvents. Finally, we discuss general concepts of biased docking, its performance in pose prediction, and virtual screening campaigns as well as other potential applications.
Project description:Protein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the past two decades, it is still a challenging problem, characterized by the still limited understanding of the energetics between proteins and ligands, and the vast conformational space that has to be searched to find a satisfactory solution. In this project, we developed a novel reinforcement learning (RL) approach, the asynchronous advantage actor-critic model (A3C), to address the protein ligand docking problem. The overall framework consists of two models. During the search process, the agent takes an action selected by the actor model based on the current location. The critic model then evaluates this action and predict the distance between the current location and true binding site. Experimental results showed that in both single- and multi-atom cases, our model improves binding site prediction substantially compared to a naïve model. For the single-atom ligand, copper ion (Cu2+), the model predicted binding sites have a median root-mean-square-deviation (RMSD) of 2.39 Å to the true binding sites when starting from random starting locations. For the multi-atom ligand, sulfate ion (SO42-), the predicted binding sites have a median RMSD of 3.82 Å to the true binding sites. The ligand-specific models built in this study can be used in solvent mapping studies and the RL framework can be readily scaled up to larger and more diverse sets of ligands.
Project description:We report the performance of HADDOCK in the 2018 iteration of the Grand Challenge organised by the D3R consortium. Building on the findings of our participation in last year's challenge, we significantly improved our pose prediction protocol which resulted in a mean RMSD for the top scoring pose of 3.04 and 2.67 Å for the cross-docking and self-docking experiments respectively, which corresponds to an overall success rate of 63% and 71% when considering the top1 and top5 models respectively. This performance ranks HADDOCK as the 6th and 3rd best performing group (excluding multiple submissions from a same group) out of a total of 44 and 47 submissions respectively. Our ligand-based binding affinity predictor is the 3rd best predictor overall, behind only the two leading structure-based implementations, and the best ligand-based one with a Kendall's Tau correlation of 0.36 for the Cathepsin challenge. It also performed well in the classification part of the Kinase challenges, with Matthews Correlation Coefficients of 0.49 (ranked 1st), 0.39 (ranked 4th) and 0.21 (ranked 4th) for the JAK2, vEGFR2 and p38a targets respectively. Through our participation in last year's competition we came to the conclusion that template selection is of critical importance for the successful outcome of the docking. This year we have made improvements in two additional areas of importance: ligand conformer selection and initial positioning, which have been key to our excellent pose prediction performance this year.
Project description:The thyroid-stimulating hormone receptor (TSHR) is a member of the glycoprotein hormone receptors, a sub-group of class A G-protein-coupled receptors (GPCRs). TSHR and its endogenous ligand thyrotropin (TSH) are of essential importance for growth and function of the thyroid gland and proper function of the TSH/TSHR system is pivotal for production and release of thyroid hormones. This receptor is also important with respect to pathophysiology, such as autoimmune (including ophthalmopathy) or non-autoimmune thyroid dysfunctions and cancer development. Pharmacological interventions directly targeting the TSHR should provide benefits to disease treatment compared to currently available therapies of dysfunctions associated with the TSHR or the thyroid gland. Upon TSHR activation, the molecular events conveying conformational changes from the extra- to the intracellular side of the cell across the membrane comprise reception, conversion, and amplification of the signal. These steps are highly dependent on structural features of this receptor and its intermolecular interaction partners, e.g., TSH, antibodies, small molecules, G-proteins, or arrestin. For better understanding of signal transduction, pathogenic mechanisms such as autoantibody action and mutational modifications or for developing new pharmacological strategies, it is essential to combine available structural data with functional information to generate homology models of the entire receptor. Although so far these insights are fragmental, in the past few decades essential contributions have been made to investigate in-depth the involved determinants, such as by structure determination via X-ray crystallography. This review summarizes available knowledge (as of December 2016) concerning the TSHR protein structure, associated functional aspects, and based on these insights we suggest several receptor complex models. Moreover, distinct TSHR properties will be highlighted in comparison to other class A GPCRs to understand the molecular activation mechanisms of this receptor comprehensively. Finally, limitations of current knowledge and lack of information are discussed highlighting the need for intensified efforts toward TSHR structure elucidation.
Project description:GLP-1 and glucagon regulate glucose metabolism through a network of metabolic pathways initiated upon binding to their specific receptors that belong to class B G-protein coupled receptors (GPCRs). The therapeutic potential of glucagon is currently being evaluated, while GLP-1 is already used in the treatment of type 2 diabetes and obesity. Development of a second generation of GLP-1 based therapeutics depends on a molecular and structural understanding of the interactions between the GLP-1 receptor (GLP-1R) and its ligand GLP-1. There is considerable sequence conservation between GLP-1 and glucagon and between the hGLP-1R and human glucagon receptor (hGCGR), yet each receptor recognizes only its own specific ligand. Glucagon receptors in fish and frogs also exhibit ligand selectivity only towards glucagon and not GLP-1. Based on competitive binding experiments and assays of increase in intracellular cAMP, we demonstrate here that a GPCR in zebrafish (Danio rerio) exhibits dual ligand selectivity towards GLP-1 and glucagon, a characteristic not found in mammals. Further, many structural features found in hGLP-1R and hGCGR are also found in this zebrafish GPCR (zfGPCR). We show this by mapping of its sequence and structural features onto the hGLP-1R and hGCGR based on their partial and complementary crystal structures. Thus, we propose that zfGPCR represents a dual GLP-1R/GCGR. The main differences between the three receptors are in their stalk regions that connect their N-terminal extracellular domains (NECDs) with their transmembrane domains and the absence of loop 3 in the NECD in zfGLP-1R/GCGR. These observations suggest that the interactions between GLP-1 and glucagon with loop 3 and the stalk regions may induce different conformational changes in hGLP-1R and hGCGR upon ligand binding and activation that lead to selective recognition of their native ligands.
