Ligand and Target Discovery by Fragment-Based Screening in Human Cells.

Advances in the synthesis and screening of small-molecule libraries have accelerated the discovery of chemical probes for studying biological processes. Still, only a small fraction of the human proteome has chemical ligands. Here, we describe a platform that marries fragment-based ligand discovery with quantitative chemical proteomics to map thousands of reversible small molecule-protein interactions directly in human cells, many of which can be site-specifically determined. We show that fragment hits can be advanced to furnish selective ligands that affect the activity of proteins heretofore lacking chemical probes. We further combine fragment-based chemical proteomics with phenotypic screening to identify small molecules that promote adipocyte differentiation by engaging the poorly characterized membrane protein PGRMC2. Fragment-based screening in human cells thus provides an extensive proteome-wide map of protein ligandability and facilitates the coordinated discovery of bioactive small molecules and their molecular targets.

Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction.

We present results on the extent to which physics-based simulation (exemplified by FEP+) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction. For both methods, predictions of activity for LFA-1 inhibitors from a medicinal chemistry lead optimization project were accurate within the applicable domain of each approach. A hybrid model that combined predictions by both approaches by simple averaging performed better than either method, with respect to both ranking and absolute pKi values. Two publicly available FEP+ benchmarks, covering 16 diverse biological targets, were used to test the generality of the synergy. By identifying training data specifically focused on relevant ligands, accurate QuanSA models were derived using ligand activity data known at the time of the original series publications. Results across the 16 benchmark targets demonstrated significant improvements both for ranking and for absolute pKi values using hybrid predictions that combined the FEP+ and QuanSA predicted affinity values. The results argue for a combined approach for affinity prediction that makes use of physics-driven methods as well as those driven by machine learning, each applied carefully on appropriate compounds, with hybrid prediction strategies being employed where possible.

Structure-based amelioration of PXR transactivation in a novel series of macrocyclic allosteric inhibitors of HIV-1 integrase.

Previous studies have identified a series of imidazo[1,2-a]pyridine (IZP) derivatives as potent allosteric inhibitors of HIV-1 integrase (ALLINIs) and virus infection in cell culture. However, IZPs were also found to be relatively potent activators of the pregnane-X receptor (PXR), raising the specter of induction of CYP-mediated drug disposition pathways. In an attempt to modify PXR activity without affecting anti-HIV-1 activity, rational structure-based design and modeling approaches were used. An X-ray cocrystal structure of (S,S)-1 in the PXR ligand binding domain (LBD) allowed an examination of the potential of rational structural modifications designed to abrogate PXR. The introduction of bulky basic amines at the C-8 position provided macrocyclic IZP derivatives that displayed potent HIV-1 inhibitory activity in cell culture with no detectable PXR transactivation at the highest concentration tested.

Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory.

Accurate prediction of intermolecular interaction energies is a fundamental challenge in electronic structure theory due to their subtle character and small magnitudes relative to total molecular energies. Symmetry adapted perturbation theory (SAPT) provides rigorous quantum mechanical means for computing such quantities directly and accurately, but for a computational cost of at least O(N5), where N is the number of atoms. Here, we report machine learned models of SAPT components with a computational cost that scales asymptotically linearly, O(N). We use modified multi-target Behler-Parrinello neural networks and specialized intermolecular symmetry functions to address the idiosyncrasies of the intermolecular problem, achieving 1.2 kcal mol-1 mean absolute errors on a test set of hydrogen bound complexes including structural data extracted from the Cambridge Structural Database and Protein Data Bank, spanning an interaction energy range of 20 kcal mol-1. Additionally, we recover accurate predictions of the physically meaningful SAPT component energies, of which dispersion and induction/polarization were the easiest to predict and electrostatics and exchange-repulsion are the most difficult.

Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.

