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1.
Molecules ; 26(5)2021 Mar 07.
Artigo em Inglês | MEDLINE | ID: mdl-33800013

RESUMO

With the emergence and global spread of the COVID-19 pandemic, the scientific community worldwide has focused on search for new therapeutic strategies against this disease. One such critical approach is targeting proteins such as helicases that regulate most of the SARS-CoV-2 RNA metabolism. The purpose of the current study was to predict a library of phytochemicals derived from diverse plant families with high binding affinity to SARS-CoV-2 helicase (Nsp13) enzyme. High throughput virtual screening of the Medicinal Plant Database for Drug Design (MPD3) database was performed on SARS-CoV-2 helicase using AutoDock Vina. Nilotinib, with a docking value of -9.6 kcal/mol, was chosen as a reference molecule. A compound (PubChem CID: 110143421, ZINC database ID: ZINC257223845, eMolecules: 43290531) was screened as the best binder (binding energy of -10.2 kcal/mol on average) to the enzyme by using repeated docking runs in the screening process. On inspection, the compound was disclosed to show different binding sites of the triangular pockets collectively formed by Rec1A, Rec2A, and 1B domains and a stalk domain at the base. The molecule is often bound to the ATP binding site (referred to as binding site 2) of the helicase enzyme. The compound was further discovered to fulfill drug-likeness and lead-likeness criteria, have good physicochemical and pharmacokinetics properties, and to be non-toxic. Molecular dynamic simulation analysis of the control/lead compound complexes demonstrated the formation of stable complexes with good intermolecular binding affinity. Lastly, affirmation of the docking simulation studies was accomplished by estimating the binding free energy by MMPB/GBSA technique. Taken together, these findings present further in silco investigation of plant-derived lead compounds to effectively address COVID-19.


Assuntos
Metiltransferases/antagonistas & inibidores , Metiltransferases/metabolismo , RNA Helicases/antagonistas & inibidores , RNA Helicases/metabolismo , Proteínas não Estruturais Virais/antagonistas & inibidores , Proteínas não Estruturais Virais/metabolismo , Antivirais/química , Antivirais/metabolismo , Antivirais/farmacocinética , Antivirais/toxicidade , Sítios de Ligação , Disponibilidade Biológica , Biologia Computacional/métodos , Bases de Dados de Compostos Químicos , Desenho de Fármacos , Humanos , Ligação de Hidrogênio , Metiltransferases/química , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Compostos Fitoquímicos/química , Compostos Fitoquímicos/metabolismo , Plantas Medicinais/química , Ligação Proteica , Domínios Proteicos/efeitos dos fármacos , Pirimidinas/química , Pirimidinas/metabolismo , Pirimidinas/farmacocinética , Pirimidinas/toxicidade , RNA Helicases/química , Relação Estrutura-Atividade , Termodinâmica , Proteínas não Estruturais Virais/química
2.
Molecules ; 26(5)2021 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-33807773

RESUMO

In late 2019, a global pandemic occurred. The causative agent was identified as a member of the Coronaviridae family, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we present an analysis on the substances identified in the human metabolome capable of binding the active site of the SARS-CoV-2 main protease (Mpro). The substances present in the human metabolome have both endogenous and exogenous origins. The aim of this research was to find molecules whose biochemical and toxicological profile was known that could be the starting point for the development of antiviral therapies. Our analysis revealed numerous metabolites-including xenobiotics-that bind this protease, which are essential to the lifecycle of the virus. Among these substances, silybin, a flavolignan compound and the main active component of silymarin, is particularly noteworthy. Silymarin is a standardized extract of milk thistle, Silybum marianum, and has been shown to exhibit antioxidant, hepatoprotective, antineoplastic, and antiviral activities. Our results-obtained in silico and in vitro-prove that silybin and silymarin, respectively, are able to inhibit Mpro, representing a possible food-derived natural compound that is useful as a therapeutic strategy against COVID-19.


