Summary Lipid biology continues to emerge as an area of significant therapeutic interest, particularly as the result of an enhanced understanding of the wealth of signaling molecules with diverse physiological properties. This growth in knowledge is epitomized by lysophosphatidic acid (LPA), which functions through interactions with six cognate G protein-coupled receptors. Herein we present three crystal structures of LPA1 in complex with antagonist tool compounds selected and designed through structural and stability analysis. Structural analysis combined with molecular dynamics identified a basis for ligand access to the LPA1 binding pocket from the extracellular space contrasting with the proposed access for the sphingosine 1-phosphate receptor. Characteristics of the LPA1 binding pocket raise the possibility of promiscuous ligand recognition of phosphorylated endocannabinoids. Cell-based assays confirmed this hypothesis, linking the distinct receptor systems through metabolically related ligands with potential functional and therapeutic implications for treatment of disease.

We present the blinded prediction results in the Second Antibody Modeling Assessment (AMA-II) using a fully automatic antibody structure prediction method implemented in the programs BioLuminate and Prime. We have developed a novel knowledge based approach to model the CDR loops, using a combination of sequence similarity, geometry matching, and the clustering of database structures. The homology models are further optimized with a physics-based energy function (VSGB2.0), which improves the model quality significantly. H3 loop modeling remains the most challenging task. Our ab initio loop prediction performs well for the H3 loop in the crystal structure context, and allows improved results when refining the H3 loops in the context of homology models. For the 10 human and mouse derived antibodies in this assessment, the average RMSDs for the homology model Fv and framework regions are 1.19 Å and 0.74 Å, respectively. The average RMSDs for five non-H3 CDR loops range from 0.61 Å to 1.05 Å, and the H3 loop average RMSD is 2.91 Å using our knowledge-based loop prediction approach. The ab initio H3 loop predictions yield an average RMSD of 1.28 Å when performed in the context of the crystal structure and 2.67 Å in the context of the homology modeled structure. Notably, our method for predicting the H3 loop in the crystal structure environment ranked first among the seven participating groups in AMA-II, and our method made the best prediction among all participants for seven of the ten targets.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the PREDICT method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 ''virtual hit'' compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, K i < 5 M). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1-to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.modeling ͉ in silico screening ͉ structure-based G protein-coupled receptors (GPCRs) are membraneembedded proteins, responsible for communication between the cell and its environment (1). As a consequence, many major diseases, such as hypertension, cardiac dysfunction, depression, anxiety, obesity, inflammation, and pain, involve malfunction of these receptors (2), making them among the most important drug targets for pharmacological intervention (3-5). Thus, whereas GPCRs are only a small subset of the human genome, they are the targets for Ϸ50% of all recently launched drugs (6). As targets of paramount importance, it is expected that drug discovery for GPCRs would benefit from the introduction of computational methodologies (7), especially as these methods can be used in conjunction with such experimental methods as high-throughput screening (8, 9), NMR, and crystallography (10).Unfortunately, GPCRs, like other membrane-embedded proteins, have characteristics that make their 3D structure extremely difficult to determine experimentally. To date, the only GPCR for which a 3D structure was determined by x-ray crystallography is bovine rhodopsin (11), which is unique among GPCRs in that its ligand, retinal, is covalently bound and that it responds to light rather than to ligand binding. Hence, in the case of GPCRs, the limited availability of structural data has forced the computational design of ligands to heavily rely on ligand-based techniques. Indeed, for many GPCRs, the natural ligand can provide a good starting point, leading to useful pharmacophore models that can be used for identifying lead structures with novel scaffolds (6). These methods have been successfully applied for the discovery of peptide agonists to the somatostatin receptor (12) and for...

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

We present a fast and effective covalent docking approach suitable for large-scale virtual screening (VS). We applied this method to four targets (HCV NS3 protease, Cathepsin K, EGFR, and XPO1) with known crystal structures and known covalent inhibitors. We implemented a customized "VS mode" of the Schrödinger Covalent Docking algorithm (CovDock), which we refer to as CovDock-VS. Known actives and target-specific sets of decoys were docked to selected X-ray structures, and poses were filtered based on noncovalent protein-ligand interactions known to be important for activity. We were able to retrieve 71%, 72%, and 77% of the known actives for Cathepsin K, HCV NS3 protease, and EGFR within 5% of the decoy library, respectively. With the more challenging XPO1 target, where no specific interactions with the protein could be used for postprocessing of the docking results, we were able to retrieve 95% of the actives within 30% of the decoy library and achieved an early enrichment factor (EF1%) of 33. The poses of the known actives bound to existing crystal structures of 4 targets were predicted with an average RMSD of 1.9 Å. To the best of our knowledge, CovDock-VS is the first fully automated tool for efficient virtual screening of covalent inhibitors. Importantly, CovDock-VS can handle multiple chemical reactions within the same library, only requiring a generic SMARTS-based predefinition of the reaction. CovDock-VS provides a fast and accurate way of differentiating actives from decoys without significantly deteriorating the accuracy of the predicted poses for covalent protein-ligand complexes. Therefore, we propose CovDock-VS as an efficient structure-based virtual screening method for discovery of novel and diverse covalent ligands.