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1.
Drug Discov Today ; 2020 May 18.
Artigo em Inglês | MEDLINE | ID: mdl-32439609

RESUMO

The androgen receptor is a ligand-dependent transcriptional factor and an essential therapeutic target for prostate cancer. Competitive binding of antagonists to the androgen receptor can alleviate aberrant activation of the androgen receptor in prostate cancer. In recent years, computer-aided drug design has played an essential part in the discovery of novel androgen receptor antagonists. This review summarizes the recent advances in the discovery of novel androgen receptor antagonists through computer-aided drug design approaches; and discusses the applications of molecular modeling techniques to understand the resistance mechanisms of androgen receptor antagonists at the molecular level.

2.
J Chem Inf Model ; 2020 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-32352294

RESUMO

Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving novel hit compounds in drug discovery. However, the accuracy of the current docking scoring function (SF) is usually insufficient. In this study, in order to improve the screening power of SF, a novel approach named EAT-Score was proposed by directly utilizing the energy auxiliary terms (EAT) provided by molecular docking scoring through eXtreme Gradient Boosting (XGBoost). Here, EAT specifically refers to the output of the Molecular Operating Environment (MOE) scoring, including the energy scores of five different classical SFs and the Protein-Ligand Interaction Fingerprint (PLIF) terms. The performance of EAT-Score to discriminate actives from decoys was strictly validated on the DUD-E diverse subset by using different performance metrics. The results showed that EAT-Score performed much better than classical SFs in VS, with its AUC values exhibiting an improvement of around 0.3. Meanwhile, EAT-Score could achieve comparable even better prediction performance compared with other state-of-the-art VS methods, such as some machine learning (ML)-based SFs and classical SFs implemented in docking programs, in terms of AUC, LogAUC, or BEDROC. Furthermore, the EAT-Score model can capture important binding pattern information from protein-ligand complexes by Shapley additive explanations (SHAP) analysis, which may be very helpful in interpreting the ligand binding mechanism for a certain target and thereby guiding drug design.

3.
Bioorg Med Chem Lett ; 30(11): 127170, 2020 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-32273218

RESUMO

The Src homology-2 domain containing protein tyrosine phosphatase-2 (SHP2) is a convergent node for oncogenic cell-signaling cascades including the PD-L1/PD-1 pathway. Consequently, SHP2 has emerged as a compelling target for novel anti-cancer agents. Replacing one of phenyl ring in PTP1B inhibitor 1 with heterocyclic ring led to a series of heterocyclic bis-aryl amide derivatives. The representative compound 7b displayed SHP2 inhibitory activity with IC50 of 2.63 ± 0.08 µM, exhibited about 4-fold selectivity for SHP2 over TCPTP and had no detectable activity against SHP1 and PTP1B. These preliminary results could provide a possible opportunity for the development of novel SHP2 inhibitors with optimal potency and improved pharmacological properties.

4.
Br J Pharmacol ; 2020 Apr 28.
Artigo em Inglês | MEDLINE | ID: mdl-32346861

RESUMO

BACKGROUND AND PURPOSE: Febrile seizures (FS), the most common seizures in childhood and often accompanied by later epileptogenesis, are not well controlled. Inflammatory processes have been implicated in the pathophysiology of epilepsy. However, whether the caspase-1 (Casp1) is involved in FS generation and potential to be a target for the treatment of FS is still unclear. EXPERIMENTAL APPROACH: By using pharmacology and gene intervention methods, we tested the role of caspase-1 in FS generation. Then, based on structural virtual screening against the active pocket of caspase-1, we were aimed to find druggable and safe small-molecular antagonists targeting caspase-1 and test their therapeutic potential for FS. KEY RESULTS: Here we show that caspase-1 is essential for FS generation. The level of cleaved caspase-1 increases prior to FS onset. Caspase-1-/- mice were resistant to FS and showed lower neuronal excitability than wild-type littermates. Conversely, overexpression of caspase-1 using in utero electroporation increased neuronal excitability and enhanced susceptibility to FS. Based on structural virtual screening, over one million compounds against the active pocket of caspase-1 were screened with molecular docking approaches. We yielded CZL80, a brain-penetrable small molecule caspase-1, which dramatically reduced neuronal excitability, incidence of FS generation, and relieved later enhanced epileptogenic susceptibility in adult. It was devoid of acute diazepam-like respiratory depression and chronic liver toxicity. CONCLUSION AND IMPLICATIONS: These findings support that caspase-1 is crucial for FS generation, and CZL80 serves as a promising small molecular inhibitor for FS and later enhanced epileptogenic susceptibility.