Assuntos
Antivirais/farmacologia , Metaboloma , Inibidores de Proteases/farmacologia , Silimarina/farmacologia , Antivirais/química , Antivirais/metabolismo , Sítios de Ligação , Domínio Catalítico/efeitos dos fármacos , Simulação por Computador , /química , Bases de Dados de Compostos Químicos , Descoberta de Drogas , Ensaios Enzimáticos , Humanos , Ligantes , Simulação de Acoplamento Molecular , Inibidores de Proteases/química , Inibidores de Proteases/metabolismo , Silimarina/química , Silimarina/metabolismo , Software
3.
Int J Mol Sci ; 22(6)2021 Mar 11.
Artigo em Inglês | MEDLINE | ID: mdl-33799614

RESUMO

The estrogen receptors α (ERα) are transcription factors involved in several physiological processes belonging to the nuclear receptors (NRs) protein family. Besides the endogenous ligands, several other chemicals are able to bind to those receptors. Among them are endocrine disrupting chemicals (EDCs) that can trigger toxicological pathways. Many studies have focused on predicting EDCs based on their ability to bind NRs; mainly, estrogen receptors (ER), thyroid hormones receptors (TR), androgen receptors (AR), glucocorticoid receptors (GR), and peroxisome proliferator-activated receptors gamma (PPARγ). In this work, we suggest a pipeline designed for the prediction of ERα binding activity. The flagged compounds can be further explored using experimental techniques to assess their potential to be EDCs. The pipeline is a combination of structure based (docking and pharmacophore models) and ligand based (pharmacophore models) methods. The models have been constructed using the Environmental Protection Agency (EPA) data encompassing a large number of structurally diverse compounds. A validation step was then achieved using two external databases: the NR-DBIND (Nuclear Receptors DataBase Including Negative Data) and the EADB (Estrogenic Activity DataBase). Different combination protocols were explored. Results showed that the combination of models performed better than each model taken individually. The consensus protocol that reached values of 0.81 and 0.54 for sensitivity and specificity, respectively, was the best suited for our toxicological study. Insights and recommendations were drawn to alleviate the screening quality of other projects focusing on ERα binding predictions.


Assuntos
Disruptores Endócrinos/química , Receptor alfa de Estrogênio/química , Simulação de Acoplamento Molecular , Sítios de Ligação , Bases de Dados de Compostos Químicos , Conjuntos de Dados como Assunto , Disruptores Endócrinos/classificação , Disruptores Endócrinos/metabolismo , Receptor alfa de Estrogênio/metabolismo , Humanos , Ligantes , Ligação Proteica , Projetos de Pesquisa , Sensibilidade e Especificidade , Relação Estrutura-Atividade , Estados Unidos , United States Environmental Protection Agency
4.
Bioorg Med Chem ; 38: 116119, 2021 05 15.
Artigo em Inglês | MEDLINE | ID: mdl-33831697

RESUMO

In response to the pandemic caused by SARS-CoV-2, we constructed a hybrid support vector machine (SVM) classification model using a set of publicly posted SARS-CoV-2 pseudotyped particle (PP) entry assay repurposing screen data to identify novel potent compounds as a starting point for drug development to treat COVID-19 patients. Two different molecular descriptor systems, atom typing descriptors and 3D fingerprints (FPs), were employed to construct the SVM classification models. Both models achieved reasonable performance, with the area under the curve of receiver operating characteristic (AUC-ROC) of 0.84 and 0.82, respectively. The consensus prediction outperformed the two individual models with significantly improved AUC-ROC of 0.91, where the compounds with inconsistent classifications were excluded. The consensus model was then used to screen the 173,898 compounds in the NCATS annotated and diverse chemical libraries. Of the 255 compounds selected for experimental confirmation, 116 compounds exhibited inhibitory activities in the SARS-CoV-2 PP entry assay with IC50 values ranged between 0.17 µM and 62.2 µM, representing an enrichment factor of 3.2. These 116 active compounds with diverse and novel structures could potentially serve as starting points for chemistry optimization for COVID-19 drug discovery.


