Linguistic measures of chemical diversity and the "keywords" of molecular collections

Computerized linguistic analyses have proven of immense value in comparing and searching through large text collections ("corpora"), including those deposited on the Internet-indeed, it would nowadays be hard to imagine browsing the Web without, for instance, search algorithms extracting most appropriate keywords from documents. This paper describes how such corpus-linguistic concepts can be extended to chemistry based on characteristic "chemical words" that span more than traditional functional groups and, instead, look at common structural fragments molecules share. Using these words, it is possible to quantify the diversity of chemical collections/databases in new ways and to define molecular "keywords" by which such collections are best characterized and annotated

A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

<p>Abstract</p> <p>Background</p> <p>The current chemical space of known small molecules is estimated to exceed 10⁶⁰structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.</p> <p>Results</p> <p>The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.</p> <p>Conclusions</p> <p>Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.</p

Molecular Dynamics Simulation Study and Hybrid Pharmacophore Model Development in Human LTA4H Inhibitor Design

Human leukotriene A4 hydrolase (hLTA4H) is a bi-functional enzyme catalyzes the hydrolase and aminopeptidase functions upon the fatty acid and peptide substrates, respectively, utilizing the same but overlapping binding site. Particularly the hydrolase function of this enzyme catalyzes the rate-limiting step of the leukotriene (LT) cascade that converts the LTA4 to LTB4. This product is a potent pro-inflammatory activator of inflammatory responses and thus blocking this conversion provides a valuable means to design anti-inflammatory agents. Four structurally very similar chemical compounds with highly different inhibitory profile towards the hydrolase function of hLTA4H were selected from the literature. Molecular dynamics (MD) simulations of the complexes of hLTA4H with these inhibitors were performed and the results have provided valuable information explaining the reasons for the differences in their biological activities. Binding mode analysis revealed that the additional thiophene moiety of most active inhibitor helps the pyrrolidine moiety to interact the most important R563 and K565 residues. The hLTA4H complexes with the most active compound and substrate were utilized in the development of hybrid pharmacophore models. These developed pharmacophore models were used in screening chemical databases in order to identify lead candidates to design potent hLTA4H inhibitors. Final evaluation based on molecular docking and electronic parameters has identified three compounds of diverse chemical scaffolds as potential leads to be used in novel and potent hLTA4H inhibitor design

Data mining of high density genomic variant data for prediction of Alzheimer's disease risk

<p>Abstract</p> <p>Background</p> <p>The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods.</p> <p>Methods</p> <p>Two different approaches were devised to select SNPs associated with LOAD in a publicly available GWAS data set consisting of three cohorts. In both approaches, single-locus analysis (logistic regression) was conducted to filter the data with a less conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs used next in multi-locus analysis (random forest (RF)). In the second approach, we took into account prior biological knowledge, and performed sample stratification and linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci to input into the RF classifier construction step.</p> <p>Results</p> <p>The first approach gave 199 SNPs mostly associated with genes in calcium signaling, cell adhesion, endocytosis, immune response, and synaptic function. These SNPs together with <it>APOE and GAB2 </it>SNPs formed a predictive subset for LOAD status with an average error of 9.8% using 10-fold cross validation (CV) in RF modeling. Nineteen variants in LD with <it>ST5, TRPC1, ATG10, ANO3, NDUFA12, and NISCH </it>respectively, genes linked directly or indirectly with neurobiology, were identified with the second approach. These variants were part of a model that included <it>APOE </it>and <it>GAB2 </it>SNPs to predict LOAD risk which produced a 10-fold CV average error of 17.5% in the classification modeling.</p> <p>Conclusions</p> <p>With the two proposed approaches, we identified a large subset of SNPs in genes mostly clustered around specific pathways/functions and a smaller set of SNPs, within or in proximity to five genes not previously reported, that may be relevant for the prediction/understanding of AD.</p

