Data Mining of Protein-Binding Profiling Data Identifies Structural Modifications that Distinguish Selective and Promiscuous Compounds

Activity profiling of compound collections across multiple targets is increasingly being used in probe and drug discovery. Herein, we discuss an approach to systematically analyzing the structure–activity relationships of a large screening profile data with emphasis on identifying structural changes that have a significant impact on the number of proteins to which a compound binds. As a case study, we analyzed a recently released public data set of more than 15 000 compounds screened across 100 sequence-unrelated proteins. The screened compounds have different origins and include natural products, synthetic molecules from academic groups, and commercial compounds. Similar synthetic structures from academic groups showed, overall, greater promiscuity differences than do natural products and commercial compounds. The method implemented in this work readily identified structural changes that differentiated highly specific from promiscuous compounds. This approach is general and can be applied to analyze any other large-scale protein-binding profile data

Activity Landscape Plotter: A Web-Based Application for the Analysis of Structure–Activity Relationships

Activity landscape modeling is a powerful method for the quantitative analysis of structure–activity relationships. This cheminformatics area is in continuous growth, and several quantitative and visual approaches are constantly being developed. However, these approaches often fall into disuse due to their limited access. Herein, we present <i>Activity Landscape Plotter</i> as the first freely available web-based tool to automatically analyze structure–activity relationships of compound data sets. Based on the concept of activity landscape modeling, the online service performs pairwise structure and activity relationships from an input data set supplied by the user. For visual analysis, Activity Landscape Plotter generates Structure–Activity Similarity and Dual-Activity Difference maps. The user can interactively navigate through the maps and export all the pairwise structure–activity information as comma delimited files. Activity Landscape Plotter is freely accessible at https://unam-shiny-difacquim.shinyapps.io/ActLSmaps/

Chemical Multiverse and Diversity of Food Chemicals

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations

Systematic Mining of Generally Recognized as Safe (GRAS) Flavor Chemicals for Bioactive Compounds

Bioactive food compounds can be both therapeutically and nutritionally relevant. Screening strategies are widely employed to identify bioactive compounds from edible plants. Flavor additives contained in the so-called FEMA GRAS (generally recognized as safe) list of approved flavoring ingredients is an additional source of potentially bioactive compounds. This work used the principles of molecular similarity to identify compounds with potential mood-modulating properties. The ability of certain GRAS molecules to inhibit histone deacetylase-1 (HDAC1), proposed as an important player in mood modulation, was assayed. Two GRAS chemicals were identified as HDAC1 inhibitors in the micromolar range, results similar to what was observed for the structurally related mood prescription drug valproic acid. Additional studies on bioavailability, toxicity at higher concentrations, and off-target effects are warranted. The methodology described in this work could be employed to identify potentially bioactive flavor chemicals present in the FEMA GRAS list

Density Functional Theory and Electrochemical Studies: Structure–Efficiency Relationship on Corrosion Inhibition

The relationship between structure and corrosion inhibition of a series of 30 imidazol, benzimidazol, and pyridine derivatives has been established through the investigation of quantum descriptors calculated with PBE/6-311++G**. A quantitative structure–property relationship model was obtained by examination of these descriptors using a genetic functional approximation method based on a multiple linear regression analysis. Our results indicate that the efficiency of corrosion inhibitors is strongly associated with aromaticity, electron donor ability, and molecular volume descriptors. In order to calibrate and validate the proposed model, we performed electrochemical impedance spectroscopy (EIS) studies on imidazole, 2-methylimidazole, benzimidazole, 2-chloromethylbenzimidazole, pyridine, and 2-aminopyridine compounds. The experimental values for efficiency of corrosion inhibition are in good agreement with the estimated values obtained by our model, thus confirming that our approach represents a promising and suitable tool to predict the inhibition of corrosion attributes of nitrogen containing heterocyclic compounds. The adsorption behavior of imidazole or benzimidazole heterocyclic molecules on the Fe(110) surface was also studied to elucidate the inhibition mechanism; the aromaticity played an important role in the adsorbate–surface complex

Combinatorial Libraries As a Tool for the Discovery of Novel, Broad-Spectrum Antibacterial Agents Targeting the ESKAPE Pathogens

Mixture based synthetic combinatorial libraries offer a tremendous enhancement for the rate of drug discovery, allowing the activity of millions of compounds to be assessed through the testing of exponentially fewer samples. In this study, we used a scaffold-ranking library to screen 37 different libraries for antibacterial activity against the ESKAPE pathogens. Each library contained between 10000 and 750000 structural analogues for a total of >6 million compounds. From this, we identified a bis-cyclic guanidine library that displayed strong antibacterial activity. A positional scanning library for these compounds was developed and used to identify the most effective functional groups at each variant position. Individual compounds were synthesized that were broadly active against all ESKAPE organisms at concentrations <2 μM. In addition, these compounds were bactericidal, had antibiofilm effects, showed limited potential for the development of resistance, and displayed almost no toxicity when tested against human lung cells and erythrocytes. Using a murine model of peritonitis, we also demonstrate that these agents are highly efficacious in vivo

Rapid Scanning Structure–Activity Relationships in Combinatorial Data Sets: Identification of Activity Switches

We present a general approach to describe the structure–activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses dual-activity difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries