RESUMO
Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by specific genomic regions known as super-enhancers (SEs). The robust transcription of genes at such SEs is enabled by the formation of phase-separated condensates by transcription factors and coactivators with intrinsically disordered regions. The androgen receptor (AR), the main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the transcriptional coactivator mediator complex subunit 1 (MED1) to SEs in androgen-dependent PCa cells, thereby promoting oncogenic transcriptional programs. In this work, we reveal that full-length AR forms foci with liquid-like properties in different PCa models. We demonstrate that foci formation correlates with AR transcriptional activity, as this activity can be modulated by changing cellular foci content chemically or by silencing MED1. AR ability to phase separate was also validated in vitro by using recombinant full-length AR protein. We also demonstrate that AR antagonists, which suppress transcriptional activity by targeting key regions for homotypic or heterotypic interactions of this receptor, hinder foci formation in PCa cells and phase separation in vitro. Our results suggest that enhanced compartmentalization of AR and coactivators may play an important role in the activation of oncogenic transcription programs in androgen-dependent PCa.
Assuntos
Neoplasias da Próstata , Receptores Androgênicos , Masculino , Humanos , Receptores Androgênicos/genética , Receptores Androgênicos/metabolismo , Androgênios , Fatores de Transcrição/metabolismo , Regulação da Expressão Gênica , Neoplasias da Próstata/genética , Neoplasias da Próstata/metabolismo , Expressão Gênica , Linhagem Celular Tumoral , Regulação Neoplásica da Expressão GênicaRESUMO
Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.
Assuntos
Aprendizado Profundo , Desenho de Fármacos , Modelos Moleculares , Proteólise , Quimera de Direcionamento de Proteólise , Ubiquitina-Proteína Ligases/metabolismo , Ubiquitina-Proteína Ligases/químicaRESUMO
SUMMARY: Deep learning (DL) can significantly accelerate virtual screening of ultra-large chemical libraries, enabling the evaluation of billions of compounds at a fraction of the computational cost and time required by conventional docking. Here, we introduce DD-GUI, the graphical user interface for such DL approach we have previously developed, termed Deep Docking (DD). The DD-GUI allows for quick setups of large-scale virtual screens in an intuitive way, and provides convenient tools to track the progress and analyze the outcomes of a drug discovery project. AVAILABILITY AND IMPLEMENTATION: DD-GUI is freely available with an MIT license on GitHub at https://github.com/jamesgleave/DeepDockingGUI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Aprendizado Profundo , Software , Bibliotecas de Moléculas Pequenas/farmacologia , Descoberta de DrogasRESUMO
The rapid global spread of the SARS-CoV-2 virus facilitated the development of novel direct-acting antiviral agents (DAAs). The papain-like protease (PLpro) has been proposed as one of the major SARS-CoV-2 targets for DAAs due to its dual role in processing viral proteins and facilitating the host's immune suppression. This dual role makes identifying small molecules that can effectively neutralize SARS-CoV-2 PLpro activity a high-priority task. However, PLpro drug discovery faces a significant challenge due to the high mobility and induced-fit effects in the protease's active site. Herein, we virtually screened the ZINC20 database with Deep Docking (DD) to identify prospective noncovalent PLpro binders and combined ultra-large consensus docking with two pharmacophore (ph4)-filtering strategies. The analysis of active compounds revealed their somewhat-limited diversity, likely attributed to the induced-fit nature of PLpro's active site in the crystal structures, and therefore, the use of rigid docking protocols poses inherited limitations. The top hits were assessed against recombinant viral proteins and live viruses, demonstrating desirable inhibitory activities. The best compound VPC-300195 (IC50: 15 µM) ranks among the top noncovalent PLpro inhibitors discovered through in silico methodologies. In the search for novel SARS-CoV-2 PLpro-specific chemotypes, the identified inhibitors could serve as diverse templates for the development of effective noncovalent PLpro inhibitors.
Assuntos
COVID-19 , Hepatite C Crônica , Humanos , SARS-CoV-2 , Antivirais/farmacologia , Antivirais/química , Modelos Moleculares , Estudos Prospectivos , Inibidores de Proteases/farmacologia , Inibidores de Proteases/química , Proteínas Virais/química , Peptídeo HidrolasesRESUMO
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.
