Energetics of Endotoxin Recognition in the Toll-Like Receptor 4 Innate Immune Response.

Bacterial outer membrane lipopolysaccharide (LPS) potently stimulates the mammalian innate immune system, and can lead to sepsis, the primary cause of death from infections. LPS is sensed by Toll-like receptor 4 (TLR4) in complex with its lipid-binding coreceptor MD-2, but subtle structural variations in LPS can profoundly modulate the response. To better understand the mechanism of LPS-induced stimulation and bacterial evasion, we have calculated the binding affinity to MD-2 of agonistic and antagonistic LPS variants including lipid A, lipid IVa, and synthetic antagonist Eritoran, and provide evidence that the coreceptor is a molecular switch that undergoes ligand-induced conformational changes to appropriately activate or inhibit the receptor complex.

Activity cliffs and activity cliff generators based on chemotype-related activity landscapes.

Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized.

Discovery and development of DNA methyltransferase inhibitors using in silico approaches.

Multiple strategies have evolved during the past few years to advance epigenetic compounds targeting DNA methyltransferases (DNMTs). Significant progress has been made in HTS, lead optimization and determination of 3D structures of DNMTs. In light of the emerging concept of epi-informatics, computational approaches are employed to accelerate the development of DNMT inhibitors helping to screen chemical databases, mine the DNMT-relevant chemical space, uncover SAR and design focused libraries.

In contrast to other species, α-Galactosylceramide (α-GalCer) is not an immunostimulatory NKT cell agonist in horses.

α-GalCer is a potent immunomodulatory molecule that is presented to NKT cells via the CD1 antigen-presenting system. We hypothesized that when used as an adjuvant α-GalCer would induce protective immune responses against Rhodococcus equi, an important pathogen of young horses. Here we demonstrate that the equine CD1d molecule shares most features found in CD1d from other species and has a suitable lipid-binding groove for presenting glycolipids to NKT cells.

The interplay between molecular modeling and chemoinformatics to characterize protein-ligand and protein-protein interactions landscapes for drug discovery.

Protein-ligand and protein-protein interactions play a fundamental role in drug discovery. A number of computational approaches have been developed to characterize and use the knowledge of such interactions that can lead to drug candidates and eventually compounds in the clinic. With the increasing structural information of protein-ligand and protein-protein complexes, the combination of molecular modeling and chemoinformatics approaches are often required for the efficient analysis of a large number of such complexes.

Synthesis and characterization of novel 2-amino-chromene-nitriles that target Bcl-2 in acute myeloid leukemia cell lines.

The anti-apoptotic protein Bcl-2 is a well-known and attractive therapeutic target for cancer. In the present study the solution-phase T3P-DMSO mediated efficient synthesis of 2-amino-chromene-3-carbonitriles from alcohols, malanonitrile and phenols is reported. These novel 2-amino-chromene-3-carbonitriles showed cytotoxicity in human acute myeloid leukemia (AML) cell lines.

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis.

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data.

Novel synthetic biscoumarins target tumor necrosis factor-α in hepatocellular carcinoma in vitro and in vivo.

TNF is a pleotropic cytokine known to be involved in the progression of several pro-inflammatory disorders. Many therapeutic agents have been designed to counteract the effect of TNF in rheumatoid arthritis as well as a number of cancers. In the present study we have synthesized and evaluated the anti-cancer activity of novel biscoumarins in vitro and in vivo.

Novel benzoxazine-based aglycones block glucose uptake in vivo by inhibiting glycosidases.

Glycoside hydrolases catalyze the selective hydrolysis of glycosidic bonds in oligosaccharides, polysaccharides, and their conjugates. β-glucosidases occur in all domains of living organisms and constitute a major group among glycoside hydrolases. On the other hand, the benzoxazinoids occur in living systems and act as stable β-glucosides, such as 2-(2,4-dihydroxy-7-methoxy-2H-1,4-benzoxazin-3(4H)-one)-β-D-gluco-pyranose, which hydrolyse to an aglycone DIMBOA.

Proteochemometric modeling in a Bayesian framework.

Proteochemometrics (PCM) is an approach for bioactivity predictive modeling which models the relationship between protein and chemical information. Gaussian Processes (GP), based on Bayesian inference, provide the most objective estimation of the uncertainty of the predictions, thus permitting the evaluation of the applicability domain (AD) of the model. Furthermore, the experimental error on bioactivity measurements can be used as input for this probabilistic model.

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers.

Background: The prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching.

Investigating and Predicting how Biology Changes Molecules and Their Properties.

Most molecules are transformed and transported by specific metabolising enzymes and transporters resulting in changes in their bioactivities, pharmacokinetics and toxicity profiles. This is a key consideration in the design of drugs. Ideally, when medicines have performed their task, they need to fade away gracefully, and not introduce unexpected or untoward biological effects.

Efficient Characterization of Protein Cavities within Molecular Simulation Trajectories: trj_cavity.

Protein cavities and tunnels are critical in determining phenomena such as ligand binding, molecular transport, and enzyme catalysis. Molecular dynamics (MD) simulations enable the exploration of the flexibility and conformational plasticity of protein cavities, extending the information available from static experimental structures relevant to, for example, drug design. Here, we present a new tool (trj_cavity) implemented within the GROMACS ( www.

Rationalization of activity cliffs of a sulfonamide inhibitor of DNA methyltransferases with induced-fit docking.

