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In this study, we synthesized a series of seven benzimidazole derivatives incorporating the structural acidic framework of angiotensin II (Ang II) type 1 receptor (AT1R) antagonists (ARA-II) employing a three-step reaction sequence. The chemical structures were confirmed by 1H NMR, 13C NMR and mass spectral data. Through biosimulation, compounds 1-7 were identified as computational safe hits, thus, best candidates underwent ex vivo testing against two distinct mechanisms implicated in hypertension: antagonism of the Ang II type 1 receptor and the blockade of calcium channel. Molecular docking studies helped to understand at the molecular level the dual vasorelaxant effects with the recognition sites of the AT1R and the L-type calcium channel. In an in vivo spontaneously hypertensive rat model (SHR), intraperitoneally administration of compound 1 at 20 mg/kg resulted in a 25 % reduction in systolic blood pressure, demonstrating both ex vivo vasorelaxant action and in vivo antihypertensive multitarget efficacy. ©2024 Elsevier.
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Antihipertensivos , Bencimidazoles , Simulación del Acoplamiento Molecular , Ratas Endogámicas SHR , Bencimidazoles/química , Bencimidazoles/farmacología , Bencimidazoles/síntesis química , Animales , Antihipertensivos/farmacología , Antihipertensivos/síntesis química , Antihipertensivos/química , Ratas , Relación Estructura-Actividad , Presión Sanguínea/efectos de los fármacos , Hipertensión/tratamiento farmacológico , Receptor de Angiotensina Tipo 1/metabolismo , Estructura Molecular , Bloqueadores del Receptor Tipo 1 de Angiotensina II/farmacología , Bloqueadores del Receptor Tipo 1 de Angiotensina II/síntesis química , Bloqueadores del Receptor Tipo 1 de Angiotensina II/química , Bloqueadores de los Canales de Calcio/farmacología , Bloqueadores de los Canales de Calcio/síntesis química , Bloqueadores de los Canales de Calcio/química , Canales de Calcio Tipo L/metabolismoRESUMEN
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.
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Productos Biológicos , Bases de Datos Factuales , Productos Biológicos/química , LípidosRESUMEN
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development, and multiple Mpro crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an Mpro consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 Mpro inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential Mpro inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on Mpro enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC50 values in the midmicromolar range. The Mpro consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against Mpro. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.
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COVID-19 , SARS-CoV-2 , Humanos , Bibliotecas de Moléculas Pequeñas/farmacología , Farmacóforo , Consenso , Proteínas no Estructurales Virales/química , Inhibidores de Proteasas/farmacología , Inhibidores de Proteasas/química , Simulación del Acoplamiento Molecular , Antivirales/farmacología , Antivirales/químicaRESUMEN
Sialyl Lewis X (sLex ) antigen is a fucosylated cell-surface glycan that is normally involved in cell-cell interactions. The enhanced expression of sLex on cell surface glycans, which is attributed to the upregulation of fucosyltransferase 6 (FUT6), has been implicated in facilitating metastasis in human colorectal, lung, prostate, and oral cancers. The role that the upregulated FUT6 plays in the progression of tumor to malignancy, with reduced survival rates, makes it a potential target for anticancer drugs. Unfortunately, the lack of experimental structures for FUT6 has hampered the design and development of its inhibitors. In this study, we used in silico techniques to identify potential FUT6 inhibitors. We first modeled the three-dimensional structure of human FUT6 using AlphaFold. Then, we screened the natural compound libraries from the COCONUT database to sort out potential natural products (NPs) with best affinity toward the FUT6 model. As a result of these simulations, we identified three NPs for which we predicted binding affinities and interaction patterns quite similar to those we calculated for two experimentally tested FUT6 inhibitors, that is, fucose mimetic-1 and a GDP-triazole derived compound. We also performed molecular dynamics (MD) simulations for the FUT6 complexes with identified NPs, to investigate their stability. Analysis of the MD simulations showed that the identified NPs establish stable contacts with FUT6 under dynamics conditions. On these grounds, the three screened compounds appear as promising natural alternatives to experimentally tested FUT6 synthetic inhibitors, with expected comparable binding affinity. This envisages good prospects for future experimental validation toward FUT6 inhibition.
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Fucosiltransferasas , Neoplasias , Humanos , Masculino , Descubrimiento de Drogas , Fucosiltransferasas/antagonistas & inhibidores , Fucosiltransferasas/metabolismo , Glicosilación , Antígeno Sialil Lewis X/metabolismoRESUMEN
Visualization of the chemical space is useful in many aspects of chemistry, including compound library design, diversity analysis, and exploring structure-property relationships, to name a few. Examples of notable research areas where the visualization of chemical space has strong applications are drug discovery and natural product research. However, the sheer volume of even comparatively small sub-sections of chemical space implies that we need to use approximations at the time of navigating through chemical space. ChemMaps is a visualization methodology that approximates the distribution of compounds in large datasets based on the selection of satellite compounds that yield a similar mapping of the whole dataset when principal component analysis on a similarity matrix is performed. Here, we show how the recently proposed extended similarity indices can help find regions that are relevant to sample satellites and reduce the amount of high-dimensional data needed to describe a library's chemical space.
RESUMEN
MOTIVATION: Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein-ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited. RESULTS: Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein-ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein-ligand atom-type pair counts that take into account each atom's connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein-ligand affinities (pKd/pKi). The models were evaluated in terms of 'scoring power' on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power. AVAILABILITY AND IMPLEMENTATION: Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Aprendizaje Automático , Proteínas , Algoritmos , Ligandos , Unión Proteica , Proteínas/metabolismoRESUMEN
The quantification of chemical diversity has many applications in drug discovery, organic chemistry, food, and natural product chemistry, to name a few. As the size of the chemical space is expanding rapidly, it is imperative to develop efficient methods to quantify the diversity of large and ultralarge chemical libraries and visualize their mutual relationships in chemical space. Herein, we show an application of our recently introduced extended similarity indices to measure the fingerprint-based diversity of 19 chemical libraries typically used in drug discovery and natural products research with over 18 million compounds. Based on this concept, we introduce the Chemical Library Networks (CLNs) as a general and efficient framework to represent visually the chemical space of large chemical libraries providing a global perspective of the relation between the libraries. For the 19 compound libraries explored in this work, it was found that the (extended) Tanimoto index offers the best description of extended similarity in combination with RDKit fingerprints. CLNs are general and can be explored with any structure representation and similarity coefficient for large chemical libraries.
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Productos Biológicos , Bibliotecas de Moléculas Pequeñas , Productos Biológicos/química , Descubrimiento de Drogas/métodos , Bibliotecas de Moléculas Pequeñas/químicaRESUMEN
The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure-property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure-property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.
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Quimioinformática , Descubrimiento de Drogas , Diseño de Fármacos , Descubrimiento de Drogas/métodosRESUMEN
In May 2022, JCAMD published a Special Issue in honor of Gerald (Gerry) Maggiora, whose scientific leadership over many decades advanced the fields of computational chemistry and chemoinformatics for drug discovery. Along the way, he has impacted many researchers in both academia and the pharmaceutical industry. In this Epilogue, we explain the origins of the Festschrift and present a series of first-hand vignettes, in approximate chronological sequence, that together paint a picture of this remarkable man. Whether they highlight Gerry's endless curiosity about molecular life sciences or his willingness to challenge conventional wisdom or his generous support of junior colleagues and peers, these colleagues and collaborators are united in their appreciation of his positive influence. These tributes also reflect key trends and themes during the evolution of modern drug discovery, seen through the lens of people who worked with a visionary leader. Junior scientists will find an inspiring roadmap for creative collegiality and collaboration.
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Disciplinas de las Ciencias Biológicas , Mentores , Historia del Siglo XX , HumanosRESUMEN
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.
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Tratamiento Farmacológico de COVID-19 , Simulación por Computador , Diseño de Fármacos , Descubrimiento de Drogas/métodos , Reposicionamiento de Medicamentos , Antivirales/uso terapéutico , COVID-19/virología , Ensayos Clínicos como Asunto , Humanos , Pandemias , SARS-CoV-2/efectos de los fármacosRESUMEN
Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.
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Quimioinformática , Aprendizaje Automático , Modelos Logísticos , Algoritmos , Máquina de Vectores de SoporteRESUMEN
Inhibitors of epigenetic writers such as DNA methyltransferases (DNMTs) are attractive compounds for epigenetic drug and probe discovery. To advance epigenetic probes and drug discovery, chemical companies are developing focused libraries for epigenetic targets. Based on a knowledge-based approach, herein we report the identification of two quinazoline-based derivatives identified in focused libraries with sub-micromolar inhibition of DNMT1 (30 and 81 nM), more potent than S-adenosylhomocysteine. Also, both compounds had a low micromolar affinity of DNMT3A and did not inhibit DNMT3B. The enzymatic inhibitory activity of DNMT1 and DNMT3A was rationalized with molecular modeling. The quinazolines reported in this work are known to have low cell toxicity and be potent inhibitors of the epigenetic target G9a. Therefore, the quinazoline-based compounds presented are attractive not only as novel potent inhibitors of DNMTs but also as dual and selective epigenetic agents targeting two families of epigenetic writers.
