Parallel screening and cheminformatics modeling of flavonoid activated aptasensors.

A parallel screening of 27 different flavonoids and chalcones was conducted using 6 artificial naringenin-activated riboswitches (M1, M2, M3, O, L and H). A quantitative structure-property relationship approach was applied to understand the physicochemical properties of the flavonoid structures resulting in specificity differences relied on the fluorescence intensity of a green fluorescent protein reporter. Robust models of riboswitches M1, M2 and O that had good predictive power were constructed with descriptors selected for their high correlation.

Radiation-Responsive Amino Acid Nanosensor Gel (RANG) for Radiotherapy Monitoring and Trauma Care.

Accurate detection of doses is critical for the development of effective countermeasures and patient stratification strategies in cases of accidental exposure to ionizing radiation. Existing detection devices are limited by high fabrication costs, long processing times, need for sophisticated detection systems, and/or loss of readout signal over time, particularly in complex environments. Here, we describe fundamental studies on amino acid-facilitated templating of gold nanoparticles following exposure to ionizing radiation as a new colorimetric approach for radiation detection.

Accelerated Materials Discovery Using Chemical Informatics Investigation of Polymer Physicochemical Properties and Transgene Expression Efficacy.

Quantitative approaches to structure-property relationships are critical for the accelerated design and discovery of biomaterials in biotechnology and medicine. However, the absence of definitive structures, unlike those available for small molecules or 3D crystal structures available for some proteins, has limited the development of Quantitative Structure-Property Relationship (QSPR) models for investigating physicochemical properties and biological activity of polymers. In this study, we describe a combined experimental and cheminformatics paradigm for first developing QSPR models of polymer physicochemical properties, including molecular weight, hydrophobicity, and DNA-binding activity.

Formation of Pyrylium from Aromatic Systems with a Helium:Oxygen Flowing Atmospheric Pressure Afterglow (FAPA) Plasma Source.

The effects of oxygen addition on a helium-based flowing atmospheric pressure afterglow (FAPA) ionization source are explored. Small amounts of oxygen doped into the helium discharge gas resulted in an increase in abundance of protonated water clusters by at least three times. A corresponding increase in protonated analyte signal was also observed for small polar analytes, such as methanol and acetone.

Investigation of dielectric breakdown in silica-epoxy nanocomposites using designed interfaces.

Adding nano-sized fillers to epoxy has proven to be an effective method for improving dielectric breakdown strength (DBS). Evidence suggests that dispersion state, as well as chemistry at the filler-matrix interface can play a crucial role in property enhancement. Herein we investigate the contribution of both filler dispersion and surface chemistry on the AC dielectric breakdown strength of silica-epoxy nanocomposites.

Development of a Web-Enabled SVR-Based Machine Learning Platform and its Application on Modeling Transgene Expression Activity of Aminoglycoside-Derived Polycations.

Objective: Support Vector Regression (SVR) has become increasingly popular in cheminformatics modeling. As a result, SVR-based machine learning algorithms, including Fuzzy-SVR and Least Square-SVR (LS-SVR) have been developed and applied in various research areas. However, at present, few downloadable packages or public-domain software are available for these algorithms.

Band gap engineering in polymers through chemical doping and applied mechanical strain.

We report simulations based on density functional theory and many-body perturbation theory exploring the band gaps of common crystalline polymers including polyethylene, polypropylene and polystyrene. Our reported band gaps of 8.6 eV for single-chain polyethylene and 9.

Parallel Synthesis and Quantitative Structure-Activity Relationship (QSAR) Modeling of Aminoglycoside-Derived Lipopolymers for Transgene Expression.

We describe the parallel synthesis of lipopolymers generated by conjugating alkanoyl chlorides to polymers derived from aminoglycoside antibiotic monomers as novel vehicles for transgene delivery and expression in mammalian cells. Parallel screening of lipopolymers led to the identification of six leads that demonstrated higher transgene expression efficacies in several cancer cells, when compared to the parental polymers as well as 25 kDa poly(ethylene imine), a current standard for polymer-mediated transgene expression. Quantitiative structure-activity relationship (QSAR)-based cheminformatics modeling was employed in order to investigate the role of lipopolymer physicochemical properties (molecular descriptors) on transgene expression efficacy.

Evaluation of potential genotoxicity of HIV entry inhibitors derived from natural sources.

AIDS is a global pandemic that has seen the development of novel and effective treatments to improve the quality of life of those infected and reduction of spread of the disease. Palmitic Acid (PA), which we identified and isolated from Sargassum fusiforme, is a naturally occurring fatty acid that specifically inhibits HIV entry by binding to a novel pocket on the CD4 receptor. We also identified a structural analogue, 2-bromopalmitate (2-BP), as a more effective HIV entry inhibitor with a 20-fold increase in efficacy.

Discovery of antibiotics-derived polymers for gene delivery using combinatorial synthesis and cheminformatics modeling.

We describe the combinatorial synthesis and cheminformatics modeling of aminoglycoside antibiotics-derived polymers for transgene delivery and expression. Fifty-six polymers were synthesized by polymerizing aminoglycosides with diglycidyl ether cross-linkers. Parallel screening resulted in identification of several lead polymers that resulted in high transgene expression levels in cells.

Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers.

Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle-filled polymers is demonstrated. A key component of this "Materials Genomics" approach is the development and use of Materials Quantitative Structure-Property Relationship (MQSPR) models trained on atomic-level features of nanofiller and polymer constituents and used to predict the polar and dispersive components of their surface energies.