5.
J Chem Theory Comput ; 2020 May 06.
Artigo em Inglês | MEDLINE | ID: mdl-32324992

RESUMO

A large number of protein-protein interactions (PPIs) are mediated by the interactions between proteins and peptide segments binding partners, and therefore determination of protein-peptide interactions (PpIs) is quite crucial to elucidate important biological processes and design peptides or peptidomimetic drugs that can modulate PPIs. Nowadays, as a powerful computation tool, molecular docking has been widely utilized to predict the binding structures of protein-peptide complexes. However, although a number of docking programs have been available, the systematic study on the assessment of their performance for PpIs has never been reported. In this study, a benchmark data set called PepSet consisting of 185 protein-peptide complexes with peptide length ranging from 5 to 20 residues was employed to evaluate the performance of 14 docking programs, including three protein-protein docking programs (ZDOCK, FRODOCK, and HawkDock), three small molecule docking programs (GOLD, Surflex-Dock, and AutoDock Vina), and eight protein-peptide docking programs (GalaxyPepDock, MDockPeP, HPEPDOCK, CABS-dock, pepATTRACT, DINC, AutoDock CrankPep (ADCP), and HADDOCK peptide docking). A new evaluation parameter, named IL_RMSD, was proposed to measure the docking accuracy with fnat (the fraction of native contacts). In global docking, HPEPDOCK performs the best for the entire data set and yields the success rates of 4.3%, 24.3%, and 55.7% at the top 1, 10, and 100 levels, respectively. In local docking, overall, ADCP achieves the best predictions and reaches the success rates of 11.9%, 37.3%, and 70.3% at the top 1, 10, and 100 levels, respectively. It is expected that our work can provide some helpful insights into the selection and development of improved docking programs for PpIs. The benchmark data set is freely available at http://cadd.zju.edu.cn/pepset/.

6.
Neoplasia ; 22(4): 179-191, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32145688

RESUMO

Cullin-RING E3 ligase (CRL) is the largest family of E3 ubiquitin ligase, responsible for ubiquitylation of ∼20% of cellular proteins. CRL plays an important role in many biological processes, particularly in cancers due to abnormal activation. CRL activation requires neddylation, an enzymatic cascade transferring small ubiquitin-like protein NEDD8 to a conserved lysine residue on cullin proteins. Recent studies have validated that neddylation is an attractive anticancer target. In this study, we report the establishment of an Alpha-Screen-based high throughput screen (HTS) assay for in vitro CUL5 neddylation, and screened a library of 17,000 compounds including FDA approved drugs, natural products and synthetic drug-like small-molecule compounds. Gossypol, a natural compound derived from cotton seed, was identified as an inhibitor of cullin neddylation. Biochemical studies showed that gossypol blocked neddylation of both CUL5 and CUL1 through direct binding to SAG-CUL5 or RBX1-CUL1 complex, and CUL5-H572 plays a key role for gossypol binding. On cellular level, gossypol inhibited cullin neddylation in a variety of cancer cell lines and selectively caused accumulation of NOXA and MCL1, the substrates of CUL5 and CUL1, respectively, in multiple cancer cell lines. Combination of gossypol with specific MCL1 inhibitor synergistically suppress growth of human cancer cells. Our study revealed a previously unknown anti-cancer mechanism of gossypol with potential to develop a new class of neddylation inhibitors.

7.
J Chem Inf Model ; 60(4): 2031-2043, 2020 Apr 27.
Artigo em Inglês | MEDLINE | ID: mdl-32202787

RESUMO

Luciferase-based bioluminescence detection techniques are highly favored in high-throughput screening (HTS), in which the firefly luciferase (FLuc) is the most commonly used variant. However, FLuc inhibitors can interfere with the activity of luciferase, which may result in false positive signals in HTS assays. In order to reduce the unnecessary cost of time and money, an in silico prediction model for FLuc inhibitors is highly desirable. In this study, we built an extensive data set consisting of 20 888 FLuc inhibitors and 198 608 noninhibitors, and then developed a group of classification models based on the combination of three machine learning (ML) algorithms and four types of molecular representations. The best prediction model based on XGBoost and ECFP4 and MOE2d descriptors yielded a balanced accuracy (BA) of 0.878 and an area under the receiver operating characteristic curve (AUC) value of 0.958 for the validation set, and a BA of 0.886 and an AUC of 0.947 for the test set. Three external validation sets, including set 1 (3231 FLuc inhibitors and 69 783 noninhibitors), set 2 (695 FLuc inhibitors and 75 913 noninhibitors), and set 3 (1138 FLuc inhibitors and 8155 noninhibitors), were used to verify the predictive ability of our models. The BA values for the three external validation sets given by the best model are 0.864, 0.845, and 0.791, respectively. In addition, the important features or structural fragments related to FLuc inhibitors were recognized by the Shapley additive explanations (SHAP) method along with their influences on predictions, which may provide valuable clues to detecting undesirable luciferase inhibitors. Based on the important and explanatory features, 16 rules were proposed for detecting FLuc inhibitors, which can achieve a correction rate of 70% for FLuc inhibitors. Furthermore, a comparison with existing prediction rules and models for FLuc inhibitors used in virtual screening verified the high reliability of the models and rules proposed in this study. We also used the model to screen three curated chemical databases, and almost 10% of the molecules in the evaluated databases were predicted as inhibitors, highlighting the potential risk of false positives in luciferase-based assays. Finally, a public web server called ChemFLuc was developed (http://admet.scbdd.com/chemfluc/index/), and it offers a free available service to predict potential FLuc inhibitors.