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Inhibidores Enzimáticos , Quinazolinas , ADN , ADN (Citosina-5-)-Metiltransferasas/metabolismo , Metilación de ADN , Metilasas de Modificación del ADN/química , Inhibidores Enzimáticos/química , Inhibidores Enzimáticos/farmacología , Epigénesis Genética , Quinazolinas/farmacologíaRESUMEN
The identification of protein targets of small molecules is essential for drug discovery. With the increasing amount of chemogenomic data in the public domain, multiple ligand-based models for target prediction have emerged. However, these models are generally biased by the number of known ligands for different targets, which involves an under-representation of epigenetic targets, and despite the increasing importance of epigenetic targets in drug discovery, there are no open tools for epigenetic target prediction. In this work, we introduce Epigenetic Target Profiler (ETP), a freely accessible and easy-to-use web application for the prediction of epigenetic targets of small molecules. For a query compound, ETP predicts its bioactivity profile over a panel of 55 different epigenetic targets. To that aim, ETP uses a consensus model based on two binary classification models for each target, relying on support vector machines and built on molecular fingerprints of different design. A distance-to-model parameter related to the reliability of the predictions is included to facilitate their interpretability and assist in the identification of small molecules with potential epigenetic activity. Epigenetic Target Profiler is freely available at http://www.epigenetictargetprofiler.com.
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Computadores , Proteínas , Epigénesis Genética , Internet , Ligandos , Reproducibilidad de los Resultados , Programas InformáticosRESUMEN
Informatics is growing across disciplines, impacting several areas of chemistry, biology, and biomedical sciences. Besides the well-established bioinformatics discipline, other informatics-based interdisciplinary fields have been evolving over time, such as chemoinformatics and biomedical informatics. Other related research areas such as pharmacoinformatics, food informatics, epi-informatics, materials informatics, and neuroinformatics have emerged more recently and continue to develop as independent subdisciplines. The goals and impacts of each of these disciplines have typically been separately reviewed in the literature. Hence, it remains challenging to identify commonalities and key differences. Herein, we discuss in context three major informatics disciplines in the natural and life sciences including bioinformatics, chemoinformatics, and biomedical informatics and briefly comment on related subdisciplines. We focus the discussion on the definitions, historical background, actual impact, main similarities, and differences and evaluate the dissemination and teaching of bioinformatics, chemoinformatics, and biomedical informatics.
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Informática Médica , Biología ComputacionalRESUMEN
Inhibiting the tubulin-microtubules (Tub-Mts) system is a classic and rational approach for treating different types of cancers. A large amount of data on inhibitors in the clinic supports Tub-Mts as a validated target. However, most of the inhibitors reported thus far have been developed around common chemical scaffolds covering a narrow region of the chemical space with limited innovation. This manuscript aims to discuss the first activity landscape and scaffold content analysis of an assembled and curated cell-based database of 851 Tub-Mts inhibitors with reported activity against five cancer cell lines and the Tub-Mts system. The structure-bioactivity relationships of the Tub-Mts system inhibitors were further explored using constellations plots. This recently developed methodology enables the rapid but quantitative assessment of analog series enriched with active compounds. The constellations plots identified promising analog series with high average biological activity that could be the starting points of new and more potent Tub-Mts inhibitors.
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Quimioinformática , Neoplasias/tratamiento farmacológico , Moduladores de Tubulina/química , Tubulina (Proteína)/química , Línea Celular Tumoral , Humanos , Neoplasias/genética , Tubulina (Proteína)/efectos de los fármacos , Tubulina (Proteína)/genética , Moduladores de Tubulina/farmacologíaRESUMEN
Activity or, more generally, property landscapes (PLs) have been considered as an attractive way to visualize and explore structure-property relationships (SPRs) contained in large data sets of chemical compounds. For graphical analysis, three-dimensional representations reminiscent of natural landscapes are particularly intuitive. So far, the use of such landscape models has essentially been confined to qualitative assessment. We describe recent efforts to analyze PLs in a more quantitative manner, which make it possible to calculate topographical similarity values for comparison of landscape models as a measure of relative SPR information content.
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Relación Estructura-ActividadRESUMEN
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 identification of activity cliffs, scaffolds hops and other active an inactive molecules with distinct SAR. Structure-based analysis of activity cliffs, scaffold hops and other selected active and inactive G9a inhibitors by means of docking followed by molecular dynamics simulations led to the identification 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.