We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called "eSim"). Rather than employing heuristic "colors" or user-defined molecular feature types to represent conformation-dependent molecular electrostatics, eSim calculates the similarity of the electrostatic fields of two molecules (in addition to shape and hydrogen-bonding). We present detailed virtual screening performance data on the standard 102 target DUD-E set. In its moderately fast screening mode, eSim running on a single computing core is capable of processing over 60 molecules per second. In this mode, eSim performed significantly better than all alternate methods for which full DUD-E data were available (mean ROC area of 0.74, p [Formula: see text], by paired t-test, compared with the best performing alternate method). In addition, for 92 targets of the DUD-E set where multiple ligand-bound crystal structures were available, screening performance was assessed using alternate ligands or sets thereof (in their bound poses) as similarity targets. Using the joint alignment of five ligands for each protein target, mean ROC area exceeded 0.82 for the 92 targets. Design-focused application of ligand similarity methods depends on accurate predictions of geometric molecular relationships. We comprehensively assessed pose prediction accuracy by curating nearly 400,000 bound ligand pose pairs across the DUD-E targets. Overall, beginning from agnostic initial poses, we observed an 80% success rate for RMSD [Formula: see text] Å  among the top 20 predicted eSim poses. These examples were split roughly 50/50 into cases with high direct atomic overlap (where a shared scaffold exists between a pair) and low direct atomic overlap (where where a ligand pair has dissimilar scaffolds but largely occupies the same space). Within the high direct atomic overlap subset, the pose prediction success rate was 93%. For the more challenging subset (where dissimilar scaffolds are to be aligned), the success rate was 70%. The eSim approach enables both large-scale screening and rational design of ligands and is rooted in physically meaningful, non-heuristic, molecular comparisons.

Tyrosine Kinase 2-mediated Signal Transduction in T Lymphocytes Is Blocked by Pharmacological Stabilization of Its Pseudokinase Domain.

Inhibition of signal transduction downstream of the IL-23 receptor represents an intriguing approach to the treatment of autoimmunity. Using a chemogenomics approach marrying kinome-wide inhibitory profiles of a compound library with the cellular activity against an IL-23-stimulated transcriptional response in T lymphocytes, a class of inhibitors was identified that bind to and stabilize the pseudokinase domain of the Janus kinase tyrosine kinase 2 (Tyk2), resulting in blockade of receptor-mediated activation of the adjacent catalytic domain. These Tyk2 pseudokinase domain stabilizers were also shown to inhibit Tyk2-dependent signaling through the Type I interferon receptor but not Tyk2-independent signaling and transcriptional cellular assays, including stimulation through the receptors for IL-2 (JAK1- and JAK3-dependent) and thrombopoietin (JAK2-dependent), demonstrating the high functional selectivity of this approach. A crystal structure of the pseudokinase domain liganded with a representative example showed the compound bound to a site analogous to the ATP-binding site in catalytic kinases with features consistent with high ligand selectivity. The results support a model where the pseudokinase domain regulates activation of the catalytic domain by forming receptor-regulated inhibitory interactions. Tyk2 pseudokinase stabilizers, therefore, represent a novel approach to the design of potent and selective agents for the treatment of autoimmunity.

3D matched pairs: integrating ligand- and structure-based knowledge for ligand design and receptor annotation.

We describe an extension to the matched molecular pairs approach that merges pairwise activity differences with three-dimensional contextual information derived from X-ray crystal structures and binding pose predictions. The incorporation of 3D binding poses allows the direct comparison of structural changes to diverse chemotypes in particular binding pockets, facilitating the transfer of SAR from one series to another. Integrating matched pair data with the receptor structure can also highlight activity patterns within the binding site--for example, "hot spot" regions can be visualized where changes in the ligand structure are more likely to impact activity. The method is illustrated using P38α structural and activity data to generate novel hybrid ligands, identify SAR transfer networks, and annotate the receptor binding site.

Aspartic Acid Isomerization Characterized by High Definition Mass Spectrometry Significantly Alters the Bioactivity of a Novel Toxin from Poecilotheria.

Research in toxinology has created a pharmacological paradox. With an estimated 220,000 venomous animals worldwide, the study of peptidyl toxins provides a vast number of effector molecules. However, due to the complexity of the protein-protein interactions, there are fewer than ten venom-derived molecules on the market. Structural characterization and identification of post-translational modifications are essential to develop biological lead structures into pharmaceuticals. Utilizing advancements in mass spectrometry, we have created a high definition approach that fuses conventional high-resolution MS-MS with ion mobility spectrometry (HDMSE) to elucidate these primary structure characteristics. We investigated venom from ten species of "tiger" spider (Genus: Poecilotheria) and discovered they contain isobaric conformers originating from non-enzymatic Asp isomerization. One conformer pair conserved in five of ten species examined, denominated PcaTX-1a and PcaTX-1b, was found to be a 36-residue peptide with a cysteine knot, an amidated C-terminus, and isoAsp33Asp substitution. Although the isomerization of Asp has been implicated in many pathologies, this is the first characterization of Asp isomerization in a toxin and demonstrates the isomerized product's diminished physiological effects. This study establishes the value of a HDMSE approach to toxin screening and characterization.

Downregulation of vascular endothelial growth factor and induction of tumor dormancy by 15-lipoxygenase-2 in prostate cancer.

