8 research outputs found
Platform for Unified Molecular Analysis: PUMA
We introduce a free
platform for chemoinformatic-based diversity
analysis and visualization of chemical space of user supplied data
sets. Platform for Unified Molecular Analysis (PUMA) integrates metrics
used to characterize compound databases including visualization of
chemical space, scaffold content, and analysis of chemical diversity.
The user’s input is a file with SMILES, database names, and
compound IDs. PUMA computes molecular properties of pharmaceutical
relevance, Murcko scaffolds, and diversity analysis. The user can
interactively navigate through the graphs and export image files and
the raw data of the diversity calculations. The platform links two
public online resources: <i>Consensus Diversity Plots</i> for the assessment of global diversity and <i>Activity Landscape
Plotter</i> to analyze structure–activity relationships.
Herein, we describe the functionalities of PUMA and exemplify its
use through the analysis of compound databases of general interest.
PUMA is freely accessible at the authors web-site https://www.difacquim.com/d-tools/
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
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
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