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.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.

Inspired by natural language processing techniques, we here introduce Mol2vec, which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Like the Word2vec models, where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that point in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing the vectors of the individual substructures and, for instance, be fed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pretrained once, yields dense vector representations, and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as a reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment-independent and thus can also be easily used for proteins with low sequence similarities.

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

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.

Unusual substrate specificity of the peptidoglycan MurE ligase from Erysipelothrix rhusiopathiae.

Erysipelothrix rhusiopathiae is a Gram-positive bacterium pathogenic to many species of birds and mammals, including humans. The main feature of its peptidoglycan is the presence of l-alanine at position 3 of the peptide stem. In the present work, we cloned the murE gene from E. rhusiopathiae and purified the corresponding protein as His6-tagged form. Enzymatic assays showed that E. rhusiopathiae MurE was indeed an l-alanine-adding enzyme. Surprisingly, it was also able, although to a lesser extent, to add meso-diaminopimelic acid, the amino acid found at position 3 in many Gram-negative bacteria, Bacilli and Mycobacteria. Sequence alignment of MurE enzymes from E. rhusiopathiae and Escherichia coli revealed that the DNPR motif that is characteristic of meso-diaminopimelate-adding enzymes was replaced by HDNR. The role of the latter motif in the interaction with l-alanine and meso-diaminopimelic acid was demonstrated by site-directed mutagenesis experiments and the construction of a homology model. The overexpression of the E. rhusiopathiae murE gene in E. coli resulted in the incorporation of l-alanine at position 3 of the peptide part of peptidoglycan.

Molecular dynamics to enhance structure-based virtual screening on cathepsin B.

Molecular dynamics (MD) and molecular docking are commonly used to study molecular interactions in drug discovery. Most docking approaches consider proteins as rigid, which can decrease the accuracy of predicted docked poses. Therefore MD simulations can be used prior to docking to add flexibility to proteins. We evaluated the contribution of using MD together with docking in a docking study on human cathepsin B, a well-studied protein involved in numerous pathological processes. Using CHARMM biomolecular simulation program and AutoDock Vina molecular docking program, we found, that short MD simulations significantly improved molecular docking. Our results, expressed with the area under the receiver operating characteristic curves, show an increase in discriminatory power i.e. the ability to discriminate active from inactive compounds of molecular docking, when docking is performed to selected snapshots from MD simulations.

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.

Design, synthesis, and evaluation of new thiadiazole-based direct inhibitors of enoyl acyl carrier protein reductase (InhA) for the treatment of tuberculosis.

Mycobacterial enoyl acyl carrier protein reductase (InhA) is a clinically validated target for the treatment of tuberculosis infections, a disease that still causes the death of at least a million people annually. A known class of potent, direct, and competitive InhA inhibitors based on a tetracyclic thiadiazole structure has been shown to have in vivo activity in murine models of tuberculosis infection. On the basis of this template, we have here explored the medicinal chemistry of truncated analogues that have only three aromatic rings. In particular, compounds 8b, 8d, 8f, 8l, and 8n show interesting features, including low nanomolar InhA IC50, submicromolar antimycobacterial potency, and improved physicochemical profiles in comparison with the tetracyclic analogues. From this series, 8d is identified as having the best balance of potency and properties, whereby the resolved 8d S-enatiomer shows encouraging in vivo efficacy.

Discovery, biological evaluation, and crystal structure of a novel nanomolar selective butyrylcholinesterase inhibitor.