Project description:G protein-coupled receptors (GPCRs) are leading druggable targets for several medicines, but many GPCRs are still untapped for their therapeutic potential due to poor understanding of specific signaling properties. The complement C3a receptor 1 (C3aR1) has been extensively studied for its physiological role in C3a-mediated anaphylaxis/inflammation, and in TLQP-21-mediated lipolysis, but direct evidence for the functional relevance of the C3a and TLQP-21 ligands and signal transduction mechanisms are still limited. In addition, C3aR1 G protein coupling specificity is still unclear, and whether endogenous ligands, or drug-like compounds, show ligand-mediated biased agonism is unknown. Here, we demonstrate that C3aR1 couples preferentially to Gi/o/z proteins and can recruit β-arrestins to cause internalization. Furthermore, we showed that in comparison to C3a63-77, TLQP-21 exhibits a preference toward Gi/o-mediated signaling compared to β-arrestin recruitment and internalization. We also show that the purported antagonist SB290157 is a very potent C3aR1 agonist, where antagonism of ligand-stimulated C3aR1 calcium flux is caused by potent β-arrestin-mediated internalization. Finally, ligand-mediated signaling bias impacted cell function as demonstrated by the regulation of calcium influx, lipolysis in adipocytes, phagocytosis in microglia, and degranulation in mast cells. Overall, we characterize C3aR1 as a Gi/o/z-coupled receptor and demonstrate the functional relevance of ligand-mediated signaling bias in key cellular models. Due to C3aR1 and its endogenous ligands being implicated in inflammatory and metabolic diseases, these results are of relevance toward future C3aR1 drug discovery.
Project description:Protein-based pharmacophore models derived from protein binding site atoms without the inclusion of any ligand information have become more popular in virtual screening studies. However, the accuracy of protein-based pharmacophore models for reproducing the critical protein-ligand interactions has never been explicitly assessed. In this study, we used known protein-ligand contacts from a large set of experimentally determined protein-ligand complexes to assess the quality of the protein-based pharmacophores in reproducing these critical contacts. We demonstrate how these contacts can be used to optimize the pharmacophore generation procedure to produce pharmacophore models that optimally cover the known protein-ligand interactions. Finally, we explored the potential of the optimized protein-based pharmacophore models for pose prediction and pose rankings. Our results demonstrate that there are significant variations in the success of protein-based pharmacophore models to reproduce native contacts and consequently native ligand poses dependent on the details of the pharmacophore generation process. We show that the generation of optimized protein-based pharmacophore models is a promising approach for ligand pose prediction and pose rankings.
Project description:Understanding how ligands bind to G-protein coupled receptors (GPCRs) provides insights into a myriad of cell processes and is crucial for drug development. Here we extend a hybrid molecular mechanics/coarse-grained (MM/CG) approach applied previously to enzymes to GPCR/ligand complexes. The accuracy of this method for structural predictions is established by comparison with recent atomistic molecular dynamics simulations on the human ?2 adrenergic receptor, a member of the GPCRs superfamily. The results obtained with the MM/CG methodology show a good agreement with previous all-atom classical dynamics simulations, in particular in the structural description of the ligand binding site. This approach could be used for high-throughput predictions of ligand poses in a variety of GPCRs.
Project description:G protein-coupled receptors (GPCRs) conserve common structural folds and activation mechanisms, yet their ligand spectra and functions are highly diverse. This work investigated how the amino-acid sequences of olfactory receptors (ORs)-the largest GPCR family-encode diversified responses to various ligands. We established a proteochemometric (PCM) model based on OR sequence similarities and ligand physicochemical features to predict OR responses to odorants using supervised machine learning. The PCM model was constructed with the aid of site-directed mutagenesis, in vitro functional assays, and molecular simulations. We found that the ligand selectivity of the ORs is mostly encoded in the residues up to 8 Å around the orthosteric pocket. Subsequent predictions using Random Forest (RF) showed a hit rate of up to 58%, as assessed by in vitro functional assays of 111 ORs and 7 odorants of distinct scaffolds. Sixty-four new OR-odorant pairs were discovered, and 25 ORs were deorphanized here. The best model demonstrated a 56% deorphanization rate. The PCM-RF approach will accelerate OR-odorant mapping and OR deorphanization.
Project description:Formyl-peptide receptor type 2 (FPR2), also called ALX (the lipoxin A4 receptor), conveys the proresolving properties of lipoxin A4 and annexin A1 (AnxA1) and the proinflammatory signals elicited by serum amyloid protein A and cathelicidins, among others. We tested here the hypothesis that ALX might exist as homo- or heterodimer with FPR1 or FPR3 (the two other family members) and operate in a ligand-biased fashion. Coimmunoprecipitation and bioluminescence resonance energy transfer assays with transfected HEK293 cells revealed constitutive dimerization of the receptors; significantly, AnxA1, but not serum amyloid protein A, could activate ALX homodimers. A p38/MAPK-activated protein kinase/heat shock protein 27 signaling signature was unveiled after AnxA1 application, leading to generation of IL-10, as measured in vitro (in primary monocytes) and in vivo (after i.p. injection in the mouse). The latter response was absent in mice lacking the ALX ortholog. Using a similar approach, ALX/FPR1 heterodimerization evoked using the panagonist peptide Ac2-26, identified a JNK-mediated proapoptotic path that was confirmed in primary neutrophils. These findings provide a molecular mechanism that accounts for the dual nature of ALX and indicate that agonist binding and dimerization state contribute to the conformational landscape of FPRs.