A combined computational and experimental approach reveals the structure of a C/EBPß-Spi1 interaction required for IL1B gene transcription.

We previously reported that transcription of the human IL1B gene, encoding the proinflammatory cytokine interleukin 1ß, 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/EBPß) at a far-upstream enhancer. We have also reported that the C-terminal tail sequence beyond the C/EBPß leucine zipper is critical for its association with Spi1 via an exposed residue (Arg-232) located within a pocket at one end of the Spi1 DNA-recognition helix. Here, combining in vitro interaction studies with computational docking and molecular dynamics of existing X-ray structures for the Spi1 and C/EBPß DBDs, along with the C/EBPß C-terminal tail sequence, we found that the tail sequence is intimately associated with Arg-232 of Spi1. The Arg-232 pocket was computationally screened for small-molecule binding aimed at IL1B transcription inhibition, yielding l-arginine, a known anti-inflammatory amino acid, revealing a potential for disrupting the C/EBPß-Spi1 interaction. As evaluated by ChIP, cultured lipopolysaccharide (LPS)-activated THP-1 cells incubated with l-arginine had significantly decreased IL1B transcription and reduced C/EBPß's association with Spi1 on the IL1B promoter. No significant change was observed in direct binding of either Spi1 or C/EBPß to cognate DNA and in transcription of the C/EBPß-dependent IL6 gene in the same cells. These results support the notion that disordered sequences extending from a leucine zipper can mediate protein-protein interactions and can serve as druggable targets for regulating gene promoter activity.

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. Previous studies have identified cholic amide m-sulfonic acid, CamSA, as an inhibitor of spore germination. However, CamSA is inactive against the hypervirulent strain R20291. To circumvent this problem, a series of cholic acid amides were synthesized and tested against R20291. The best compound in the series was the simple phenyl amide analogue which possessed an IC50 value of 1.8 µM, more than 225 times as potent as the natural germination inhibitor, chenodeoxycholate. This is the most potent inhibitor of C. difficile spore germination described to date. QSAR and molecular modeling analysis demonstrated that increases in hydrophobicity and decreases in partial charge or polar surface area were correlated with increases in potency.

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. In this work, "LeadOp+R" was developed based on 198 classical chemical reactions to consider the synthetic accessibility while optimizing leads. LeadOp+R first allows user to identify a preserved space defined by the volume occupied by a fragment of the query molecule to be preserved. Then LeadOp+R searches for building blocks with the same preserved space as initial reactants and grows molecules toward the preferred receptor-ligand interactions according to reaction rules from reaction database in LeadOp+R. Multiple conformers of each intermediate product were considered and evaluated at each step. The conformer with the best group efficiency score would be selected as the initial conformer of the next building block until the program finished optimization for all selected receptor-ligand interactions. The LeadOp+R method was tested with two biomolecular systems: Tie-2 kinase and human 5-lipoxygenase. The LeadOp+R methodology was able to optimize the query molecules and systematically developed improved analogs for each of our example systems. The suggested synthetic routes for compounds proposed by LeadOp+R were the same as the published synthetic routes devised by the synthetic/organic chemists.

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. In order to find new starting points for the development of oxime-containing therapeutic reactivators and to increase our base of knowledge, we have employed a combination of computational and experimental procedures to identify additional compounds with the real or potential ability to reactivate AChE while augmenting and complementing current knowledge. Computational methods were used to identify previously uninvestigated oxime-containing molecules. Experimentally, six compounds were found with reactivation capabilities comparable to, or exceeding, those of 2-pralidoxime (2-PAM) against a panel of AChE inactivated by paraoxon, diisopropylfluorophosphate (DFP), fenamiphos, and methamidophos. One compound showed enhanced reactivation ability against DFP and fenamiphos, the least tractable of these OPs to be reactivated.

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. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints.