Assuntos
Antivirais/farmacologia , Máquina de Vetores de Suporte/estatística & dados numéricos , Internalização do Vírus/efeitos dos fármacos , Área Sob a Curva , Bases de Dados de Compostos Químicos/estatística & dados numéricos , Reposicionamento de Medicamentos , Células HEK293 , Humanos , Testes de Sensibilidade Microbiana , Curva ROC , Bibliotecas de Moléculas Pequenas/farmacologia
5.
Int J Mol Sci ; 22(6)2021 Mar 11.
Artigo em Inglês | MEDLINE | ID: mdl-33799613

RESUMO

A novel framework for inverse quantitative structure-activity relationships (inverse QSAR) has recently been proposed and developed using both artificial neural networks and mixed integer linear programming. However, classes of chemical graphs treated by the framework are limited. In order to deal with an arbitrary graph in the framework, we introduce a new model, called a two-layered model, and develop a corresponding method. In this model, each chemical graph is regarded as two parts: the exterior and the interior. The exterior consists of maximal acyclic induced subgraphs with bounded height, the interior is the connected subgraph obtained by ignoring the exterior, and the feature vector consists of the frequency of adjacent atom pairs in the interior and the frequency of chemical acyclic graphs in the exterior. Our method is more flexible than the existing method in the sense that any type of graphs can be inferred. We compared the proposed method with an existing method using several data sets obtained from PubChem database. The new method could infer more general chemical graphs with up to 50 non-hydrogen atoms. The proposed inverse QSAR method can be applied to the inference of more general chemical graphs than before.


Assuntos
Algoritmos , Modelos Químicos , Compostos Orgânicos/química , Relação Quantitativa Estrutura-Atividade , Bases de Dados de Compostos Químicos , Modelos Moleculares , Estrutura Molecular
6.
Int J Mol Sci ; 22(8)2021 Apr 12.
Artigo em Inglês | MEDLINE | ID: mdl-33921228

RESUMO

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes the papain-like protease (PLpro). The protein not only plays an essential role in viral replication but also cleaves ubiquitin and ubiquitin-like interferon-stimulated gene 15 protein (ISG15) from host proteins, making it an important target for developing new antiviral drugs. In this study, we searched for novel, noncovalent potential PLpro inhibitors by employing a multistep in silico screening of a 15 million compound library. The selectivity of the best-scored compounds was evaluated by checking their binding affinity to the human ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), which, as a deubiquitylating enzyme, exhibits structural and functional similarities to the PLpro. As a result, we identified 387 potential, selective PLpro inhibitors, from which we retrieved the 20 best compounds according to their IC50 values toward PLpro estimated by a multiple linear regression model. The selected candidates display potential activity against the protein with IC50 values in the nanomolar range from approximately 159 to 505 nM and mostly adopt a similar binding mode to the known, noncovalent SARS-CoV-2 PLpro inhibitors. We further propose the six most promising compounds for future in vitro evaluation. The results for the top potential PLpro inhibitors are deposited in the database prepared to facilitate research on anti-SARS-CoV-2 drugs.


Assuntos
Antivirais/química , Antivirais/metabolismo , Inibidores de Proteases/química , Inibidores de Proteases/metabolismo , /enzimologia , Animais , Antivirais/toxicidade , Simulação por Computador , Cristalografia por Raios X , Bases de Dados de Compostos Químicos , Bases de Dados de Proteínas , Avaliação Pré-Clínica de Medicamentos , Humanos , Concentração Inibidora 50 , Dose Letal Mediana , Ligantes , Testes de Mutagenicidade , Inibidores de Proteases/toxicidade , Relação Quantitativa Estrutura-Atividade , Ratos , Ubiquitina Tiolesterase/química , Ubiquitina Tiolesterase/metabolismo
7.
Molecules ; 26(4)2021 Feb 20.
Artigo em Inglês | MEDLINE | ID: mdl-33672700

RESUMO

Plants synthesize a large number of natural products, many of which are bioactive and have practical values as well as commercial potential. To explore this vast structural diversity, we present PSC-db, a unique plant metabolite database aimed to categorize the diverse phytochemical space by providing 3D-structural information along with physicochemical and pharmaceutical properties of the most relevant natural products. PSC-db may be utilized, for example, in qualitative estimation of biological activities (Quantitative Structure-Activity Relationship, QSAR) or massive docking campaigns to identify new bioactive compounds, as well as potential binding sites in target proteins. PSC-db has been implemented using the open-source PostgreSQL database platform where all compounds with their complementary and calculated information (classification, redundant names, unique IDs, physicochemical properties, etc.) were hierarchically organized. The source organism for each compound, as well as its biological activities against protein targets, cell lines and different organism were also included. PSC-db is freely available for public use and is hosted at the Universidad de Talca.


