A combined computational and experimental approach reveals the structure of a C/EBPβ-Spi1 interaction required for gene transcription.

We previously reported that transcription of the human gene, encoding the proinflammatory cytokine interleukin 1β, depends on long-distance chromatin looping that is stabilized by a mutual interaction between the DNA-binding domains (DBDs) of two transcription factors: Spi1 proto-oncogene at the promoter and CCAAT enhancer-binding protein (C/EBPβ) at a far-upstream enhancer. We have also reported that the C-terminal tail sequence beyond the C/EBPβ leucine zipper is critical for its association with Spi1 an exposed residue (Arg-232) located within a pocket at one end of the Spi1 DNA-recognition helix. Here, combining interaction studies with computational docking and molecular dynamics of existing X-ray structures for the Spi1 and C/EBPβ DBDs, along with the C/EBPβ C-terminal tail sequence, we found that the tail sequence is intimately associated with Arg-232 of Spi1.

The Design, Synthesis, and Characterizations of Spore Germination Inhibitors Effective against an Epidemic Strain of Clostridium difficile.

Clostridium difficile infections (CDI), particularly those caused by the BI/NAP1/027 epidemic strains, are challenging to treat. One method to address this disease is to prevent the development of CDI by inhibiting the germination of C. difficile spores.

LeadOp+R: Structure-Based Lead Optimization With Synthetic Accessibility.

We previously described a structure-based fragment hopping for lead optimization using a pre-docked fragment database, "LeadOp," that conceptually replaced "bad" fragments of a ligand with "good" fragments while leaving the core of the ligand intact thus improving the compound's activity. LeadOp was proven to optimize the query molecules and systematically developed improved analogs for each of our example systems. However, even with the fragment-based design from common building blocks, it is still a challenge for synthesis.

Biological Testing of Organophosphorus-Inactivated Acetylcholinesterase Oxime Reactivators Identified via Virtual Screening.

There is a pressing need for new therapeutics to reactivate covalently inactivated acetylcholinesterase (AChE) due to exposure to organophosphorus (OP) compounds. Current reactivation therapeutics (RTs) are not broad-spectrum and suffer from other liabilities, specifically the inability to cross the blood-brain-barrier. Additionally, the chemical diversity of available therapeutics is small, limiting opportunities for structure-activity relationship (SAR) studies to aid in the design of more effective compounds.

Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models.

Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models.

Experimental and computational studies of physicochemical properties influence NSAID-cyclodextrin complexation.

The objective of this research was to investigate physicochemical properties of an active pharmaceutical ingredient (API) that influence cyclodextrin complexation through experimental and computational studies. Native β-cyclodextrin (B-CD) and two hydroxypropyl derivatives were first evaluated by conventional phase solubility experiments for their ability to complex four poorly water-soluble nonsteroidal anti-inflammatory drugs (NSAIDs). Differential scanning calorimetry was used to confirm complexation.

Exploring the physicochemical properties of oxime-reactivation therapeutics for cyclosarin, sarin, tabun, and VX inactivated acetylcholinesterase.

The inactivation of acetylcholinesterase (AChE) by organophosphorus agent (OP) compounds is a serious problem regardless of how the individual was exposed. The reactivation of OP-inactivated AChE is dependent on the OP conjugate, and commonly a specific oxime is better at reactivating a specific OP conjugate than several diverse OP conjugates. The presented research explores the physicochemical properties needed for the reactivation of OP-inactivated AChE.

CSAR data set release 2012: ligands, affinities, complexes, and docking decoys.

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose ( www.csardock.

Oversampling to overcome overfitting: exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods.

The traditional biological assay is very time-consuming, and thus the ability to quickly screen large numbers of compounds against a specific biological target is appealing. To speed up the biological evaluation of compounds, high-throughput screening is widely used in the fields of biomedical, biological information, and drug discovery. The research presented in this study focuses on the use of support vector machines, a machine learning method, various classes of molecular descriptors, and different sampling techniques to overcome overfitting to classify compounds for cytotoxicity with respect to the Jurkat cell line.

Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes.

Little attention has been given to the selection of trial descriptor sets when designing a QSAR analysis even though a great number of descriptor classes, and often a greater number of descriptors within a given class, are now available. This paper reports an effort to explore interrelationships between QSAR models and descriptor sets. Zhou and co-workers (Zhou et al.

Predictive toxicology modeling: protocols for exploring hERG classification and Tetrahymena pyriformis end point predictions.

The inclusion and accessibility of different methodologies to explore chemical data sets has been beneficial to the field of predictive modeling, specifically in the chemical sciences in the field of Quantitative Structure-Activity Relationship (QSAR) modeling. This study discusses using contemporary protocols and QSAR modeling methods to properly model two biomolecular systems that have historically not performed well using traditional and three-dimensional QSAR methodologies. Herein, we explore, analyze, and discuss the creation of a classification human Ether-a-go-go Related Gene (hERG) potassium channel model and a continuous Tetrahymena pyriformis (T.

The great descriptor melting pot: mixing descriptors for the common good of QSAR models.

The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage.

CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions.

As part of the Community Structure-Activity Resource (CSAR) center, a set of 343 high-quality, protein-ligand crystal structures were assembled with experimentally determined K(d) or K(i) information from the literature. We encouraged the community to score the crystallographic poses of the complexes by any method of their choice. The goal of the exercise was to (1) evaluate the current ability of the field to predict activity from structure and (2) investigate the properties of the complexes and methods that appear to hinder scoring.

A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets.

The human ether-a-go-go related gene (hERG) potassium ion channel plays a key role in cardiotoxicity and is therefore a key target as part of preclinical drug discovery toxicity screening. The PubChem hERG Bioassay data set, composed of 1668 compounds, was used to construct an in silico screening model. The corresponding trial models were constructed from a descriptor pool composed of 4D fingerprints (4D-FP) and traditional 2D and 3D VolSurf-like molecular descriptors.

In silico binary classification QSAR models based on 4D-fingerprints and MOE descriptors for prediction of hERG blockage.

Blockage of the human ether-a-go-go related gene (hERG) potassium ion channel is a major factor related to cardiotoxicity. Hence, drugs binding to this channel have become an important biological end point in side effects screening. A set of 250 structurally diverse compounds screened for hERG activity from the literature was assembled using a set of reliability filters.

Membrane-interaction quantitative structure--activity relationship (MI-QSAR) analyses of skin penetration enhancers.

Membrane-interaction quantitative structure-activity relationship (MI-QSAR) models for two skin penetration enhancer data sets of 61 and 42 compounds were constructed and compared to QSAR models constructed for the same two data sets using only classic intramolecular QSAR descriptors. These two data sets involve skin penetration enhancement of hydrocortisone and hydrocortisone acetate, and the enhancers are generally similar in structure to lipids and surfactants. A new MI-QSAR descriptor, the difference in the integrated cylindrical distribution functions over the phospholipid monolayer model, in and out of the presence of the skin penetration enhancer, DeltaSigma h(r), was developed.

Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors.

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules.

Methods for applying the quantitative structure-activity relationship paradigm.

There are several Quantitative Structure-Activity Relationship (QSAR) methods to assist in the design of compounds for medicinal use. Owing to the different QSAR methodologies, deciding which QSAR method to use depends on the composition of system of interest and the desired results. The relationship between a compound's binding affinity/activity to its structural properties was first noted in the 1930s by Hammett and later refined by Hansch and Fujita in the mid-1960s.