Computer Aided Identification of Small Molecules Disrupting uPAR/α5β1- Integrin Interaction: A New Paradigm for Metastasis Prevention

Disseminated dormant cancer cells can resume growth and eventually form overt metastases, but the underlying molecular mechanism responsible for this change remains obscure. We previously established that cell surface interaction between urokinase receptor (uPAR) and alpha5beta1-integrin initiates a sequel of events, involving MAPK-ERK activation that culminates in progressive cancer growth. We also identified the site on uPAR that binds alpha5beta1-integrin. Disruption of uPAR/integrin interaction blocks ERK activation and forces cancer cells into dormancy.Using a target structure guided computation docking we identified 68 compounds from a diversity library of 13,000 small molecules that were predicted to interact with a previously identified integrin-binding site on uPAR. Of these 68 chemical hits, ten inhibited ERK activation in a cellular assay and of those, 2 compounds, 2-(Pyridin-2-ylamino)-quinolin-8-ol and, 2,2'-(methylimino)di (8-quinolinol) inhibited ERK activation by disrupting the uPAR/integrins interaction. These two compounds, when applied in vivo, inhibited ERK activity and tumor growth and blocked metastases of a model head and neck carcinoma.We showed that interaction between two large proteins (uPAR and alpha5beta1-integrin) can be disrupted by a small molecule leading to profound downstream effects. Because this interaction occurs in cells with high uPAR expression, a property almost exclusive to cancer cells, we expect a new therapy based on these lead compounds to be cancer cell specific and minimally toxic. This treatment, rather than killing disseminated metastatic cells, should induce a protracted state of dormancy and prevent overt metastases

The role of ligand efficiency metrics in drug discovery

The judicious application of ligand or binding efficiencies, which quantify the molecular properties required to gain binding affinity for a drug target, is gaining traction in the selection and optimisation of fragments, hits, and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimised ligand efficiency values for their target. Optimising ligand efficiencies based on both molecular size and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the molecular inflation that pervades current practice in medicinal chemistry, and to increase the developability of drug candidates

Rad51 Polymerization Reveals a New Chromatin Remodeling Mechanism

Rad51 protein is a well known protagonist of homologous recombination in eukaryotic cells. Rad51 polymerization on single-stranded DNA and its role in presynaptic filament formation have been extensively documented. Rad51 polymerizes also on double-stranded DNA but the significance of this filament formation remains unclear. We explored the behavior of Saccharomyces cerevisiae Rad51 on dsDNA and the influence of nucleosomes on Rad51 polymerization mechanism to investigate its putative role in chromatin accessibility to recombination machinery. We combined biochemical approaches, transmission electron microscopy (TEM) and atomic force microscopy (AFM) for analysis of the effects of the Rad51 filament on chromatinized templates. Quantitative analyses clearly demonstrated the occurrence of chromatin remodeling during nucleoprotein filament formation. During Rad51 polymerization, recombinase proteins moved all the nucleosomal arrays in front of the progressing filament. This polymerization process had a powerful remodeling effect, as Rad51 destabilized the nucleosomes along considerable stretches of DNA. Similar behavior was observed with RecA. Thus, recombinase polymerization is a powerful mechanism of chromatin remodeling. These remarkable features open up new possibilities for understanding DNA recombination and reveal new types of ATP-dependent chromatin dynamics