Assuntos
Bases de Dados de Compostos Químicos , Compostos Fitoquímicos/química , Plantas/química , Simulação de Acoplamento Molecular , Compostos Fitoquímicos/metabolismo , Plantas/metabolismo , Relação Quantitativa Estrutura-Atividade
8.
Molecules ; 26(4)2021 Feb 19.
Artigo em Inglês | MEDLINE | ID: mdl-33669720

RESUMO

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.


Assuntos
Antivirais/química , Inibidores de Cisteína Proteinase/química , Bases de Dados de Compostos Químicos , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , /enzimologia , Antivirais/uso terapêutico , /antagonistas & inibidores , Inibidores de Cisteína Proteinase/uso terapêutico , Avaliação Pré-Clínica de Medicamentos , Relação Quantitativa Estrutura-Atividade , /química
9.
Methods Mol Biol ; 2266: 3-10, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33759118

RESUMO

This chapter provides a brief overview of the applications of ZINClick virtual library. In the last years, we have investigated the click-chemical space covered by molecules containing the triazole ring and generated a database of 1,2,3-triazoles called ZINClick, starting from literature reported alkynes and azides synthesizable in no more than three synthetic steps from commercially available products. This combinatorial database contains millions of 1,4-disubstituted 1,2,3-triazoles that are easily synthesizable. The library is regularly updated and can be freely downloaded from http://www.ZINClick.org . This virtual library is a good starting point to explore a new portion of chemical space.


Assuntos
Química Click/métodos , Descoberta de Drogas/métodos , Triazóis/química , Alquinos/química , Azidas/química , Química Click/instrumentação , Bases de Dados de Compostos Químicos , Desenho de Fármacos , Bibliotecas de Moléculas Pequenas/química , Software
10.
Methods Mol Biol ; 2266: 155-170, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33759126

RESUMO

Medicinal chemistry society has enough arguments to justify the usage of fragment-based drug design (FBDD) methodologies for the identification of lead compounds. Since the FDA approval of three kinase inhibitors - vemurafenib, venetoclax, and erdafitinib, FBDD has become a challenging alternative to high-throughput screening methods in drug discovery. The following protocol presents in silico drug design of selective histone deacetylase 6 (HDAC6) inhibitors through a fragment-based approach. To date, structural motifs that are important for HDAC inhibitory activity and selectivity are described as: surface recognition group (CAP group), aliphatic or aromatic linker, and zinc-binding group (ZBG). The main idea of this FBDD method is to identify novel and target-selective CAP groups by virtual scanning of publicly available fragment databases. Template structure used to search for novel heterocyclic and carbocyclic fragments is 1,8-naphthalimide (CAP group of scriptaid, a potent HDAC inhibitor). Herein, the design of HDAC6 inhibitors is based on linking the identified fragments with the aliphatic or aromatic linker and hydroxamic acid (ZBG) moiety. Final selection of potential selective HDAC6 inhibitors is based on combined structure-based (molecular docking) and ligand-based (three-dimensional quantitative structure-activity relationships, 3D-QSAR) techniques. Designed compounds are docked in the active site pockets of human HDAC1 and HDAC6 isoforms, and their docking conformations used to predict their HDAC inhibitory and selectivity profiles through two developed 3D-QSAR models (describing HDAC1 and HDAC6 inhibitory activities).


Assuntos
Descoberta de Drogas/métodos , Desacetilase 6 de Histona/química , Inibidores de Histona Desacetilases/química , Simulação de Acoplamento Molecular/métodos , Naftalimidas/química , Motivos de Aminoácidos , Domínio Catalítico , Bases de Dados de Compostos Químicos , Desenho de Fármacos , Histona Desacetilase 1/antagonistas & inibidores , Histona Desacetilase 1/química , Desacetilase 6 de Histona/antagonistas & inibidores , Técnicas In Vitro , Ligantes , Conformação Molecular , Simulação de Dinâmica Molecular , Relação Quantitativa Estrutura-Atividade , Bibliotecas de Moléculas Pequenas , Relação Estrutura-Atividade
11.
Methods Mol Biol ; 2266: 299-312, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33759134