Assuntos
Tratamento Farmacológico da COVID-19 , Simulação por Computador , Desenho de Fármacos , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos , Antivirais/uso terapêutico , COVID-19/virologia , Ensaios Clínicos como Assunto , Humanos , Pandemias , SARS-CoV-2/efeitos dos fármacosRESUMO
The Myc family of transcription factors are involved in the development and progression of numerous cancers, including prostate cancer (PCa). Under the pressure of androgen receptor (AR)-directed therapies resistance can occur, leading to the lethal form of PCa known as neuroendocrine prostate cancer (NEPC), characterized among other features by N-Myc overexpression. There are no clinically approved treatments for NEPC, translating into poor patient prognosis and survival. Therefore, there is a pressing need to develop novel therapeutic avenues to treat NEPC patients. In this study, we investigate the N-Myc-Max DNA binding domain (DBD) as a potential target for small molecule inhibitors and utilize computer-aided drug design (CADD) approaches to discover prospective hits. Through further exploration and optimization, a compound, VPC-70619, was identified with notable anti-N-Myc potency and strong antiproliferative activity against numerous N-Myc expressing cell lines, including those representing NEPC.
Assuntos
Carcinoma Neuroendócrino , Neoplasias da Próstata , Carcinoma Neuroendócrino/metabolismo , Linhagem Celular Tumoral , Regulação Neoplásica da Expressão Gênica , Humanos , Masculino , Estudos Prospectivos , Próstata/metabolismo , Neoplasias da Próstata/tratamento farmacológico , Neoplasias da Próstata/genética , Neoplasias da Próstata/metabolismo , Receptores Androgênicos/genética , Receptores Androgênicos/metabolismoRESUMO
Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)-the hallmark target of SARS-CoV-2 coronavirus.
Assuntos
COVID-19 , SARS-CoV-2 , Humanos , Ligantes , Ligação Proteica , Proteínas/químicaRESUMO
Methicillin-resistant Staphylococcus aureus (MRSA) infections cause significant mortality and morbidity globally. MRSA resistance to ß-lactam antibiotics is mediated by two divergons that control levels of a ß-lactamase, PC1, and a penicillin-binding protein poorly acylated by ß-lactam antibiotics, PBP2a. Expression of genes encoding these proteins is controlled by two integral membrane proteins, BlaR1 and MecR1, which both have an extracellular ß-lactam-binding sensor domain. Here, we solved the X-ray crystallographic structures of the BlaR1 and MecR1 sensor domains in complex with avibactam, a diazabicyclooctane ß-lactamase inhibitor at 1.6-2.0 Å resolution. Additionally, we show that S. aureus SF8300, a clinically relevant strain from the USA300 clone of MRSA, responds to avibactam by up-regulating the expression of the blaZ and pbp2a antibiotic-resistance genes, encoding PC1 and PBP2a, respectively. The BlaR1-avibactam structure of the carbamoyl-enzyme intermediate revealed that avibactam is bound to the active-site serine in two orientations â¼180° to each other. Although a physiological role of the observed alternative pose remains to be validated, our structural results hint at the presence of a secondary sulfate-binding pocket that could be exploited in the design of future inhibitors of BlaR1/MecR1 sensor domains or the structurally similar class D ß-lactamases. The MecR1-avibactam structure adopted a singular avibactam orientation similar to one of the two states observed in the BlaR1-avibactam structure. Given avibactam up-regulates expression of blaZ and pbp2a antibiotic resistance genes, we suggest further consideration and research is needed to explore what effects administering ß-lactam-avibactam combinations have on treating MRSA infections.
Assuntos
Compostos Azabicíclicos/farmacologia , Proteínas de Bactérias/metabolismo , Staphylococcus aureus Resistente à Meticilina/efeitos dos fármacos , Inibidores de beta-Lactamases/farmacologia , Proteínas de Bactérias/química , Cristalografia por Raios X , Resistência Microbiana a Medicamentos/genética , Regulação Bacteriana da Expressão Gênica/efeitos dos fármacos , Genes Bacterianos , Staphylococcus aureus Resistente à Meticilina/genética , Staphylococcus aureus Resistente à Meticilina/metabolismo , Simulação de Acoplamento Molecular , Conformação Proteica , Estabilidade ProteicaRESUMO
MOTIVATION: Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerate the discovery of small molecule regulators of cell development. Combining large genomics and molecular data sources with powerful deep learning techniques has the potential to revolutionize predictive biology. In this study, we present Deep gene COmpound Profiler (DeepCOP), a deep learning based model that can predict gene regulating effects of low-molecular weight compounds. This model can be used for direct identification of a drug candidate causing a desired gene expression response, without utilizing any information on its interactions with protein target(s). RESULTS: In this study, we successfully combined molecular fingerprint descriptors and gene descriptors (derived from gene ontology terms) to train deep neural networks that predict differential gene regulation endpoints collected in LINCS database. We achieved 10-fold cross-validation RAUC scores of and above 0.80, as well as enrichment factors of >5. We validated our models using an external RNA-Seq dataset generated in-house that described the effect of three potent antiandrogens (with different modes of action) on gene expression in LNCaP prostate cancer cell line. The results of this pilot study demonstrate that deep learning models can effectively synergize molecular and genomic descriptors and can be used to screen for novel drug candidates with the desired effect on gene expression. We anticipate that such models can find a broad use in developing novel cancer therapeutics and can facilitate precision oncology efforts. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Aprendizado Profundo , Neoplasias , Ontologia Genética , Humanos , Masculino , Projetos Piloto , Medicina de PrecisãoRESUMO
The current COVID-19 pandemic has elicited extensive repurposing efforts (both small and large scale) to rapidly identify COVID-19 treatments among approved drugs. Herein, we provide a literature review of large-scale SARS-CoV-2 antiviral drug repurposing efforts and highlight a marked lack of consistent potency reporting. This variability indicates the importance of standardizing best practices-including the use of relevant cell lines, viral isolates, and validated screening protocols. We further surveyed available biochemical and virtual screening studies against SARS-CoV-2 targets (Spike, ACE2, RdRp, PLpro, and Mpro) and discuss repurposing candidates exhibiting consistent activity across diverse, triaging assays and predictive models. Moreover, we examine repurposed drugs and their efficacy against COVID-19 and the outcomes of representative repurposed drugs in clinical trials. Finally, we propose a drug repurposing pipeline to encourage the implementation of standard methods to fast-track the discovery of candidates and to ensure reproducible results.
Assuntos
COVID-19 , Reposicionamento de Medicamentos , Antivirais/farmacologia , Consenso , Humanos , Simulação de Acoplamento Molecular , Pandemias , SARS-CoV-2RESUMO
The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.
Assuntos
COVID-19 , Preparações Farmacêuticas , Antivirais , Reposicionamento de Medicamentos , Humanos , Pandemias , SARS-CoV-2RESUMO
Correction for 'QSAR without borders' by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.
RESUMO
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
Assuntos
Química Farmacêutica/métodos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/metabolismo , Preparações Farmacêuticas/química , Algoritmos , Animais , Inteligência Artificial , Bases de Dados Factuais , Desenho de Fármacos , História do Século XX , História do Século XXI , Humanos , Modelos Moleculares , Relação Quantitativa Estrutura-Atividade , Teoria Quântica , Reprodutibilidade dos TestesRESUMO
The inhibition of the androgen receptor (AR) is an established strategy in prostate cancer (PCa) treatment until drug resistance develops either through mutations in the ligand-binding domain (LBD) portion of the receptor or its deletion. We previously identified a druggable pocket on the DNA binding domain (DBD) dimerization surface of the AR and reported several potent inhibitors that effectively disrupted DBD-DBD interactions and consequently demonstrated certain antineoplastic activity. Here we describe further development of small molecule inhibitors of AR DBD dimerization and provide their broad biological characterization. The developed compounds demonstrate improved activity in the mammalian two-hybrid assay, enhanced inhibition of AR-V7 transcriptional activity, and improved microsomal stability. These findings position us for the development of AR inhibitors with entirely novel mechanisms of action that would bypass most forms of PCa treatment resistance, including the truncation of the LBD of the AR.
Assuntos
Antagonistas de Receptores de Andrógenos/farmacologia , DNA de Neoplasias/metabolismo , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Neoplasias da Próstata/tratamento farmacológico , Receptores Androgênicos/química , Bibliotecas de Moléculas Pequenas/farmacologia , Transcrição Gênica , Antagonistas de Receptores de Andrógenos/química , Simulação por Computador , DNA de Neoplasias/antagonistas & inibidores , Ensaios de Triagem em Larga Escala , Humanos , Masculino , Neoplasias da Próstata/metabolismo , Neoplasias da Próstata/patologia , Conformação Proteica , Domínios Proteicos , Receptores Androgênicos/genética , Receptores Androgênicos/metabolismo , Bibliotecas de Moléculas Pequenas/química , Células Tumorais CultivadasRESUMO
The ETS family of proteins consists of 28 transcription factors, many of which have been implicated in development and progression of a variety of cancers. While one family member, ERG, has been rigorously studied in the context of prostate cancer where it plays a critical role, other ETS factors keep emerging as potential hallmark oncodrivers. In recent years, numerous studies have reported initial discoveries of small molecule inhibitors of ETS proteins and opened novel avenues for ETS-directed cancer therapies. This review summarizes the state of the art data on therapeutic targeting of ETS family members and highlights the corresponding drug discovery strategies.