8.
Eur J Med Chem ; 192: 112156, 2020 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-32114360

RESUMO

Androgen receptor (AR) plays important roles in the development of prostate cancer (PCa), and therefore it has been regarded as the most important therapeutic target for both hormone-sensitive prostate cancer (HSPC) and advanced PCa. In this study, a novel hit (C18) with IC50 of 2.4 µM against AR transcriptional activity in LNCaP cell was identified through structure-based virtual screening based on molecular docking and free energy calculations. The structure-activity relationship analysis and structural optimization of C18 resulted in the discovery of a structural analogue (AT2), a more potent AR antagonist with 16-fold improved anti-AR potency. Further assays indicated that AT2 was capable of effectively inhibiting the transcriptional function of AR and blocking the nuclear translocation of AR like the second-generation AR antagonists. The antagonists discovered in this study may be served as the promising lead compounds for the development of AR-driven PCa therapeutics.

9.
J Chem Inf Model ; 2020 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-32175734

RESUMO

In structure-based drug design (SBDD), the molecular mechanics generalized Born surface area (MM/GBSA) approach has been widely used in ranking the binding affinity of small molecule ligands. However, an accurate estimation of protein-ligand binding affinity still remains a challenge due to the intrinsic limitation of the standard generalized Born (GB) model used in MM/GBSA. In this study, we proposed and evaluated the MM/GBSA approach based on a variable dielectric generalized Born (VDGB) model using residue-type-based dielectric constants. In the VDGB model, different dielectric values were assigned for the three types of protein residues, and the magnitude of the dielectric constants for residue types follows this order: charged ≥ polar ≥ nonpolar. We found that MM/GBSA based on a VDGB model (MM/GBSAVDGB) with an optimal dielectric constant of 4.0 for the charged residues and 1.0 for the noncharged residues together with a net-charge-dependent dielectric value for ligands achieved better predictions as judged by Pearson's correlation coefficient than the standard MM/GBSA with a uniform solute dielectric constant of 4.0 for the training set of 130 protein-ligand complexes. The prediction on the test set with 165 protein-ligand complexes also validated the better performance of MM/GBSAVDGB. Moreover, this method exhibited potential in predicting the relative binding free energies for multiple ligands against the same target. Furthermore, we found that rational truncation of protein residues far from the binding site can significantly speed up the MM/GBSAVDGB calculations, while it almost does not influence the prediction accuracy. Therefore, it is feasible to implement the system-truncated MM/GBSAVDGB as a scoring function for SBDD.

10.
Phys Chem Chem Phys ; 22(10): 5487-5499, 2020 Mar 14.
Artigo em Inglês | MEDLINE | ID: mdl-32101223

RESUMO

Ubiquitin specific protease 7 (USP7) has attracted increasing attention because of its multifaceted roles in different tumor types. The crystal structures of USP7-inhibitor complexes resolved recently provide reliable models for computational structure-based drug design (SBDD) towards USP7. How to accurately estimate USP7-ligand binding affinity is quite critical to guarantee the reliability of SBDD. In this study, we assessed the reliability of multiple computational methods to the binding affinity prediction for a series of USP7 inhibitors with the pyrimidinone scaffold, including molecular docking scoring, MM/PB(GB)SA, and umbrella sampling (US). It was found that the accuracy of the evaluated computational methods for binding affinity prediction follows the order: US-based method > MM/PB(GB)SA > Glide XP scoring. The calculation results demonstrate that incorporating protein flexibility through induced-fit docking or ensemble docking cannot improve the performance of the Glide scoring based on rigid-receptor docking. For the MM/PB(GB)SA methods, the choice of the protein structure and the calculation procedure has a marked impact on the predictions. More importantly, we discovered for the first time that there are significant differences in the dissociation pathways of strong-binding inhibitors and weak-binding inhibitors of USP7, which may be used as a new criterion to judge whether an inhibitor is a strong binder or not. It is expected that our work can provide valuable guidance on the design and discovery of potent USP7 inhibitors.