RESUMO

Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network architectures, reducing the development work without sacrificing the performance. When initially generating images of binding sites, users have the option to color the Voronoi cells based on either one of six structural, physicochemical, and evolutionary properties, or a blend of all six individual properties. Encouragingly, after inputting images generated by Bionoi into the convolutional autoencoder, the network was able to effectively learn the most salient features of binding pockets. The accuracy of the generated model is evaluated both visually and numerically through the reconstruction of binding site images from the latent feature space. The generated feature vectors capture well various properties of binding sites and thus can be applied in a multitude of machine learning projects. As a demonstration, we trained the ResNet-18 architecture from Microsoft on Bionoi images to show that it is capable to effectively classify nucleotide- and heme-binding pockets against a large dataset of control pockets binding a variety of small molecules. Bionoi is freely available to the research community at https://github.com/CSBG-LSU/BionoiNet.


Assuntos
Descoberta de Drogas/métodos , Aprendizado de Máquina , Redes Neurais de Computação , Proteínas/química , Software , Sítios de Ligação , Bases de Dados de Compostos Químicos , Aprendizado Profundo , Histidina Quinase/química , Ligantes
12.
J Med Chem ; 64(5): 2762-2776, 2021 03 11.
Artigo em Inglês | MEDLINE | ID: mdl-33606526

RESUMO

OCT1 is the most highly expressed cation transporter in the liver and affects pharmacokinetics and pharmacodynamics. Newly marketed drugs have previously been screened as potential OCT1 substrates and verified by virtual docking. Here, we used machine learning with transport experiment data to predict OCT1 substrates based on classic molecular descriptors, pharmacophore features, and extended-connectivity fingerprints and confirmed them by in vitro uptake experiments. We virtually screened a database of more than 1000 substances. Nineteen predicted substances were chosen for in vitro testing. Sixteen of the 19 newly tested substances (85%) were confirmed as, mostly strong, substrates, including edrophonium, fenpiverinium, ritodrine, and ractopamine. Even without a crystal structure of OCT1, machine learning algorithms predict substrates accurately and may contribute not only to a more focused screening in drug development but also to a better molecular understanding of OCT1 in general.


Assuntos
Fator 1 de Transcrição de Octâmero/metabolismo , Compostos Orgânicos/metabolismo , Bases de Dados de Compostos Químicos , Avaliação Pré-Clínica de Medicamentos , Células HEK293 , Humanos , Aprendizado de Máquina , Compostos Orgânicos/química
13.
Molecules ; 26(3)2021 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-33525411

RESUMO

SARS-CoV-2 caused the current COVID-19 pandemic and there is an urgent need to explore effective therapeutics that can inhibit enzymes that are imperative in virus reproduction. To this end, we computationally investigated the MPD3 phytochemical database along with the pool of reported natural antiviral compounds with potential to be used as anti-SARS-CoV-2. The docking results demonstrated glycyrrhizin followed by azadirachtanin, mycophenolic acid, kushenol-w and 6-azauridine, as potential candidates. Glycyrrhizin depicted very stable binding mode to the active pocket of the Mpro (binding energy, -8.7 kcal/mol), PLpro (binding energy, -7.9 kcal/mol), and Nucleocapsid (binding energy, -7.9 kcal/mol) enzymes. This compound showed binding with several key residues that are critical to natural substrate binding and functionality to all the receptors. To test docking prediction, the compound with each receptor was subjected to molecular dynamics simulation to characterize the molecule stability and decipher its possible mechanism of binding. Each complex concludes that the receptor dynamics are stable (Mpro (mean RMSD, 0.93 Å), PLpro (mean RMSD, 0.96 Å), and Nucleocapsid (mean RMSD, 3.48 Å)). Moreover, binding free energy analyses such as MMGB/PBSA and WaterSwap were run over selected trajectory snapshots to affirm intermolecular affinity in the complexes. Glycyrrhizin was rescored to form strong affinity complexes with the virus enzymes: Mpro (MMGBSA, -24.42 kcal/mol and MMPBSA, -10.80 kcal/mol), PLpro (MMGBSA, -48.69 kcal/mol and MMPBSA, -38.17 kcal/mol) and Nucleocapsid (MMGBSA, -30.05 kcal/mol and MMPBSA, -25.95 kcal/mol), were dominated mainly by vigorous van der Waals energy. Further affirmation was achieved by WaterSwap absolute binding free energy that concluded all the complexes in good equilibrium and stability (Mpro (mean, -22.44 kcal/mol), PLpro (mean, -25.46 kcal/mol), and Nucleocapsid (mean, -23.30 kcal/mol)). These promising findings substantially advance our understanding of how natural compounds could be shaped to counter SARS-CoV-2 infection.