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 ß-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. Secondly, molecular modeling was used to estimate Log P and aqueous solubility (S o) of the NSAIDs. Molecular dynamics simulations (MDS) were used to investigate the thermodynamics and geometry of drug-CD cavity docking. NSAID solubility increased linearly with increasing CD concentration for the two CD derivatives (displaying an AL profile), whereas increases in drug solubility were low and plateaued in the B-CD solutions (type B profile). The calculated Log P and S o of the NSAIDs were in good concordance with experimental values reported in the literature. Side chain substitutions on the B-CD moiety did not significantly influence complexation. Explicitly, complexation and the associated solubility increase were mainly dependent on the chemical structure of the NSAID. MDS indicated that each NSAID-CD complex had a distinct geometry. Moreover, complexing energy had a large, stabilizing, and fairly constant hydrophobic component for a given CD across the NSAIDs, while electrostatic and solvation interaction complex energies were quite variable but smaller in magnitude.

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. Four different OPs, cyclosarin, sarin, tabun, and VX, were analyzed using the same set of oxime reactivators. A trial descriptor pool of semiempirical, traditional, and molecular interaction field descriptors was used to construct an ensemble of QSAR models for each OP-conjugate pair. Based on the molecular information and the cross-validation ability, individual QSAR models were selected to be part of an OP-conjugate consensus model. The OP-conjugate specific models provide important insight into the physicochemical properties required to reactivate the OP conjugates of interest. The reactivation of AChE inactivated with either cyclosarin or tabun requires the oxime therapeutic to possess an overall polar-positive surface area. Oxime therapeutics for the reactivation of sarin-inactivated AChE are conformationally dependent while oxime reverse therapeutics for VX require a compact region with a highly hydrophilic region and two positively charged pyridine rings.

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.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3-4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pK(a). This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.

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. The cell cytotoxicity data set is imbalanced (a few active compounds and very many inactive compounds), and the ability of the predictive modeling methods is adversely affected in these situations. Commonly imbalanced data sets are overfit with respect to the dominant classified end point; in this study the models routinely overfit toward inactive (noncytotoxic) compounds when the imbalance was substantial. Support vector machine (SVM) models were used to probe the proficiency of different classes of molecular descriptors and oversampling ratios. The SVM models were constructed from 4D-FPs, MOE (1D, 2D, and 21/2D), noNP+MOE, and CATS2D trial descriptors pools and compared to the predictive abilities of CATS2D-based random forest models. Compared to previous results in the literature, the SVM models built from oversampled data sets exhibited better predictive abilities for the training and external test sets.

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., Nano Lett. 2008, 8 (3), 859-865) designed, synthesized, and tested a combinatorial library of 80 surface modified, that is decorated, multi-walled carbon nanotubes for their composite nanotoxicity using six endpoints all based on a common 0 to 100 activity scale. Each of the six endpoints for the 29 most nanotoxic decorated nanotubes were incorporated as the training set for this study. The study reported here includes trial descriptor sets for all possible combinations of MOE, VolSurf, and 4D-fingerprints (FP) descriptor classes, as well as including and excluding explicit spatial contributions from the nanotube. Optimized QSAR models were constructed from these multiple trial descriptor sets. It was found that (a) both the form and quality of the best QSAR models for each of the endpoints are distinct and (b) some endpoints are quite dependent upon 4D-FP descriptors of the entire nanotube-decorator complex. However, other endpoints yielded equally good models only using decorator descriptors with and without the decorator-only 4D-FP descriptors. Lastly, and most importantly, the quality, significance, and interpretation of a QSAR model were found to be critically dependent on the trial descriptor sets used within a given QSAR endpoint study.

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. pyriformis) model using Support Vector Machine (SVM) and Support Vector Regression (SVR), respectively. The models are constructed with three types of molecular descriptors that capture the gross physicochemical features of the compounds: (i) 2D, 2 1/2D, and 3D physical features, (ii) VolSurf-like molecular interaction fields, and (iii) 4D-Fingerprints. The best hERG SVM model achieved 89% accuracy and the three-best SVM models were able to screen a Pubchem data set with an accuracy of 97%. The best T. pyriformis model had an R(2) value of 0.924 for the training set and was able to predict the continuous end points for two test sets with R(2) values of 0.832 and 0.620, respectively. The studies presented within demonstrate the predictive ability (classification and continuous end points) of QSAR models constructed from curated data sets, biologically relevant molecular descriptors, and Support Vector Machines and Support Vector Regression. The ability of these protocols and methodologies to accommodate large data sets (several thousands compounds) that are chemically diverse - and in the case of classification modeling unbalanced (one experimental outcome dominates the data set) - allows scientists to further explore a remarkable amount of biological and chemical information.