DR-predictor: incorporating flexible docking with specialized electronic reactivity and machine learning techniques to predict CYP-mediated sites of metabolism.

Computational methods that can identify CYP-mediated sites of metabolism (SOMs) of drug-like compounds have become required tools for early stage lead optimization. In recent years, methods that combine CYP binding site features with CYP/ligand binding information have been sought in order to increase the prediction accuracy of such hybrid models over those that use only one representation. Two challenges that any hybrid ligand/structure-based method must overcome are (1) identification of the best binding pose for a specific ligand with a given CYP and (2) appropriately incorporating the results of docking with ligand reactivity.

RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules.

Summary: Regioselectivity-WebPredictor (RS-WebPredictor) is a server that predicts isozyme-specific cytochrome P450 (CYP)-mediated sites of metabolism (SOMs) on drug-like molecules. Predictions may be made for the promiscuous 2C9, 2D6 and 3A4 CYP isozymes, as well as CYPs 1A2, 2A6, 2B6, 2C8, 2C19 and 2E1. RS-WebPredictor is the first freely accessible server that predicts the regioselectivity of the last six isozymes.

Odor-structure relationship studies of tetralin and indan musks.

A list of 147 tetralin- and indan-like compounds was compiled from the literature for investigating the relationship between molecular structure and musk odor. Each compound in the data set was represented by 374 CODESSA and 970 TAE descriptors. A genetic algorithm (GA) for pattern recognition analysis was used to identify a subset of molecular descriptors that could differentiate musks from nonmusks in a plot of the two largest principal components (PCs) of the data.

RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes.

RS-Predictor is a tool for creating pathway-independent, isozyme-specific, site of metabolism (SOM) prediction models using any set of known cytochrome P450 (CYP) substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study, we report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4, the largest publicly accessible collection of P450 ligands and metabolites released to date.

Exploiting domain knowledge for improved quantitative high-throughput screening curve fitting.

Least-squares fitting of the Hill equation to quantitative high-throughput screening (qHTS) assays results in frequent unsatisfactory fits. We learn and exploit prior knowledge to improve the Hill fitting in a nonlinear regression method called domain knowledge fitter (DK-fitter). This paper formulates and solves DK-fitter for 44 public qHTS data sets.

Fast bundle algorithm for multiple-instance learning.

We present a bundle algorithm for multiple-instance classification and ranking. These frameworks yield improved models on many problems possessing special structure. Multiple-instance loss functions are typically nonsmooth and nonconvex, and current algorithms convert these to smooth nonconvex optimization problems that are solved iteratively.

Modeling Choices for Virtual Screening Hit Identification.

Making suitable modeling choices is crucial for successful in silico drug design, and one of the most important of these is the proper extraction and curation of data from qHTS screens, and the use of optimized statistical learning methods to obtain valid models. More specifically, we aim to learn the top-1 % most potent compounds against a variety of targets in a procedure we call virtual screening hit identification (VISHID). To do so, we exploit quantitative high-throughput screens (qHTS) obtained from PubChem, descriptors derived from molecular structures, and support vector machines (SVM) for model generation.

Rank order entropy: why one metric is not enough.

The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the misapplication of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r(2), PRESS r(2), F-tests, etc.) designed to increase user confidence in the validity of QSAR predictions.

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set.

PESDserv: a server for high-throughput comparison of protein binding site surfaces.

Summary: Structure-based approaches complement ligand-based approaches for lead-discovery and cross-reactivity prediction. We present to the scientific community a web server for comparing the surface of a ligand bound site of a protein against a ligand bound site surface database of 106 796 sites. The web server implements the property encoded shape distributions (PESD) algorithm for surface comparison.

SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism.

SMARTCyp is an in silico method that predicts the sites of cytochrome P450-mediated metabolism of druglike molecules. The method is foremost a reactivity model, and as such, it shows a preference for predicting sites that are metabolized by the cytochrome P450 3A4 isoform. SMARTCyp predicts the site of metabolism directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures.

Evaluation of chemically selective displacer analogues for protein purification.

A library of molecular analogues to the selective displacer, N'1'-(4-methylquinolin-2-yl)ethane-1,2-diamine dinitrate, was employed to study the effects of changes in displacer chemistry on their efficacy for selective separations. High throughput screens were carried out using a robotic liquid handling system to examine the ability of these compounds to selectively displace proteins in batch adsorption systems. Experiments were conducted using the model protein pairs ribonuclease A/alpha-chymotrypsinogen A and cytochrome C/lysozyme on a strong cation exchanger.

Binding affinity prediction with property-encoded shape distribution signatures.

We report the use of the molecular signatures known as "property-encoded shape distributions" (PESD) together with standard support vector machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore, a regression-based method that was previously shown to perform better than 14 other scoring functions.

Rapid comparison of protein binding site surfaces with property encoded shape distributions.

Patterns in shape and property distributions on the surface of binding sites are often conserved across functional proteins without significant conservation of the underlying amino-acid residues. To explore similarities of these sites from the viewpoint of a ligand, a sequence and fold-independent method was created to rapidly and accurately compare binding sites of proteins represented by property-mapped triangulated Gauss-Connolly surfaces. Within this paradigm, signatures for each binding site surface are produced by calculating their property-encoded shape distributions (PESD), a measure of the probability that a particular property will be at a specific distance to another on the molecular surface.