Assuntos
Antivirais/química , Bases de Dados de Compostos Químicos , Sistemas de Liberação de Medicamentos , Desenho de Fármacos , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Compostos Fitoquímicos/química , Proteínas Virais/química , Antivirais/uso terapêutico , /epidemiologia , Humanos , Pandemias , Compostos Fitoquímicos/uso terapêutico , Proteínas Virais/antagonistas & inibidores
14.
J Med Syst ; 45(4): 45, 2021 Feb 23.
Artigo em Inglês | MEDLINE | ID: mdl-33624190

RESUMO

We present a protocol for integrating two types of biological data - clinical and molecular - for more effective classification of patients with cancer. The proposed approach is a hybrid between early and late data integration strategy. In this hybrid protocol, the set of informative clinical features is extended by the classification results based on molecular data sets. The results are then treated as new synthetic variables. The hybrid protocol was applied to METABRIC breast cancer samples and TCGA urothelial bladder carcinoma samples. Various data types were used for clinical endpoint prediction: clinical data, gene expression, somatic copy number aberrations, RNA-Seq, methylation, and reverse phase protein array. The performance of the hybrid data integration was evaluated with a repeated cross validation procedure and compared with other methods of data integration: early integration and late integration via super learning. The hybrid method gave similar results to those obtained by the best of the tested variants of super learning. What is more, the hybrid method allowed for further sensitivity analysis and recursive feature elimination, which led to compact predictive models for cancer clinical endpoints. For breast cancer, the final model consists of eight clinical variables and two synthetic features obtained from molecular data. For urothelial bladder carcinoma, only two clinical features and one synthetic variable were necessary to build the best predictive model. We have shown that the inclusion of the synthetic variables based on the RNA expression levels and copy number alterations can lead to improved quality of prognostic tests. Thus, it should be considered for inclusion in wider medical practice.


Assuntos
Algoritmos , Gerenciamento de Dados/métodos , Conjuntos de Dados como Assunto/classificação , Bases de Dados de Compostos Químicos
15.
J Med Chem ; 64(4): 2010-2023, 2021 02 25.
Artigo em Inglês | MEDLINE | ID: mdl-33543615

RESUMO

Hsp90 is a new promising target for cancer treatment. Many inhibitors have been discovered as therapeutic agents, and some have passed Phase I and II. However, no one is approved by FDA yet. Novel and druggable Hsp90 inhibitors are still demanding. Here, we report a new way to discover high potent Hsp90 inhibitors as antinasopharyngeal carcinoma agents through assembling fragments. With chemotyping analysis, we extract seven chemotypes from 3482 known Hsp90 inhibitors, screen 500 fragments that are compatible with the chemotypes, and confirm 15 anti-Hsp90 fragments. Click chemistry is employed to construct 172 molecules and synthesize 21 compounds among them. The best inhibitor 3d was further optimized and resulted in more potent 4f (IC50 = 0.16 µM). In vitro and in vivo experiments confirmed that 4f is a promising agent against nasopharyngeal carcinoma. This study may provide a strategy in discovering new ligands against targets without well-understood structures.


