From Cancer to Pain Target by Automated Selectivity Inversion of a Clinical Candidate.

Elimination of inadvertent binding is crucial for inhibitor design targeting conserved protein classes like kinases. Compounds in clinical trials provide a rich source for initiating drug design efforts by exploiting such secondary binding events. Considering both aspects, we shifted the selectivity of tozasertib, originally developed against AurA as cancer target, toward the pain target TrkA. First, selectivity-determining features in binding pockets were identified by fusing interaction grids of several key and off-target conformations. A focused library was subsequently created and prioritized using a multiobjective selection scheme that filters for selective and highly active compounds based on orthogonal methods grounded in computational chemistry and machine learning. Eighteen high-ranking compounds were synthesized and experimentally tested. The top-ranked compound has 10000-fold improved selectivity versus AurA, nanomolar cellular activity, and is highly selective in a kinase panel. This was achieved in a single round of automated in silico optimization, highlighting the power of recent advances in computer-aided drug design to automate design and selection processes.

A validated antibody panel for the characterization of tau post-translational modifications.

BACKGROUND: Tau is a microtubule-binding protein, which is subject to various post-translational modifications (PTMs) including phosphorylation, methylation, acetylation, glycosylation, nitration, sumoylation and truncation. Aberrant PTMs such as hyperphosphorylation result in tau aggregation and the formation of neurofibrillary tangles, which are a hallmark of Alzheimer's disease (AD). In order to study the importance of PTMs on tau function, antibodies raised against specific modification sites are widely used. However, quality control of these antibodies is lacking and their specificity for particular modifications is often unclear. METHODS: In this study, we first designed an online tool called 'TauPTM', which enables the visualization of PTMs and their interactions on human tau. Using TauPTM, we next searched for commercially available antibodies against tau PTMs and characterized their specificity by peptide array, immunoblotting, electrochemiluminescence ELISA and immunofluorescence technologies. RESULTS: We demonstrate that commercially available antibodies can show a significant lack of specificity, and PTM-specific antibodies in particular often recognize non-modified versions of the protein. In addition, detection may be hindered by other PTMs in close vicinity, complicating the interpretation of results. Finally, we compiled a panel of specific antibodies and show that they are useful to detect PTM-modified endogenous tau in hiPSC-derived neurons and mouse brains. CONCLUSION: This study has created a platform to reliably and robustly detect changes in localization and abundance of post-translationally modified tau in health and disease. A web-based version of TauPTM is fully available at http://www.tauptm.org .

Mutation of Tyr137 of the universal Escherichia coli fimbrial adhesin FimH relaxes the tyrosine gate prior to mannose binding.

The most prevalent diseases manifested by Escherichia coli are acute and recurrent bladder infections and chronic inflammatory bowel diseases such as Crohn's disease. E. coli clinical isolates express the FimH adhesin, which consists of a mannose-specific lectin domain connected via a pilin domain to the tip of type 1 pili. Although the isolated FimH lectin domain has affinities in the nanomolar range for all high-mannosidic glycans, differentiation between these glycans is based on their capacity to form predominantly hydrophobic interactions within the tyrosine gate at the entrance to the binding pocket. In this study, novel crystal structures of tyrosine-gate mutants of FimH, ligand-free or in complex with heptyl α-d-O-mannopyranoside or 4-biphenyl α-d-O-mannopyranoside, are combined with quantum-mechanical calculations and molecular-dynamics simulations. In the Y48A FimH crystal structure, a large increase in the dynamics of the alkyl chain of heptyl α-d-O-mannopyranoside attempts to compensate for the absence of the aromatic ring; however, the highly energetic and stringent mannose-binding pocket of wild-type FimH is largely maintained. The Y137A mutation, on the other hand, is the most detrimental to FimH affinity and specificity: (i) in the absence of ligand the FimH C-terminal residue Thr158 intrudes into the mannose-binding pocket and (ii) ethylenediaminetetraacetic acid interacts strongly with Glu50, Thr53 and Asn136, in spite of multiple dialysis and purification steps. Upon mutation, pre-ligand-binding relaxation of the backbone dihedral angles at position 137 in the tyrosine gate and their coupling to Tyr48 via the interiorly located Ile52 form the basis of the loss of affinity of the FimH adhesin in the Y137A mutant.

KinMap: a web-based tool for interactive navigation through human kinome data.

BACKGROUND: Annotations of the phylogenetic tree of the human kinome is an intuitive way to visualize compound profiling data, structural features of kinases or functional relationships within this important class of proteins. The increasing volume and complexity of kinase-related data underlines the need for a tool that enables complex queries pertaining to kinase disease involvement and potential therapeutic uses of kinase inhibitors. RESULTS: Here, we present KinMap, a user-friendly online tool that facilitates the interactive navigation through kinase knowledge by linking biochemical, structural, and disease association data to the human kinome tree. To this end, preprocessed data from freely-available sources, such as ChEMBL, the Protein Data Bank, and the Center for Therapeutic Target Validation platform are integrated into KinMap and can easily be complemented by proprietary data. The value of KinMap will be exemplarily demonstrated for uncovering new therapeutic indications of known kinase inhibitors and for prioritizing kinases for drug development efforts. CONCLUSION: KinMap represents a new generation of kinome tree viewers which facilitates interactive exploration of the human kinome. KinMap enables generation of high-quality annotated images of the human kinome tree as well as exchange of kinome-related data in scientific communications. Furthermore, KinMap supports multiple input and output formats and recognizes alternative kinase names and links them to a unified naming scheme, which makes it a useful tool across different disciplines and applications. A web-service of KinMap is freely available at http://www.kinhub.org/kinmap/ .

Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay.

Kinome-wide screening would have the advantage of providing structure-activity relationships against hundreds of targets simultaneously. Here, we report the generation of ligand-based activity prediction models for over 280 kinases by employing Machine Learning methods on an extensive data set of proprietary bioactivity data combined with open data. High quality (AUC > 0.7) was achieved for ∼200 kinases by (1) combining open with proprietary data, (2) choosing Random Forest over alternative tested Machine Learning methods, and (3) balancing the training data sets. Tests on left-out and external data indicate a high value for virtual screening projects. Importantly, the derived models are evenly distributed across the kinome tree, allowing reliable profiling prediction for all kinase branches. The prediction quality was further improved by employing experimental bioactivity fingerprints of a small kinase subset. Overall, the generated models can support various hit identification tasks, including virtual screening, compound repurposing, and the detection of potential off-targets.

Identification and Visualization of Kinase-Specific Subpockets.

The identification and design of selective compounds is important for the reduction of unwanted side effects as well as for the development of tool compounds for target validation studies. This is, in particular, true for therapeutically important protein families that possess conserved folds and have numerous members such as kinases. To support the design of selective kinase inhibitors, we developed a novel approach that allows identification of specificity determining subpockets between closely related kinases solely based on their three-dimensional structures. To account for the intrinsic flexibility of the proteins, multiple X-ray structures of the target protein of interest as well as of unwanted off-target(s) are taken into account. The binding pockets of these protein structures are calculated and fused to a combined target and off-target pocket, respectively. Subsequently, shape differences between these two combined pockets are identified via fusion rules. The approach provides a user-friendly visualization of target-specific areas in a binding pocket which should be explored when designing selective compounds. Furthermore, the approach can be easily combined with in silico alanine mutation studies to identify selectivity determining residues. The potential impact of the approach is demonstrated in four retrospective experiments on closely related kinases, i.e., p38α vs Erk2, PAK1 vs PAK4, ITK vs AurA, and BRAF vs VEGFR2. Overall, the presented approach does not require any profiling data for training purposes, provides an intuitive visualization of a large number of protein structures at once, and could also be applied to other target classes.

Design, synthesis, antimicrobial evaluation and molecular docking studies of some new 2,3-dihydrothiazoles and 4-thiazolidinones containing sulfisoxazole.

Microbial resistance to the available drugs poses a serious threat in modern medicine. We report the design, synthesis and in vitro antimicrobial evaluation of new functionalized 2,3-dihydrothiazoles and 4-thiazolidinones tagged with sulfisoxazole moiety. Compound 8d was most active against Bacillis subtilis (MIC, 0.007 µg/mL). Moreover, compounds 7c-d and 8c displayed significant activities against B. subtilis and Streptococcus pneumoniae (MIC, 0.03-0.06 µg/mL and 0.06-0.12 µg/mL versus ampicillin 0.24 µg/mL and 0.12 µg/mL; respectively). Compounds 7a and 7c-d were highly potent against Escherichia coli (MIC, 0.49-0.98 µg/mL versus gentamycin 1.95 µg/mL). On the other hand, compounds 7e and 9c were fourfolds more active than amphotericin B against Syncephalastrum racemosum. Molecular docking studies showed that the synthesized compounds could act as inhibitors for the dihydropteroate synthase enzyme (DHPS). This study is a platform for the future design of more potent antimicrobial agents.

Pocketome of human kinases: prioritizing the ATP binding sites of (yet) untapped protein kinases for drug discovery.

Protein kinases are involved in a variety of diseases including cancer, inflammation, and autoimmune disorders. Although the development of new kinase inhibitors is a major focus in pharmaceutical research, a large number of kinases remained so far unexplored in drug discovery projects. The selection and assessment of targets is an essential but challenging area. Today, a few thousands of experimentally determined kinase structures are available, covering about half of the human kinome. This large structural source allows guiding the target selection via structure-based druggability prediction approaches such as DoGSiteScorer. Here, a thorough analysis of the ATP pockets of the entire human kinome in the DFG-in state is presented in order to prioritize novel kinase structures for drug discovery projects. For this, all human kinase X-ray structures available in the PDB were collected, and homology models were generated for the missing part of the kinome. DoGSiteScorer was used to calculate geometrical and physicochemical properties of the ATP pockets and to predict the potential of each kinase to be druggable. The results indicate that about 75% of the kinome are in principle druggable. Top ranking structures comprise kinases that are primary targets of known approved drugs but additionally point to so far less explored kinases. The presented analysis provides new insights into the druggability of ATP binding pockets of the entire kinome. We anticipate this comprehensive druggability assessment of protein kinases to be helpful for the community to prioritize so far untapped kinases for drug discovery efforts.

