1,630 research outputs found
Computer-guided discovery of epigenetics drugs: molecular modeling and identification of inhibitors of DNMT1
Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach
In this work, we analyze the structure–activity relationships (SAR) of epigenetic inhibitors (lysine mimetics) against lysine
methyltransferase (G9a or EHMT2) using a combined activity landscape, molecular docking and molecular dynamics
approach. The study was based on a set of 251 G9a inhibitors with reported experimental activity. The activity landscape
analysis rapidly led to the identifcation of activity clifs, scafolds hops and other active an inactive molecules with distinct
SAR. Structure-based analysis of activity clifs, scafold hops and other selected active and inactive G9a inhibitors by means
of docking followed by molecular dynamics simulations led to the identifcation of interactions with key residues involved
in activity against G9a, for instance with ASP 1083, LEU 1086, ASP 1088, TYR 1154 and PHE 1158. The outcome of this
work is expected to further advance the development of G9a inhibitors
Activity-difference maps and consensus similarity measure characterize structure-activity relationships
Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.
This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.M-L is very
grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB
thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013-
StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of
Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work
was supported by a scholarship from the Secretariat of Public Education and the Mexican
government
Analysis of a large food chemical database: chemical space, diversity, and complexity [version 1; referees: 2 approved, 1 approved with reservations]
Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FoodDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of “Food Informatics”. Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections
Antidiabetic activity of some pentacyclic acid triterpenoids, role of PTPe1B: In vitro, in silico, and in vivo approaches
Synthesis, in vitro and in silico screening of ethyl 2-(6-substituted benzo[d]thiazol-2-ylamino)-2-oxoacetates as protein-tyrosine phosphatase 1B inhibitors.
Latin American databases of natural products: biodiversity and drug discovery against SARS-CoV2
In this study, we evaluated 3444 Latin American natural products using cheminformatic tools. We also characterized 196 compounds for the first time from the flora of El Salvador that were compared with the databases of secondary metabolites from Brazil, Mexico, and Panama, and 42 969 compounds (natural, semi-synthetic, synthetic) from different regions of the world. The overall analysis was performed using drug-likeness properties, molecular fingerprints of different designs, two parameters similarity, molecular scaffolds, and molecular complexity metrics. It was found that, in general, Salvadoran natural products have a large diversity based on fingerprints. Simultaneously, those belonging to Mexico and Panama present the greatest diversity of scaffolds compared to the other databases. This
study provided evidence of the high structural complexity that Latin America's natural products have as
a benchmark. The COVID-19 pandemic has had a negative effect on a global level. Thus, in the search for substances that may influence the coronavirus life cycle, the secondary metabolites from El Salvador and Panama were evaluated by docking against the endoribonuclease NSP-15, an enzyme involved in the SARS CoV-2 viral replication. We propose in this study three natural products as potential inhibitors
of NSP-15
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