Assuntos
Antineoplásicos/uso terapêutico , Proteínas de Choque Térmico HSP90/antagonistas & inibidores , Carcinoma Nasofaríngeo/tratamento farmacológico , Neoplasias Nasofaríngeas/tratamento farmacológico , Triazóis/uso terapêutico , Animais , Antineoplásicos/síntese química , Antineoplásicos/metabolismo , Linhagem Celular Tumoral , Bases de Dados de Compostos Químicos , Proteínas de Choque Térmico HSP90/metabolismo , Humanos , Masculino , Camundongos Nus , Simulação de Dinâmica Molecular , Ligação Proteica , Bibliotecas de Moléculas Pequenas/síntese química , Bibliotecas de Moléculas Pequenas/metabolismo , Bibliotecas de Moléculas Pequenas/uso terapêutico , Triazóis/síntese química , Triazóis/metabolismo , Ensaios Antitumorais Modelo de Xenoenxerto
16.
Med Sci Monit ; 27: e927624, 2021 Jan 13.
Artigo em Inglês | MEDLINE | ID: mdl-33436534

RESUMO

BACKGROUND Traditional Chinese medicine has widely used Bolbostemma paniculatum to treat diseases, including cancer, but its underlying mechanisms remain unclear. The present study aimed to elucidate the potential pharmacological mechanisms of "Tu Bei Mu" (TBM), the Chinese name for Bolbostemmatis Rhizoma, the dry tuber of B. paniculatum, for the treatment of hepatocellular carcinoma (HCC). MATERIAL AND METHODS The active components and putative therapeutic targets of TBM were explored using SwissTargetPrediction, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and Search Tool for Interactions of Chemicals (STITCH). The HCC-related target database was built using DrugBank, DisGeNet, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). A protein-protein interaction network of the common targets was constructed, based on the matches between TBM potential targets and HCC-related targets, using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the cluster networks were used to elucidate the biological functions of TBM. RESULTS Pharmacological network diagrams of the TBM compound-target network and HCC-related target network were successfully constructed. A total of 22 active components, 191 predicted biological targets of TBM, and 3775 HCC-related targets were identified. Through construction of an HCC-related target database and a protein-protein interaction network of the common targets, TBM was predicted to be effective in treating HCC mainly through the PI3K-Akt, HIF-1, p53, and PPAR signaling pathways. CONCLUSIONS The PI3K/Akt, HIF1, p53, and PPAR pathways may play vital roles in TBM treatment of HCC. Also, the potential anti-cancer effect of TBM on HCC appears to stem from the synergetic effect of multiple targets and mechanisms.


Assuntos
Antineoplásicos/farmacologia , Carcinoma Hepatocelular/tratamento farmacológico , Medicamentos de Ervas Chinesas/farmacologia , Neoplasias Hepáticas/tratamento farmacológico , Biologia de Sistemas/métodos , Antineoplásicos/química , Antineoplásicos/uso terapêutico , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/metabolismo , Bases de Dados de Compostos Químicos , Bases de Dados Genéticas , Medicamentos de Ervas Chinesas/química , Medicamentos de Ervas Chinesas/uso terapêutico , Humanos , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/metabolismo , Mapas de Interação de Proteínas/efeitos dos fármacos , Transdução de Sinais/efeitos dos fármacos
17.
Arch Biochem Biophys ; 700: 108771, 2021 03 30.
Artigo em Inglês | MEDLINE | ID: mdl-33485847

RESUMO

In the current study, a structure-based virtual screening paradigm was used to screen a small molecular database against the Non-structural protein 15 (Nsp15) endoribonuclease of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 is the causative agent of the recent outbreak of coronavirus disease 2019 (COVID-19) which left the entire world locked down inside the home. A multi-step molecular docking study was performed against antiviral specific compounds (~8722) collected from the Asinex antiviral database. The less or non-interacting molecules were wiped out sequentially in the molecular docking. Further, MM-GBSA based binding free energy was estimated for 26 compounds which shows a high affinity towards the Nsp15. The drug-likeness and pharmacokinetic parameters of all 26 compounds were explored, and five molecules were found to have an acceptable pharmacokinetic profile. Overall, the Glide-XP docking score and Prime-MM-GBSA binding free energy of the selected molecules were explained strong interaction potentiality towards the Nsp15 endoribonuclease. The dynamic behavior of each molecule with Nsp15 was assessed using conventional molecular dynamics (MD) simulation. The MD simulation information was strongly favors the Nsp15 and each identified ligand stability in dynamic condition. Finally, from the MD simulation trajectories, the binding free energy was estimated using the MM-PBSA method. Hence, the proposed final five molecules might be considered as potential Nsp15 modulators for SARS-CoV-2 inhibition.