Butyrylcholinesterase (BChE) is regarded as a promising drug target as its levels and activity significantly increase in the late stages of Alzheimer's disease. To discover novel BChE inhibitors, we used a hierarchical virtual screening protocol followed by biochemical evaluation of 40 highest scoring hit compounds. Three of the compounds identified showed significant inhibitory activities against BChE. The most potent, compound 1 (IC50 = 21.3 nM), was resynthesized and resolved into its pure enantiomers. A high degree of stereoselective activity was revealed, and a dissociation constant of 2.7 nM was determined for the most potent stereoisomer (+)-1. The crystal structure of human BChE in complex with compound (+)-1 was solved, revealing the binding mode and providing clues for potential optimization. Additionally, compound 1 inhibited amyloid ß(1-42) peptide self-induced aggregation into fibrils (by 61.7% at 10 µM) and protected cultured SH-SY5Y cells against amyloid-ß-induced toxicity. These data suggest that compound 1 represents a promising candidate for hit-to-lead follow-up in the drug-discovery process against Alzheimer's disease.

Endocrine disruptome--an open source prediction tool for assessing endocrine disruption potential through nuclear receptor binding.

Predicting the endocrine disruption potential of compounds is a daunting but essential task. Here we report a new tool for this purpose that we have termed Endocrine Disruptome. It is a free and simple-to-use Web service that runs on an open source platform called Docking interface for Target Systems (DoTS). The molecular docking is handled via AutoDock Vina. Compounds are docked to 18 integrated and well-validated crystal structures of 14 different human nuclear receptors: androgen receptor; estrogen receptors α and ß; glucocorticoid receptor; liver X receptors α and ß; mineralocorticoid receptor; peroxisome proliferator activated receptors α, ß/δ, and γ; progesterone receptor; retinoid X receptor α; and thyroid receptors α and ß. Endocrine Disruptome is free of charge and available at http://endocrinedisruptome.ki.si.

Novel toll-like receptor 4 (TLR4) antagonists identified by structure- and ligand-based virtual screening.

Toll-like receptor 4 (TLR4) in complex with its accessory protein MD-2 represents an emerging target for the treatment of severe sepsis and neuropathic pain. We performed structure-based and ligand-based virtual screening targeting the TLR4-MD-2 interface. Three in silico hit compounds showed promising TLR4 antagonistic activities with micromolar IC50 values. These compounds also suppressed cytokine secretion by human peripheral blood mononuclear cells. The specific affinity of the most potent hit was confirmed by surface plasmon resonance direct-binding experiments. The results of our study represent a very promising starting point for the development of potent small-molecule antagonists of TLR4.

Biochemical characterization of MurF from Streptococcus pneumoniae and the identification of a new MurF inhibitor through ligand-based virtual screening.

MurF is an essential bacterial enzyme that is involved in the last intracellular stage of peptidoglycan biosynthesis, and therefore it has the potential to be exploited as a target for the development of new antibacterials. Here, we report on the expression, purification and biochemical characterization of MurF from an important pathogen, Streptococcus pneumoniae. Additionally, ligand-based virtual screening was successfully used and a new hit compound with micromolar inhibitory activities against MurF enzymes from S. pneumoniae and Escherichia coli was identified.

Structure-activity relationships of new cyanothiophene inhibitors of the essential peptidoglycan biosynthesis enzyme MurF.

Peptidoglycan is an essential component of the bacterial cell wall, and enzymes involved in its biosynthesis represent validated targets for antibacterial drug discovery. MurF catalyzes the final intracellular peptidoglycan biosynthesis step: the addition of D-Ala-D-Ala to the nucleotide precursor UDP-MurNAc-L-Ala-γ-D-Glu-meso-DAP (or L-Lys). As MurF has no human counterpart, it represents an attractive target for the development of new antibacterial drugs. Using recently published cyanothiophene inhibitors of MurF from Streptococcus pneumoniae as a starting point, we designed and synthesized a series of structurally related derivatives and investigated their inhibition of MurF enzymes from different bacterial species. Systematic structural modifications of the parent compounds resulted in a series of nanomolar inhibitors of MurF from S. pneumoniae and micromolar inhibitors of MurF from Escherichia coli and Staphylococcus aureus. Some of the inhibitors also show antibacterial activity against S. pneumoniae R6. These findings, together with two new co-crystal structures, represent an excellent starting point for further optimization toward effective novel antibacterials.