Assuntos
Química Computacional , Inibidores Enzimáticos/metabolismo , Pirimidinonas/química , Peptidase 7 Específica de Ubiquitina/metabolismo , Desenho de Fármacos , Inibidores Enzimáticos/química , Ligação Proteica
11.
J Med Chem ; 63(9): 4411-4429, 2020 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-31928004

RESUMO

Negative design is a group of virtual screening methods that aims at weeding out compounds with undesired properties during the early stages of drug development. These methods are mainly designed to predict three important types of pharmacological properties: drug-likeness, frequent hitters, and toxicity. In order to achieve high screening efficiency, most negative design methods are physicochemical property-based and/or substructure-based rules or filters. Such methods have advantages of simplicity and good interpretability, but they also suffer from some defects such as inflexibility, discontinuity, and hard decision-making. In this review, the advances in negative design for the evaluations of drug-likeness, frequent hitters, and toxicity are outlined. In addition, the related Web servers and software packages developed recently for negative design are summarized. Finally, future research directions in this field are discussed.

12.
Brief Bioinform ; 2020 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-31982914

RESUMO

How to accurately estimate protein-ligand binding affinity remains a key challenge in computer-aided drug design (CADD). In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well with experimentally measured biological activities. In the past few years, machine learning (ML)-based SFs have gradually emerged as potential alternatives and outperformed classical SFs in a series of studies. In this study, to better recognize the potential of classical SFs, we have conducted a comparative assessment of 25 commonly used SFs. Accordingly, the scoring power was systematically estimated by using the state-of-the-art ML methods that replaced the original multiple linear regression method to refit individual energy terms. The results show that the newly-developed ML-based SFs consistently performed better than classical ones. In particular, gradient boosting decision tree (GBDT) and random forest (RF) achieved the best predictions in most cases. The newly-developed ML-based SFs were also tested on another benchmark modified from PDBbind v2007, and the impacts of structural and sequence similarities were evaluated. The results indicated that the superiority of the ML-based SFs could be fully guaranteed when sufficient similar targets were contained in the training set. Moreover, the effect of the combinations of features from multiple SFs was explored, and the results indicated that combining NNscore2.0 with one to four other classical SFs could yield the best scoring power. However, it was not applicable to derive a generic target-specific SF or SF combination.

13.
Phys Chem Chem Phys ; 22(6): 3149-3159, 2020 Feb 14.
Artigo em Inglês | MEDLINE | ID: mdl-31995074

RESUMO

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.


Assuntos
Simulação de Acoplamento Molecular/métodos , Proteínas/química , Bibliotecas de Moléculas Pequenas/química , Algoritmos , Desenho de Fármacos , Ligantes , Estrutura Molecular , Ligação Proteica , Relação Estrutura-Atividade , Termodinâmica , Fluxo de Trabalho
14.
Drug Discov Today ; 25(4): 657-667, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31987936

RESUMO

One of the major challenges in early drug discovery is the recognition of frequent hitters (FHs), that is, compounds that nonspecifically bind to a range of macromolecular targets or false positives caused by various types of assay interferences. In this review, we survey the mechanisms underlying different types of FHs, including aggregators, spectroscopic interference compounds (i.e., luciferase inhibitors and fluorescent compounds), chemical reactive compounds, and promiscuous compounds. We also review commonly used experimental detection techniques and computational prediction models for FH identification. In addition, the rational applications of these computational filters are discussed. It is believed that, with the rational use of FH filters, the efficiency of drug discovery will be significantly improved.

15.
J Chem Inf Model ; 60(1): 63-76, 2020 Jan 27.
Artigo em Inglês | MEDLINE | ID: mdl-31869226

RESUMO

Lipophilicity, as evaluated by the n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In this study, we developed several quantitative structure-property relationship (QSPR) models to predict log D7.4 based on a large and structurally diverse data set. Eight popular machine learning algorithms were employed to build the prediction models with 43 molecular descriptors selected by a wrapper feature selection method. The results demonstrated that XGBoost yielded better prediction performance than any other single model (RT2 = 0.906 and RMSET = 0.395). Moreover, the consensus model from the top three models could continue to improve the prediction performance (RT2 = 0.922 and RMSET = 0.359). The robustness, reliability, and generalization ability of the models were strictly evaluated by the Y-randomization test and applicability domain analysis. Moreover, the group contribution model based on 110 atom types and the local models for different ionization states were also established and compared to the global models. The results demonstrated that the descriptor-based consensus model is superior to the group contribution method, and the local models have no advantage over the global models. Finally, matched molecular pair (MMP) analysis and descriptor importance analysis were performed to extract transformation rules and give some explanations related to log D7.4. In conclusion, we believe that the consensus model developed in this study can be used as a reliable and promising tool to evaluate log D7.4 in drug discovery.