The enzyme 15-lipoxygenase-2 (15-LOX-2) utilizes arachidonic acid, a polyunsaturated fatty acid, to synthesize 15(S)-hydroxyeicosatetraenoic acid. Abundantly expressed in normal prostate epithelium but frequently suppressed in the cancerous tissues, 15-LOX-2 has been suggested as a functional suppressor of prostate cancer, but the mechanism(s) involved remains unknown. To study the functional role of 15-LOX-2 in prostate cancer, we expressed 15-LOX-2 as a fusion protein with GFP in DU145 and PC-3 cells and found that 15-LOX-2 increased cell cycle arrest at G0/G1 phase. When injected into athymic nu/nu mice, prostate cancer cells with 15-LOX-2 expression could still form palpable tumors without significant changes in tumorigenicity. But, the tumors with 15-LOX-2 expression grew significantly slower than those derived from vector controls and were kept dormant for a long period of time. Histological evaluation revealed an increase in cell death in tumors derived from prostate cancer cells with 15-LOX-2 expression, while in vitro cell culture conditions, no such increase in apoptosis was observed. Further studies found that the expression of vascular endothelial growth factor A (VEGF-A) was significantly reduced in prostate cancer cells with 15-LOX-2 expression restored. Our studies suggest that 15-LOX-2 suppresses VEGF gene expression and sustains tumor dormancy in prostate cancer. Loss of 15-LOX-2 functionalities, therefore, represents a key step for prostate cancer cells to exit from dormancy and embark on malignant progression in vivo.

Grid computing in large pharmaceutical molecular modeling.

Most major pharmaceutical companies have employed grid computing to expand their compute resources with the intention of minimizing additional financial expenditure. Historically, one of the issues restricting widespread utilization of the grid resources in molecular modeling is the limited set of suitable applications amenable to coarse-grained parallelization. Recent advances in grid infrastructure technology coupled with advances in application research and redesign will enable fine-grained parallel problems, such as quantum mechanics and molecular dynamics, which were previously inaccessible to the grid environment. This will enable new science as well as increase resource flexibility to load balance and schedule existing workloads.

A simple strategy for mitigating the effect of data variability on the identification of active chemotypes from high-throughput screening data.

Among the several goals of a high-throughput screening campaign is the identification of as many active chemotypes as possible for further evaluation. Often, however, the number of concentration response curves (e.g., IC(50)s or K(i)s) that can be collected following a primary screen is limited by practical constraints such as protein supply, screening workload, and so forth. One possible approach to this dilemma is to cluster the hits from the primary screen and sample only a few compounds from each cluster. This introduces the question as to how many compounds must be selected from a cluster to ensure that an active compound is identified, if it exists at all. This article seeks to address this question using a Monte Carlo simulation in which the dependence of the success of sampling is directly linked to screening data variability. Furthermore, the authors demonstrate that the use of replicated compounds in the screening collection can easily assess this variability and provide a priori guidance to the screener and chemist as to the extent of sampling required to maximize chemotype identification during the triage process. The individual steps of the Monte Carlo simulation provide insight into the correspondence between the percentage inhibition and eventual IC(50) curves.

Development of an in silico model for predicting efflux substrates in Caco-2 cells.

P-glycoprotein (P-gp) is an ATP dependent efflux transporter protein that has been demonstrated to play a critical role in affecting the absorption, metabolism, elimination and toxicity (ADMET) characteristics of a large number of marketed drugs. Therefore, it is important to evaluate whether or not compounds of interest are likely to interact with P-gp and/or other efflux transporters. An in silico efflux substrate (potential substrate of P-gp and or other transporters) classification model has been developed based on in vitro bi-directional Caco-2 cell permeability and five descriptors, using 14 marketed drugs and >100 discovery compounds synthesized at Bristol-Myers Squibb PRI. The model suggests that efflux substrates tend to contain electron deficient aromatic rings, are highly branched, and most contain tertiary nitrogen. This model demonstrated approximately 80% predictability of both non-substrates and substrates from a training set of 125 compounds. For a validation set of 46 compounds the predictability was approximately 72% for non-substrates and approximately 89% for substrates. The model has the potential to be used both as a filter for library designs to identify potential efflux substrates in early discovery as well as a primary screening methodology to identify the efflux substrate potential of drug candidates.

A reexamination of poneratoxin from the venom of the bullet ant Paraponera clavata.