Inhibitors of human DNA methyltransferases (DNMT) are of increasing interest to develop novel epi-drugs for the treatment of cancer and other diseases. As the number of compounds with reported DNMT inhibition is increasing, molecular docking is shedding light to elucidate their mechanism of action and further interpret structure-activity relationships. Herein, we present a structure-based rationalization of the activity of SW155246, a distinct sulfonamide compound recently reported as an inhibitor of human DNMT1 obtained from high-throughput screening.

Toward drug repurposing in epigenetics: olsalazine as a hypomethylating compound active in a cellular context.

DNA hypomethylating drugs that act on DNA methyltransferase (DNMT) isoforms are promising anticancer agents. By using a well-characterized live-cell system to measure DNA methylation revisions (imprints), we characterize olsalazine, an approved anti-inflammatory drug, as a novel DNA hypomethylating agent. The cell-based screen used in this work is highly tractable, internally controlled, and well-suited for a drug repurposing strategy in epigenetics.

Are phylogenetic trees suitable for chemogenomics analyses of bioactivity data sets: the importance of shared active compounds and choosing a suitable data embedding method, as exemplified on Kinases.

Background: 'Phylogenetic trees' are commonly used for the analysis of chemogenomics datasets and to relate protein targets to each other, based on the (shared) bioactivities of their ligands. However, no real assessment as to the suitability of this representation has been performed yet in this area. We aimed to address this shortcoming in the current work, as exemplified by a kinase data set, given the importance of kinases in many diseases as well as the availability of large-scale datasets for analysis.

How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space.

Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, "Bayes Affinity Fingerprints").

Extensions to In Silico Bioactivity Predictions Using Pathway Annotations and Differential Pharmacology Analysis: Application to Xenopus laevis Phenotypic Readouts.

The simultaneous increase of computational power and the availability of chemical and biological data have contributed to the recent popularity of in silico bioactivity prediction algorithms. Such methods are commonly used to infer the 'Mechanism of Action' of small molecules and they can also be employed in cases where full bioactivity profiles have not been established experimentally. However, protein target predictions by themselves do not necessarily capture information about the effect of a compound on a biological system, and hence merging their output with a systems biology approach can help to better understand the complex network modulation which leads to a particular phenotype.

FAst MEtabolizer (FAME): A rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes.

FAst MEtabolizer (FAME) is a fast and accurate predictor of sites of metabolism (SoMs). It is based on a collection of random forest models trained on diverse chemical data sets of more than 20 000 molecules annotated with their experimentally determined SoMs. Using a comprehensive set of available data, FAME aims to assess metabolic processes from a holistic point of view.

The structural basis for endotoxin-induced allosteric regulation of the Toll-like receptor 4 (TLR4) innate immune receptor.

As part of the innate immune system, Toll-like receptor 4 (TLR4) recognizes bacterial cell surface lipopolysaccharide (LPS) by forming a complex with a lipid-binding co-receptor, MD-2. In the presence of agonist, TLR4·MD-2 dimerizes to form an active receptor complex, leading to initiation of intracellular inflammatory signals. TLR4 is of great biomedical interest, but its pharmacological manipulation is complicated because even subtle variations in the structure of LPS can profoundly impact the resultant immunological response.

Full "Laplacianised" posterior naive Bayesian algorithm.

Background: In the last decade the standard Naive Bayes (SNB) algorithm has been widely employed in multi-class classification problems in cheminformatics. This popularity is mainly due to the fact that the algorithm is simple to implement and in many cases yields respectable classification results. Using clever heuristic arguments "anchored" by insightful cheminformatics knowledge, Xia et al.

Lewis acid catalysis and ligand exchange in the asymmetric binaphthol-catalyzed propargylation of ketones.

1,1'-Bi-2-naphthol (BINOL)-derived catalysts catalyze the asymmetric propargylation of ketones. Density functional theory (DFT) calculations show that the reaction proceeds via a closed six-membered transition structure (TS) in which the chiral catalyst undergoes an exchange process with the original cyclic boronate ligand. This leads to a Lewis acid type activation mode, not a Brønsted acid process, which accurately predicts the stereochemical outcome observed experimentally.

Linking Ayurveda and Western medicine by integrative analysis.

In this article, we discuss our recent work in elucidating the mode-of-action of compounds used in traditional medicine including Ayurvedic medicine. Using computational ('in silico') approach, we predict potential targets for Ayurvedic anti-cancer compounds, obtained from the Indian Plant Anticancer Database given its chemical structure. In our analysis, we observed that: (i) the targets predicted can be connected to cancer pathogenesis i.

Selective inhibition of the unfolded protein response: targeting catalytic sites for Schiff base modification.

Constitutive protein misfolding in the endoplasmic reticulum (ER) can lead to cellular toxicity and disease. Consequently, the protein folding environment within the ER is highly optimised and tightly regulated by the unfolded protein response (UPR). The apparent convergence of myriad diseases upon proteostasis in the ER has triggered a broad effort to identify selective inhibitors of the UPR.

Computer-aided (in silico) approaches in the mode-of-action analysis and safety assessment of ostarine and 4-methylamphetamine.

Objective: This study exemplifies computer-aided (in silico) approaches in assessing the risks of new psychoactive substances emerging in the European Union. In this work, we (i) consider the potential of Ostarine exhibiting psychoactivity and (ii) anticipate potential activities and toxicities of 4-methylamphetamine.

Method: The approach, termed in silico target prediction, suggests potential protein targets modulated by compounds given their chemical structure.