Assuntos
Sistemas de Liberação de Medicamentos , Neoplasias/tratamento farmacológico , Proteínas Proto-Oncogênicas c-ets/metabolismo , Sequência de Aminoácidos , Animais , Humanos , Proteínas Proto-Oncogênicas c-ets/química , Bibliotecas de Moléculas Pequenas/química , Bibliotecas de Moléculas Pequenas/farmacologia , Bibliotecas de Moléculas Pequenas/uso terapêuticoRESUMO
Topoisomerase II (TopoII) is an enzyme essential for cellular metabolism and replication as it regulates DNA topology. Since inhibition of TopoII induces cell death, it is a well-established drug target in cancer therapy; several broadly used anticancer drugs including etoposide and doxorubicin are TopoII inhibitors. However, these therapeutics tend to cause severe side effects and suffer from relatively low ligand affinity, leaving TopoII targeting with small molecules an active area of research. In recent years computer-aided drug discovery (CADD) approaches have been actively used to expand knowledge on the role of TopoII in cancer and to develop novel strategies for its inhibition. Herein, we overview studies that employed structure-based approaches such as docking and molecular dynamic simulations, as well as ligand-based approaches, such as QSAR (quantitative structure-activity relationship) modeling among others, to gain understanding in TopoII targeting with existing drugs and to search for novel drug candidates.
Assuntos
Descoberta de Drogas , Inibidores da Topoisomerase II , Desenho Assistido por Computador , Computadores , DNA Topoisomerases Tipo II , Desenho de Fármacos , Etoposídeo , Inibidores da Topoisomerase II/farmacologiaRESUMO
Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly; thus, predictive models are needed to anticipate resistant mutations and to guide the drug discovery process. In this work, we leverage experimental data collected on 68 AR mutants, either observed in the clinic or described in the literature, to train a deep neural network (DNN) that predicts the response of these mutants to currently used and experimental anti-androgens and testosterone. We demonstrate that the use of this DNN, with general 2D descriptors, provides a more accurate prediction of the biological outcome (inhibition, activation, no-response, mixed-response) in AR mutant-drug pairs compared to other machine learning approaches. Finally, the developed approach was used to make predictions of AR mutant response to the latest AR inhibitor darolutamide, which were then validated by in-vitro experiments.
Assuntos
Aprendizado Profundo , Neoplasias da Próstata/metabolismo , Receptores Androgênicos/metabolismo , Antagonistas de Receptores de Andrógenos/química , Antagonistas de Receptores de Andrógenos/farmacologia , Linhagem Celular Tumoral , Humanos , Masculino , Mutação/genética , Redes Neurais de Computação , Neoplasias da Próstata/tratamento farmacológico , Neoplasias da Próstata/genética , Curva ROC , Receptores Androgênicos/genética , Transcrição Gênica/efeitos dos fármacosRESUMO
Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERß, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERß isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.
Assuntos
Neoplasias da Mama/metabolismo , Receptor alfa de Estrogênio/antagonistas & inibidores , Bibliotecas de Moléculas Pequenas/farmacologia , Sítios de Ligação/efeitos dos fármacos , Neoplasias da Mama/tratamento farmacológico , Simulação por Computador , Desenho Assistido por Computador , Resistência a Medicamentos/efeitos dos fármacos , Receptor alfa de Estrogênio/química , Feminino , Humanos , Ligantes , Bibliotecas de Moléculas Pequenas/uso terapêuticoRESUMO
Resistance to androgen-receptor (AR) directed therapies is, among other factors, associated with Myc transcription factors that are involved in development and progression of many cancers. Overexpression of N-Myc protein in prostate cancer (PCa) leads to its transformation to advanced neuroendocrine prostate cancer (NEPC) that currently has no approved treatments. N-Myc has a short half-life but acts as an NEPC stimulator when it is stabilized by forming a protective complex with Aurora A kinase (AURKA). Therefore, dual-inhibition of N-Myc and AURKA would be an attractive therapeutic avenue for NEPC. Following our computer-aided drug discovery approach, compounds exhibiting potent N-Myc specific inhibition and strong anti-proliferative activity against several N-Myc driven cell lines, were identified. Thereafter, we have developed dual inhibitors of N-Myc and AURKA through structure-based drug design approach by merging our novel N-Myc specific chemical scaffolds with fragments of known AURKA inhibitors. Favorable binding modes of the designed compounds to both N-Myc and AURKA target sites have been predicted by docking. A promising lead compound, 70812, demonstrated low-micromolar potency against both N-Myc and AURKA in vitro assays and effectively suppressed NEPC cell growth.