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Inhibidores Enzimáticos/química , Antígenos de Histocompatibilidad/química , N-Metiltransferasa de Histona-Lisina/química , Relación Estructura-Actividad , Antígenos de Histocompatibilidad/ultraestructura , N-Metiltransferasa de Histona-Lisina/antagonistas & inhibidores , N-Metiltransferasa de Histona-Lisina/ultraestructura , Humanos , Lisina/química , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Conformación Proteica/efectos de los fármacos , Quinazolinas/químicaRESUMEN
Small molecule libraries for virtual screening are becoming a well-established tool for the identification of new hit compounds. As for experimental assays, the library quality, defined in terms of structural complexity and diversity, is crucial to increase the chance of a successful outcome in the screening campaign. In this context, Diversity-Oriented Synthesis has proven to be very effective, as the compounds generated are structurally complex and differ not only for the appendages, but also for the molecular scaffold. In this work, we automated the design of a library of lactams by applying a Diversity-Oriented Synthesis strategy called Build/Couple/Pair. We evaluated the novelty and diversity of these compounds by comparing them with lactam moieties contained in approved drugs, natural products, and bioactive compounds from ChEMBL. Finally, depending on their scaffold we classified them into ß-, γ-, δ-, ε-, and isolated, fused, bridged and spirolactam groups and we assessed their drug-like and lead-like properties, thus providing the value of this novel in silico designed library for medicinal chemistry applications.
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Diseño de Fármacos , Lactamas/química , Bibliotecas de Moléculas Pequeñas/química , Productos Biológicos/química , Técnicas Químicas Combinatorias , Lactamas/metabolismo , Bibliotecas de Moléculas Pequeñas/metabolismoRESUMEN
A critical biological event that contributes to the appearance and progress of cancer and diabetes is the reversible phosphorylation of proteins, a process controlled by protein tyrosine-kinases (PTKs) and protein tyrosine-phosphatases (PTPs). Within the PTPs, PTP1B has gained significant interest since it is a validated target in drug discovery. Indeed, several PTP1B inhibitors have been developed, from both, synthesis and natural products. However, none have been approved by the FDA, due to their poor selectivity and/or pharmacokinetic properties. One of the most significant challenges to the discovery of PTP1B inhibitors (in vitro or in silico) is the use of truncated structures (PTP1B1-300), missing valuable information about the mechanisms of inhibition, and selectivity of ligands. The present study describes the biochemical characterization of a full-length PTP1B (hPTP1B1-400), as well as the description of phenalenones 1-4 and ursolic acid (5) as allosteric modulators. Compounds 1-5 showed inhibitory potential on hPTP1B1-400, with IC50 values ranging from 12.7 to 82.1 µM. Kinetic studies showed that 1 and 5 behave as mixed and non-competitive inhibitors, respectively. Circular dichroism experiments confirmed that 1 and 5 induced conformational changes to hPTP1B1-400. Further insights into the structure of hPTP1B1-400 were obtained from a homology model, which pointed out that the C-terminus (residues 301-400) is highly disordered. Molecular docking with the homologated model suggested that compounds 1 and 3-5 bind to the C-terminal domain, likely inducing conformational changes on the protein. Docking positions of compounds 1, 4, and 5 were refined with molecular dynamics simulations. Importantly, these simulations confirmed the high flexibility of the C-terminus of hPTP1B1-400, as well as the changes to its rigidity when bound to 1, 4, and 5.
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Fenalenos/farmacología , Proteína Tirosina Fosfatasa no Receptora Tipo 1/antagonistas & inhibidores , Talaromyces/química , Simulación por Computador , Dimerización , Humanos , Técnicas In Vitro , Cinética , Simulación del Acoplamiento Molecular , Fenalenos/químicaRESUMEN
The stereoselective synthesis and anti- Hymenolepis nana activity of six Linezolid-type compounds, obtained by chemical modification of l-Alanine, are reported in this work. The synthetic strategy was to prepare diasteromeric N,N-dibenzylamino oxazolidinones 1 and 2, and coupling with 4-(4-bromophenyl)morpholine (3) to obtain N,N-dibenzylamino Linezolid analogues 4 and 5. A hydrogenolysis reaction over 4 and 5 resulted in amino-free Linezolid analogues 6 and 7, which were acetylated to reach diasteromeric Linezolid analogues 8 and 9. The six Linezolid analogues 4-9 show in vitro antiparasitic activity against Hymenolepis nana cestode, but not against several bacterial strains. Interestingly, compounds 6, 7 and 9 exhibit high potency, having shorter paralysis and death times after exposure (6-10 and 18-21 min, respectively), shorter than those found with antihelmintic compound Praziquantel (20 and 30 min) at 20 mg/mL. In addition, a cytocompatibility assay of 6-9 with human cells (ARPE-19 cells) demonstrate a non-cytotoxic effect at 0.4 mM. These results show the pharmacological potential of the newly reported Linezolid-type analogues as antiparasitic agents against Hymenolepis nana.