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. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.

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. A final binary classification model was constructed via a support vector machine (SVM). The resultant model was then validated using the PubChem hERG Bioassay data set (AID 376) and an external hERG data set by evaluating the model's ability to determine hERG blockers from nonblockers. The external data set (the test set) consisted of 356 compounds collected from available literature data and consisting of 287 actives and 69 inactives. Four different sampling protocols and a 10-fold cross-correlation analysis--used in the validation process to evaluate classification models--explored the impact of the active--inactive data imbalance distribution of the PubChem high-throughput data set. Four different data sets were explored, and the one employing Lipinski's rule-of-five coupled with measures of relative molecular lipophilicity performed the best in the 10-fold cross-correlation validation of the training data set as well as overall prediction accuracy of the external test sets. The linear SVM binary classification model building strategy was applied to different combinations of MOE (traditional 2D, "21/2D", and 3D VolSurf-like) and 4D-FP molecular descriptors to further explore and refine previously proposed key descriptors, identify new significant features that contribute to the prediction of hERG toxicity, and construct the optimal SVM binary classification model from a shrunken descriptor pool. The accuracy, sensitivity, and specificity of the best model determined from 10-fold cross-validation are 95, 90, and 96%, respectively; the overall accuracy is near 87% for the external set. The models constructed in this study demonstrate the following: (i) robustness based upon performance in accuracy across the structural diversity of the training set, (ii) ability to predict a compound's "predisposition" to block hERG ion channels, and (iii) define and illustrate structural features that can be overlaid onto the chemical structures to aid in the 3D structure-activity interpretation of the hERG blocking effect.

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. This data set was used to construct a set of two-state hERG QSAR models. The descriptor pool used to construct the models consisted of 4D-fingerprints generated from the thermodynamic distribution of conformer states available to a molecule, 204 traditional 2D descriptors and 76 3D VolSurf-like descriptors computed using the Molecular Operating Environment (MOE) software. One model is a continuous partial least-squares (PLS) QSAR hERG binding model. Another related model is an optimized binary classification QSAR model that classifies compounds as active or inactive. This binary model achieves 91% accuracy over a large range of molecular diversity spanning the training set. Two external test sets were constructed. One test set is the condensed PubChem bioassay database containing 876 compounds, and the other test set consists of 106 additional compounds found in the literature. Both of the test sets were used to validate the binary QSAR model. The binary QSAR model permits a structural interpretation of possible sources for hERG activity. In particular, the presence of a polar negative group at a distance of 6-8 A from a hydrogen bond donor in a compound is predicted to be a quite structure-specific pharmacophore that increases hERG blockage. Since a data set of high chemical diversity was used to construct the binary model, it is applicable for performing general virtual hERG screening.

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. In 1988 Cramer and coworkers created Comparative Molecular Field Analysis (CoMFA) incorporating the three-dimensional (3D) aspects of the compounds, specifically the electrostatic fields of the compound, into the QSAR model. Hopfinger and coworkers included an additional dimension to 3D-QSAR methodology in 1997 that eliminated the question of "Which conformation to use in a QSAR study?", creating 4D-QSAR. In 1999 Chemical Computing Group Inc. (CCG) developed the Binary-QSAR methodology and added novel 3D-QSAR descriptors to the traditional QSAR model allowing the 3D properties of compounds to be incorporated into the traditional QSAR model. Recently CCG released Probabilistic Receptor Potentials to calculate the substrate's atomic preferences in the active site. These potentials are constructed by fitting analytical functions to experimental properties of the substrates using knowledge-based methods. An overview of these and other QSAR methods will be discussed along with an in-depth examination of the methodologies used to construct QSAR models. Also, included in this chapter is a case study of molecules used to create QSAR models utilizing different methodologies and QSAR programs.