Assuntos
Antivirais/farmacologia , /virologia , Endorribonucleases/antagonistas & inibidores , /enzimologia , Proteínas não Estruturais Virais/antagonistas & inibidores , Antivirais/química , Antivirais/farmacocinética , Bases de Dados de Compostos Químicos , Avaliação Pré-Clínica de Medicamentos , Endorribonucleases/química , Inibidores Enzimáticos/química , Inibidores Enzimáticos/farmacocinética , Inibidores Enzimáticos/farmacologia , Humanos , Técnicas In Vitro , Ligantes , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Estrutura Molecular , Interface Usuário-Computador , Proteínas não Estruturais Virais/química
18.
Food Funct ; 12(3): 1176-1191, 2021 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-33432314

RESUMO

Shan Zha has garnered increasing attention in the field of functional foods and medicines due to its widely reported healing effects. However, the potential mechanisms of Shan Zha for human health benefits have not been fully interpreted. Therefore, in the current study, a systems-based method that integrates ADME evaluation, target fishing, gene ontology enrichment analysis, network pharmacology, and pathway analysis is proposed to clarify the underlying pharmacological mechanisms of Shan Zha. As a result, 45 active components of Shan Zha that interacted with 161 protein targets were screened and identified. Moreover, gene ontology enrichment, network and pathway analysis indicated that Shan Zha is beneficial for the treatment of cardiovascular system diseases, digestive system diseases, immune system diseases, inflammatory diseases, cancer, and other diseases through multiple mechanisms. Our study not only proposed an integrated method to comprehensively elucidate the complicated mechanisms of Shan Zha for the treatment of various disorders at the system level, but also provided a reference approach for the mechanistic research of other functional foods.


Assuntos
Crataegus/química , Frutas/química , Alimento Funcional , Compostos Fitoquímicos/química , Compostos Fitoquímicos/farmacologia , Bases de Dados de Compostos Químicos , Humanos , Estrutura Molecular , Fitoterapia , Relação Estrutura-Atividade
19.
Phys Chem Chem Phys ; 23(3): 2430-2437, 2021 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-33459730

RESUMO

Intrinsically disordered proteins (IDPs) are a group of proteins that lack well-defined structures under native conditions and carry out crucial physiological functions in various biochemical pathways. Due to the heterogeneous nature of IDPs, molecular dynamics simulations have been extensively adopted to investigate the conformational ensembles and dynamic properties of these proteins. However, their accuracy remains limited by the development of force fields and sampling algorithms. Here, we evaluated the quality of both force fields and enhanced sampling algorithms based on five short pepX peptides. Our results show that the more extended conformational ensembles sampled by the AMOEBA polarizable force field present a higher ability to reproduce experimental NMR observables than AMBER and CHARMM classical force fields. Moreover, a better agreement with experiments is achieved in the simulation of IaMD (integrated accelerated molecular dynamics) than in aMD (accelerated molecular dynamics). The results together indicate that the combination of AMOEBA force field and IaMD enhanced sampling might be a better choice for simulating IDPs. This work may provide important clues for developments and applications of force fields and enhanced sampling methods in future simulations of IDPs.


Assuntos
Proteínas Intrinsicamente Desordenadas/química , Oligopeptídeos/química , Algoritmos , Bases de Dados de Compostos Químicos , Simulação de Dinâmica Molecular
20.
J Chem Inf Model ; 60(12): 5832-5852, 2020 12 28.
Artigo em Inglês | MEDLINE | ID: mdl-33326239

RESUMO

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.


Assuntos
Antivirais/química , /efeitos dos fármacos , Proteínas não Estruturais Virais/química , Inteligência Artificial , Sítios de Ligação , Simulação por Computador , Bases de Dados de Compostos Químicos , Desenho de Fármacos , Avaliação Pré-Clínica de Medicamentos , Humanos , Simulação de Acoplamento Molecular , Conformação Proteica , Glicoproteína da Espícula de Coronavírus/química , Relação Estrutura-Atividade
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