Exploring the free-energy landscape of carbohydrate-protein complexes: development and validation of scoring functions considering the binding-site topology.

Carbohydrates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the structure-based design of carbohydrate-based ligands. We assembled a diverse data set comprising 273 carbohydrate-protein crystal structures with known binding affinity and evaluated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as combinations thereof. Unfortunately, the tested functions were not capable of reproducing binding affinities in the studied complexes. To simplify the complex free-energy surface of carbohydrate-protein systems, we classified the studied proteins according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classification scheme reproduced binding affinities in the carbohydrate data set with an r(2) of 0.71 and root-mean-squared-error of 1.25 kcal/mol (N = 236). The improvement in model performance underlines the significance of the differences in the local micro-environments of carbohydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and solvent exposure.

Design, synthesis, antimicrobial evaluation and molecular docking studies of some new thiophene, pyrazole and pyridone derivatives bearing sulfisoxazole moiety.

Development of new antimicrobial agents is a good solution to overcome drug-resistance problems. In this context, new functionalized thiophene, acrylamide, arylhydrazone, pyrazole and pyridone derivatives bearing sulfisoxazole moiety were designed, synthesized and evaluated for their in vitro antibacterial and antifungal activities. Among the synthesized compounds, thiophene 4d and 6-thioglucosylpyridone 17 displayed significant antibacterial activities against Escherichia coli (MIC, 0.007 µg/mL vs gentamycin 1.95 µg/mL) and Bacillis subtilis (MIC, 0.007 µg/mL vs ampicillin 0.24 µg/mL), respectively. Whereas, the pyrazole 6 showed the highest antifungal activity against Aspergillus fumigates (MIC, 0.03 µg/mL vs amphotericin B 0.12 µg/mL). In general, most of the synthesized compounds exhibited better antimicrobial activities than sulfisoxazole; this might be attributed to the synergistic effect of the sulfonamide and attached heterocyclic moieties as well as the increased lipophilic characters of the synthesized compounds. Molecular docking studies indicated that the synthesized compounds could occupy both p-amino benzoic acid (PABA) and pterin binding pockets of the dihydropteroate synthase (DHPS), suggesting that the target compounds could act by the inhibition of microbial DHPS enzyme. The results provide important information for the future design of more potent antimicrobial agents.

Molecular design and synthesis of HCV inhibitors based on thiazolone scaffold.

A series of thiazolone derivatives was designed and synthesized as potential HCV NS5B allosteric polymerase inhibitors at the allosteric site thumb II. Their antiviral activity was evaluated and molecular modeling was utilized to give further envision on their probable binding modes in the allosteric binding site. Among the tested molecules, compound 9b displayed sub-micromolar inhibitory activity with an EC50 of 0.79 µM indicating excellent potency profile. It also showed good safety profile (CC50≥75 µM and SI≥94.3).

Analogs design, synthesis and biological evaluation of peptidomimetics with potential anti-HCV activity.

Two series of peptidomimetics were designed, prepared and evaluated for their anti-HCV activity. One series possesses a C-terminal carboxylate functionality. In the other series, the electrophilic vinyl sulfonate moiety was introduced as a novel class of HCV NS3/4A protease inhibitors. In vitro based studies were then performed to evaluate the efficacies of the inhibitors using Human hepatoma cells, with the vinyl sulfonate ester (10) in particular, found to have highly potent anti-HCV activity with an EC(50) = 0.296 µM. Finally, molecular modeling studies were performed through docking of the synthesized compounds in the HCV NS3/4A protease active site to assess their binding modes with the enzyme and gain further insight into their structure-activity relationships.

A molecular-modeling toolbox aimed at bridging the gap between medicinal chemistry and computational sciences.

In the current era of high-throughput drug discovery and development, molecular modeling has become an indispensable tool for identifying, optimizing and prioritizing small-molecule drug candidates. The required background in computational chemistry and the knowledge of how to handle the complex underlying protocols, however, might keep medicinal chemists from routinely using in silico technologies. Our objective is to encourage those researchers to exploit existing modeling technologies more frequently through easy-to-use graphical user interfaces. In this account, we present two innovative tools (which we are prepared to share with academic institutions) facilitating computational tasks commonly utilized in drug discovery and development: (1) the VirtualDesignLab estimates the binding affinity of small molecules by simulating and quantifying their binding to the three-dimensional structure of a target protein; and (2) the MD Client launches molecular dynamics simulations aimed at exploring the time-dependent stability of ligand-protein complexes and provides residue-based interaction energies. This allows medicinal chemists to identify sites of potential improvement in their candidate molecule. As a case study, we present the application of our tools towards the design of novel antagonists for the FimH adhesin.