In 1991, Piek et al. [45] described a voltage-gated sodium channel (VGSC) modifier from "bullet ant" (Paraponera clavata) venom they called poneratoxin (PoTx). Using UV chromatography and Edman degradation they showed two "identical peptides" of 25 residues. We reinvestigated PoTx using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-TMS). De novo sequencing showed the two peptides were actually structurally different peptides: the originally described PoTx and a glycyl pro-peptide (glycyl-PoTx) that lacks C-terminus amidation. We examined P. clavata venom from different geographical locations and discovered two additional PoTx analogs: an A23E substitution analog and a D22N; A23V substitutions analog. We tested PoTx and these three natural analogs on the mammalian sensory voltage-gated sodium channel, Nav1.7, using whole cell voltage-clamp. PoTx and each analog induced slowly activating currents in response to small depolarizing steps and sustained currents due to blockade of channel inactivation, similar to that described previously in skeletal muscle [19]. Glycyl-PoTx had the same potency and efficacy as PoTx. A23E PoTx, with a decrease in both C-terminal net positive charge and hydrophobicity, had an eight-fold reduction in potency compared to PoTx. In contrast, the D22N; A23V PoTx, with an increase in both C-terminal net positive charge and hydrophobicity, had a nearly five-fold increase in potency compared to PoTx. We found that changes in PoTx C-terminus caused a significant change in PoTx potency.

Boosting Endogenous Resistance of Brain to Ischemia.

Most survivors of ischemic stroke remain physically disabled and require prolonged rehabilitation. However, some stroke victims achieve a full neurological recovery suggesting that the human brain can defend itself against ischemic injury, but the protective mechanisms are unknown. This study used selective pharmacological agents and a rat model of cerebral ischemic stroke to detect endogenous brain protective mechanisms that require activation of α7 nicotinic acetylcholine receptors (nAChRs). This endogenous protection was found to be (1) limited to less severe injuries; (2) significantly augmented by intranasal administration of a positive allosteric modulator of α7 nAChRs, significantly reducing brain injury and neurological deficits after more severe ischemic injuries; and (3) reduced by inhibition of calcium/calmodulin-dependent kinase-II. The physiological role of α7 nAChRs remains largely unknown. The therapeutic activation of α7 nAChRs after cerebral ischemia may serve as an important physiological responsibility of these ubiquitous receptors and holds a significant translational potential.

NMR structure of a potent small molecule inhibitor bound to human keratinocyte fatty acid-binding protein.

The NMR structure is presented for compound 1 (BMS-480404) (Ki = 33 (+/-2) nM) bound to keratinocyte fatty acid-binding protein. This article describes interactions between a high affinity drug-like compound and a member of the fatty acid-binding protein family. A benzyl group ortho to the mandelic acid in 1 occupies an area of the protein that fatty acids do not normally contact. Similar to that in the kFABP-palmitic acid structure, the acid moiety in 1 is proximal to R129 and Y131. Computational modeling indicates that the acid moiety in 1 interacts indirectly via a modeled water molecule to R109.

Serial biopsies/fine-needle aspirates and their assessment.

Taking a series of repeat biopsies or fine needle aspirates of a tumor during the course of therapy can provide information about treatment-induced changes in tumor biomarkers and help monitor patient response to adjuvant therapy. It is hoped that analysis of biomarkers in serial biopsies will also further our understanding of the molecular mechanisms that determine a tumor's response or resistance to therapy, may facilitate investigation of molecular biology of tumor response, and may provide useful information informing the development of new drugs for breast cancer therapy. In this chapter, practical, clinical considerations in the taking of repeat biopsies are considered and protocols for the taking of fine needle aspirates and core-cut/trucut biopsies are detailed. Their assessment for biomarkers indicative of cellular proliferation, apoptosis, and endocrine response/resistance such as estrogen and progesterone receptor status, HER2 and epidermal growth factor receptor are considered.

Benzonatate inhibition of voltage-gated sodium currents.

Benzonatate was FDA-approved in 1958 as an antitussive. Its mechanism of action is thought to be anesthesia of vagal sensory nerve fibers that mediate cough. Vagal sensory neurons highly express the Nav1.7 subtype of voltage-gated sodium channels, and inhibition of this channel inhibits the cough reflex. Local anesthetics inhibit voltage-gated sodium channels, but there are no reports of whether benzonatate affects these channels. Our hypothesis is that benzonatate inhibits Nav1.7 voltage-gated sodium channels. We used whole cell voltage clamp recording to test the effects of benzonatate on voltage-gated sodium (Na(+)) currents in two murine cell lines, catecholamine A differentiated (CAD) cells, which express primarily Nav1.7, and N1E-115, which express primarily Nav1.3. We found that, like local anesthetics, benzonatate strongly and reversibly inhibits voltage-gated Na(+) channels. Benzonatate causes both tonic and phasic inhibition. It has greater effects on channel inactivation than on activation, and its potency is much greater at depolarized potentials, indicating inactivated-state-specific effects. Na(+) currents in CAD cells and N1E-115 cells are similarly affected, indicating that benzonatate is not Na(+) channel subtype-specific. Benzonatate is a mixture of polyethoxy esters of 4-(butylamino) benzoic acid having varying degrees of hydrophobicity. We found that Na(+) currents are inhibited most potently by a benzonatate fraction containing the 9-ethoxy component. Detectable effects of benzonatate occur at concentrations as low as 0.3 µM, which has been reported in humans. We conclude that benzonatate has local anesthetic-like effects on voltage-gated sodium channels, including Nav1.7, which is a possible mechanism for cough suppression by the drug.