17.
Brief Bioinform ; 2019 Dec 24.
Artigo em Inglês | MEDLINE | ID: mdl-31885044

RESUMO

BACKGROUND: With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. RESULTS: We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. CONCLUSION: BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.

18.
Phys Chem Chem Phys ; 21(45): 25276-25289, 2019 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-31701109

RESUMO

As a member of the bromodomain and extra terminal domain (BET) protein family, bromodomain-containing protein 4 (BRD4) is an epigenetic reader and can recognize acetylated lysine residues in histones. BRD4 has been regarded as an essential drug target for cancers, inflammatory diseases and acute heart failure, and therefore the discovery of potent BRD4 inhibitors with novel scaffolds is highly desirable. In this study, the crystalline water molecules in BRD4 involved in ligand binding were analyzed first, and the simulation results suggest that several conserved crystalline water molecules are quite essential to keep the stability of the crystalline water network and therefore they need to be reserved in structure-based drug design. Then, a docking-based virtual screening workflow with the consideration of the conserved crystalline water network in the binding pocket was utilized to identify the potential inhibitors of BRD4. The in vitro fluorescence resonance energy transfer (HTRF) binding assay illustrates that 4 hits have good inhibitory activity against BRD4 in the micromolar regime, including three compounds with IC50 values below 5 µM and one below 1 µM (0.37 µM). The structural analysis demonstrates that three active compounds possess novel scaffolds. Moreover, the interaction patterns between the hits and BRD4 were characterized by molecular dynamics simulations and binding free energy calculations, and then several suggestions for the further optimization of these hits were proposed.


Assuntos
Simulação de Acoplamento Molecular , Proteínas Nucleares/química , Fatores de Transcrição/química , Água/química , Proteínas de Ciclo Celular , Cristalização , Transferência Ressonante de Energia de Fluorescência , Humanos , Simulação de Dinâmica Molecular , Proteínas Nucleares/antagonistas & inibidores , Fatores de Transcrição/antagonistas & inibidores
19.
J Chem Inf Model ; 59(11): 4587-4601, 2019 Nov 25.
Artigo em Inglês | MEDLINE | ID: mdl-31644282

RESUMO

Adverse effects induced by drug-drug interactions may result in early termination of drug development or even withdrawal of drugs from the market, and many drug-drug interactions are caused by the inhibition of cytochrome P450 (CYP450) enzymes. Therefore, the accurate prediction of the inhibition capability of a given compound against a specific CYP450 isoform is highly desirable. In this study, three ensemble learning methods, including random forest, gradient boosting decision tree, and eXtreme gradient boosting (XGBoost), and two deep learning methods, including deep neural networks and convolutional neural networks, were used to develop classification models to discriminate inhibitors and noninhibitors for five major CYP450 isoforms (1A2, 2C9, 2C19, 2D6, and 3A4). The results demonstrate that the ensemble learning models generally give better predictions than the deep learning models for the external test sets. Among all of the models, the XGBoost models achieve the best classification capability (average prediction accuracy of 90.4%) for the test sets, which even outperform the previously reported model developed by the multitask deep autoencoder neural network (88.5%). The Shapley additive explanation method was then used to interpret the models and analyze the misclassified molecules. The important molecular descriptors given by our models are consistent with the structural preferences for inhibitors of different CYP450 isoforms, which may provide valuable clues to detect potential drug-drug interactions in the early stage of drug discovery.

20.
Drug Discov Today ; 24(12): 2323-2331, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31494187

RESUMO

DNA methyltransferases (DNMTs) are a conserved family of cytosine methylases with crucial roles in epigenetic regulation. They have been considered as promising therapeutic targets for the epigenetic treatment of cancer. Therefore, DNMT inhibitors (DNMTis) have attracted considerable interest in recent years for the modulation of the aberrant DNA methylation pattern in a reversible way. In this review, we provide a structure-based overview of the therapeutic importance of DNMTs against different cancer types, and then summarize recently investigated DNMTis as well as their inhibitory mechanisms, focusing on recent advances in the development of DNMTis with specificity and/or selectivity using computational approaches.

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