Esophageal squamous cell carcinoma (ESCC) is a world-wide prevalent cancer, which is particularly common in certain regions of Asia. Here we report the whole-exome or targeted deep sequencing of 139 paired ESCC cases, and analysis of somatic copy number variations (SCNV) of over 180 ESCCs. We identified novel significantly mutated genes such as FAT1, FAT2, ZNF750 and KMT2D, in addition to previously discovered ones (TP53, PIK3CA and NOTCH1). Further SCNV evaluation, immunohistochemistry and biological analysis suggested their functional relevance in ESCC. Notably, RTK-MAPK-PI3K pathways, cell cycle and epigenetic regulation are frequently dysregulated by multiple molecular mechanisms in this cancer. Moreover, our approaches uncovered many novel druggable candidates, and XPO1 was further explored as a therapeutic target because of its mutation and protein overexpression. Together, our integrated study unmasks a number of novel genetic lesions in ESCC and provides an important molecular foundation for understanding esophageal tumors and developing therapeutic 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...

The prolyl isomerase PIN1, a critical modifier of multiple signalling pathways, is overexpressed in the majority of cancers and its activity strongly contributes to tumour initiation and progression. Inactivation of PIN1 function conversely curbs tumour growth and cancer stem cell expansion, restores chemosensitivity and blocks metastatic spread, thus providing the rationale for a therapeutic strategy based on PIN1 inhibition. Notwithstanding, potent PIN1 inhibitors are still missing from the arsenal of anti-cancer drugs. By a mechanism-based screening, we have identified a novel covalent PIN1 inhibitor, KPT-6566, able to selectively inhibit PIN1 and target it for degradation. We demonstrate that KPT-6566 covalently binds to the catalytic site of PIN1. This interaction results in the release of a quinone-mimicking drug that generates reactive oxygen species and DNA damage, inducing cell death specifically in cancer cells. Accordingly, KPT-6566 treatment impairs PIN1-dependent cancer phenotypes in vitro and growth of lung metastasis in vivo.

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.

The cystic fibrosis transmembrane conductance regulator (CFTR) is an unusual ABC transporter, functioning as a chloride channel critical for fluid homeostasis in multiple organs. Disruption of CFTR function is associated with cystic fibrosis making it an attractive therapeutic target. In addition, CFTR blockers are being developed as potential antidiarrheals. CFTR drug discovery is hampered by the lack of high resolution structural data, and considerable efforts have been invested in modeling the channel structure. Although previously published CFTR models that have been made publicly available mostly agree with experimental data relating to the overall structure, they present the channel in an outward-facing conformation that does not agree with expected properties of a "channel-like" structure. Here, we make available a model of CFTR in such a "channel-like" conformation, derived by a unique modeling approach combining restrained homology modeling and ROSETTA refinement. In contrast to others, the present model is in agreement with expected channel properties such as pore shape, dimensions, solvent accessibility, and experimentally derived distances. We have used the model to explore the interaction of open channel blockers within the pore, revealing a common binding mode and ionic interaction with K95, in agreement with experimental data. The binding-site was further validated using a virtual screening enrichment experiment, suggesting the model might be suitable for drug discovery. In addition, we subjected the model to a molecular dynamics simulation, revealing previously unaddressed salt-bridge interactions that may be important for structure stability and pore-lining residues that may take part in Cl(-) conductance.

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.

Folding correctors of F508del-CFTR were discovered by in silico structure-based screening utilizing homology models of CFTR. The intracellular segment of CFTR was modeled and three cavities were identified at inter-domain interfaces: (1) Interface between the two Nucleotide Binding Domains (NBDs); (2) Interface between NBD1 and Intracellular Loop (ICL) 4, in the region of the F508 deletion; (3) multi-domain interface between NBD1:2:ICL1:2:4. We hypothesized that compounds binding at these interfaces may improve the stability of the protein, potentially affecting the folding yield or surface stability. In silico structure-based screening was performed at the putative binding-sites and a total of 496 candidate compounds from all three sites were tested in functional assays. A total of 15 compounds, representing diverse chemotypes, were identified as F508del folding correctors. This corresponds to a 3% hit rate, ∼tenfold higher than hit rates obtained in corresponding high-throughput screening campaigns. The same binding sites also yielded potentiators and, most notably, compounds with a dual corrector-potentiator activity (dual-acting). Compounds harboring both activity types may prove to be better leads for the development of CF therapeutics than either pure correctors or pure potentiators. To the best of our knowledge this is the first report of structure-based discovery of CFTR modulators.

Background:The CFTR chloride channel undergoes conformational changes during its gating cycle. Results: H620Q mutation associated with increased channel P o , and the corrector/potentiator CFFT-001 both lead to similar conformational shifts in NBD1. Conclusion:There is an intrinsic conformational equilibrium within NBD1 that is correlated with channel activity. Significance: Conformational fluctuations within NBD1 are fundamental to CFTR regulation.