Molecular properties that influence the oral bioavailability of drug candidates.

Oral bioavailability measurements in rats for over 1100 drug candidates studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline) have allowed us to analyze the relative importance of molecular properties considered to influence that drug property. Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count (sum of donors and acceptors) are found to be important predictors of good oral bioavailability, independent of molecular weight. That on average both the number of rotatable bonds and polar surface area or hydrogen bond count tend to increase with molecular weight may in part explain the success of the molecular weight parameter in predicting oral bioavailability. The commonly applied molecular weight cutoff at 500 does not itself significantly separate compounds with poor oral bioavailability from those with acceptable values in this extensive data set. Our observations suggest that compounds which meet only the two criteria of (1) 10 or fewer rotatable bonds and (2) polar surface area equal to or less than 140 A(2) (or 12 or fewer H-bond donors and acceptors) will have a high probability of good oral bioavailability in the rat. Data sets for the artificial membrane permeation rate and for clearance in the rat were also examined. Reduced polar surface area correlates better with increased permeation rate than does lipophilicity (C log P), and increased rotatable bond count has a negative effect on the permeation rate. A threshold permeation rate is a prerequisite of oral bioavailability. The rotatable bond count does not correlate with the data examined here for the in vivo clearance rate in the rat.

An online system for metabolic network analysis.

Metabolic networks have become one of the centers of attention in life sciences research with the advancements in the metabolomics field. A vast array of studies analyzes metabolites and their interrelations to seek explanations for various biological questions, and numerous genome-scale metabolic networks have been assembled to serve for this purpose. The increasing focus on this topic comes with the need for software systems that store, query, browse, analyze and visualize metabolic networks. PathCase Metabolomics Analysis Workbench (PathCaseMAW) is built, released and runs on a manually created generic mammalian metabolic network. The PathCaseMAW system provides a database-enabled framework and Web-based computational tools for browsing, querying, analyzing and visualizing stored metabolic networks. PathCaseMAW editor, with its user-friendly interface, can be used to create a new metabolic network and/or update an existing metabolic network. The network can also be created from an existing genome-scale reconstructed network using the PathCaseMAW SBML parser. The metabolic network can be accessed through a Web interface or an iPad application. For metabolomics analysis, steady-state metabolic network dynamics analysis (SMDA) algorithm is implemented and integrated with the system. SMDA tool is accessible through both the Web-based interface and the iPad application for metabolomics analysis based on a metabolic profile. PathCaseMAW is a comprehensive system with various data input and data access subsystems. It is easy to work with by design, and is a promising tool for metabolomics research and for educational purposes. Database URL: http://nashua.case.edu/PathwaysMAW/Web.

iPathCase(KEGG): An iPad interface for KEGG metabolic pathways.

BACKGROUND: Kyoto Encyclopedia of Genes and Genomes (KEGG) is an online and integrated molecular database for several organisms. KEGG has been a highly useful site, helping domain scientists understand, research, study, and teach metabolisms by linking sequenced genomes to higher level systematic functions. KEGG databases are accessible through the web pages of the system, but the capabilities of the web interface are limited. Third party systems have been built over the KEGG data to provide extensive functionalities. However, there have been no attempts towards providing a tablet interface for KEGG data. Recognizing the rise of mobile technologies and the importance of tablets in education, this paper presents the design and implementation of iPathCase(KEGG), an iPad interface for KEGG data, which is empowered with multiple browsing and visualization capabilities. RESULTS: iPathCase(KEGG) has been implemented and is available, free of charge, in the Apple App Store (locatable by searching for "Pathcase" in the app store). The application provides browsing and interactive visualization functionalities on the KEGG data. Users can pick pathways, visualize them, and see detail pages of reactions and molecules using the multi-touch interface of iPad. CONCLUSIONS: iPathCase(KEGG) provides a mobile interface to access KEGG data. Interactive visualization and browsing functionalities let users to interact with the data in multiple ways. As the importance of tablets and their usage in research education continue to rise, we think iPathCase(KEGG) will be a useful tool for life science instructors and researchers.