2,3-Diarylpropenoic acids as selective non-steroidal inhibitors of type-5 17ß-hydroxysteroid dehydrogenase (AKR1C3).

The aldo-keto reductase AKR1C3 is an important target for the development of new drugs. Selective inhibitors of this enzyme are needed because they should not inhibit other, structurally closely related AKR1C isoforms. A comprehensive series of 2,3-diarylpropenoic acids was synthesized and evaluated for the inhibition of AKR1C1-AKR1C3. We found that the 4-methylsulfonylphenyl substituent at position 2 of these acids is required to exhibit the selective inhibition of AKR1C3. The best results were obtained for the compounds that fulfill the above requirement and possess a 4-bromophenyl, 4-methylthiophenyl, 4-methylphenyl or 4-ethylphenyl substituent at position 3 of the substituted propenoic acids (i.e., acids 28, 29, 37, and 39, respectively). These compounds represent an important step toward the development of drug candidates for a treatment of the hormone-dependent and hormone-independent forms of prostate and breast cancers.

Cinnamic acid derivatives induce cell cycle arrest in carcinoma cell lines.

Cinnamic acid derivatives can be found in plant material, and they possess a remarkable variety of biological effects. In the present study, we have investigated the cytotoxic effects of representative cinnamic acid esters and amides. The cytotoxicity was determined by MTT test on human cervix adenocarcinoma (HeLa), myelogenous leukemia (K562), malignant melanoma (Fem-x), and estrogen-receptor-positive breast cancer (MCF-7) cells, versus peripheral blood mononuclear cells (PBMCs) without or with the addition of the plant lectin phytohemaglutinin (PHA). The compounds tested showed significant cytotoxicity (IC50s between 42 and 166 µM) and furthermore selectivity of these cytotoxic effects on the malignant cell lines versus the PBMCs was also seen, especially when electron-withdrawing groups, such as a cyano group (compound 5), were present on the aromatic rings of the alcohol or amine parts of the cinnamic acid derivatives. The additional study on cell cycle phase distribution indicated that novel cinnamic acid derivatives inhibit cell growth by induction of cell death. Thus, cinnamic acids derivatives represent important lead compounds for further development of antineoplastic agents.

Virtual screening yields inhibitors of novel antifungal drug target, benzoate 4-monooxygenase.

Fungal CYP53 enzymes are highly conserved proteins, involved in phenolic detoxification, and have no homologues in higher eukaryotes, rendering them favorable drug targets. Aiming to discover novel CYP53 inhibitors, we employed two parallel virtual screening protocols and evaluated highest scoring hit compounds by analyzing the spectral binding interactions, by surveying the antifungal activity, and assessing the inhibition of catalytic activity. On the basis of combined results, we selected 3-methyl-4-(1H-pyrrol-1-yl)benzoic acid (compound 2) as the best candidate for hit-to-lead follow-up in the antifungal drug discovery process.

Selective inhibitors of aldo-keto reductases AKR1C1 and AKR1C3 discovered by virtual screening of a fragment library.

Human aldo-keto reductases 1C1-1C4 (AKR1C1-AKR1C4) function in vivo as 3-keto-, 17-keto-, and 20-ketosteroid reductases and regulate the activity of androgens, estrogens, and progesterone and the occupancy and transactivation of their corresponding receptors. Aberrant expression and action of AKR1C enzymes can lead to different pathophysiological conditions. AKR1C enzymes thus represent important targets for development of new drugs. We performed a virtual high-throughput screen of a fragment library that was followed by biochemical evaluation on AKR1C1-AKR1C4 enzymes. Twenty-four structurally diverse compounds were discovered with low µM K(i) values for AKR1C1, AKR1C3, or both. Two structural series included the salicylates and the N-phenylanthranilic acids, and additionally a series of inhibitors with completely novel scaffolds was discovered. Two of the best selective AKR1C3 inhibitors had K(i) values of 0.1 and 2.7 µM, exceeding expected activity for fragments. The compounds identified represent an excellent starting point for further hit-to-lead development.