Association between IgM Anti-Herpes Simplex Virus and Plasma Amyloid-Beta Levels

OBJECTIVE: Herpes simplex virus (HSV) reactivation has been identified as a possible risk factor for Alzheimer's disease (AD) and plasma amyloid-beta (Aβ) levels might be considered as possible biomarkers of the risk of AD. The aim of our study was to investigate the association between anti-HSV antibodies and plasma Aβ levels. METHODS: The study sample consisted of 1222 subjects (73.9 y in mean) from the Three-City cohort. IgM and IgG anti-HSV antibodies were quantified using an ELISA kit, and plasma levels of Aβ(1-40) and Aβ(1-42) were measured using an xMAP-based assay technology. Cross-sectional analyses of the associations between anti-HSV antibodies and plasma Aβ levels were performed by multi-linear regression. RESULTS: After adjustment for study center, age, sex, education, and apolipoprotein E-e4 polymorphism, plasma Aβ(1-42) and Aβ(1-40) levels were specifically inversely associated with anti-HSV IgM levels (β = -20.7, P=0.001 and β = -92.4, P=0.007, respectively). In a sub-sample with information on CLU- and CR1-linked SNPs genotyping (n=754), additional adjustment for CR1 or CLU markers did not modify these associations (adjustment for CR1 rs6656401, β = -25.6, P=0.002 for Aβ(1-42) and β = -132.7, P=0.002 for Aβ(1-40;) adjustment for CLU rs2279590, β = -25.6, P=0.002 for Aβ(1-42) and β = -134.8, P=0.002 for Aβ(1-40)). No association between the plasma Aβ(1-42)-to-Aβ(1-40) ratio and anti-HSV IgM or IgG were evidenced. CONCLUSION: High anti-HSV IgM levels, markers of HSV reactivation, are associated with lower plasma Aβ(1-40) and Aβ(1-42) levels, which suggest a possible involvement of the virus in the alterations of the APP processing and potentially in the pathogenesis of AD in human

Epigenetic re-wiring of breast cancer by pharmacological targeting of C-terminal binding protein

The C-terminal binding protein (CtBP) is an NADH-dependent dimeric family of nuclear proteins that scaffold interactions between transcriptional regulators and chromatin-modifying complexes. Its association with poor survival in several cancers implicates CtBP as a promising target for pharmacological intervention. We employed computer-assisted drug design to search for CtBP inhibitors, using quantitative structure-activity relationship (QSAR) modeling and docking. Functional screening of these drugs identified 4 compounds with low toxicity and high water solubility. Micro molar concentrations of these CtBP inhibitors produces significant de-repression of epigenetically silenced pro-epithelial genes, preferentially in the triple-negative breast cancer cell line MDA-MB-231. This epigenetic reprogramming occurs through eviction of CtBP from gene promoters; disrupted recruitment of chromatin-modifying protein complexes containing LSD1, and HDAC1; and re-wiring of activating histone marks at targeted genes. In functional assays, CtBP inhibition disrupts CtBP dimerization, decreases cell migration, abolishes cellular invasion, and improves DNA repair. Combinatorial use of CtBP inhibitors with the LSD1 inhibitor pargyline has synergistic influence. Finally, integrated correlation of gene expression in breast cancer patients with nuclear levels of CtBP1 and LSD1, reveals new potential therapeutic vulnerabilities. These findings implicate a broad role for this class of compounds in strategies for epigenetically targeted therapeutic intervention

Development of an in-vivo active reversible butyrylcholinesterase inhibitor

Alzheimer’s disease (AD) is characterized by severe basal forebrain cholinergic deficit, which results in progressive and chronic deterioration of memory and cognitive functions. Similar to acetylcholinesterase, butyrylcholinesterase (BChE) contributes to the termination of cholinergic neurotransmission. Its enzymatic activity increases with the disease progression, thus classifying BChE as a viable therapeutic target in advanced AD. Potent, selective and reversible human BChE inhibitors were developed. The solved crystal structure of human BChE in complex with the most potent inhibitor reveals its binding mode and provides the molecular basis of its low nanomolar potency. Additionally, this compound is noncytotoxic and has neuroprotective properties. Furthermore, this inhibitor moderately crosses the blood-brain barrier and improves memory, cognitive functions and learning abilities of mice in a model of the cholinergic deficit that characterizes AD, without producing acute cholinergic adverse effects. Our study provides an advanced lead compound for developing drugs for alleviating symptoms caused by cholinergic hypofunction in advanced AD