Medicinal Chemistry - Volume 16, Issue 5, 2020

Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, reproduction, homeostasis, and metabolism. According to the alignments of the conserved domains, NRs are classified and assigned the following seven subfamilies or eight subfamilies: (1) NR1: thyroid hormone like (thyroid hormone, retinoic acid, RAR-related orphan receptor, peroxisome proliferator activated, vitamin D3- like), (2) NR2: HNF4-like (hepatocyte nuclear factor 4, retinoic acid X, tailless-like, COUP-TFlike, USP), (3) NR3: estrogen-like (estrogen, estrogen-related, glucocorticoid-like), (4) NR4: nerve growth factor IB-like (NGFI-B-like), (5) NR5: fushi tarazu-F1 like (fushi tarazu-F1 like), (6) NR6: germ cell nuclear factor like (germ cell nuclear factor), and (7) NR0: knirps like (knirps, knirpsrelated, embryonic gonad protein, ODR7, trithorax) and DAX like (DAX, SHP), or dividing NR0 into (7) NR7: knirps like and (8) NR8: DAX like. Different NRs families have different structural features and functions. Since the function of a NR is closely correlated with which subfamily it belongs to, it is highly desirable to identify NRs and their subfamilies rapidly and effectively. The knowledge acquired is essential for a proper understanding of normal and abnormal cellular mechanisms. With the advent of the post-genomics era, huge amounts of sequence-known proteins have increased explosively. Conventional methods for accurately classifying the family of NRs are experimental means with high cost and low efficiency. Therefore, it has created a greater need for bioinformatics tools to effectively recognize NRs and their subfamilies for the purpose of understanding their biological function. In this review, we summarized the application of machine learning methods in the prediction of NRs from different aspects. We hope that this review will provide a reference for further research on the classification of NRs and their families.

Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance—especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)—poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.

Background: Tuberculosis is one of the biggest threats to human health. Recent studies have demonstrated that anti-tubercular peptides are promising candidates for the discovery of new anti-tubercular drugs. Since experimental methods are still labor intensive, it is highly desirable to develop automatic computational methods to identify anti-tubercular peptides from the huge amount of natural and synthetic peptides. Hence, accurate and fast computational methods are highly needed. Methods and Results: In this study, a support vector machine based method was proposed to identify anti-tubercular peptides, in which the peptides were encoded by using the optimal g-gap dipeptide compositions. Comparative results demonstrated that our method outperforms existing methods on the same benchmark dataset. For the convenience of scientific community, a freely accessible web-server was built, which is available at http://lin-group.cn/server/iATP. Conclusion: It is anticipated that the proposed method will become a useful tool for identifying anti-tubercular peptides.

Background: Observational studies support the inflammation hypothesis in coronary heart disease (CHD). As a pleiotropic proinflammatory cytokine, Interleukin-18 (IL-18), has also been found to be associated with the risk of CHD. However, to our knowledge, the method of Mendelian Randomization has not been used to explore the causal effect of IL-18 on CHD. Objective: To assess the causal effect of IL-18 on the risk of CHD. Methods and Results: Genetic variant instruments for IL-18 were obtained from information of the CHS and InCHIANTI cohort, and consisted of the per-allele difference in mean IL-18 for 16 independent variants that reached genome-wide significance. The per-allele difference in log-odds of CHD for each of these variants was estimated from CARDIoGRAMplusC4D, a two-stage meta -analysis. Two-sample Mendelian Randomization (MR) was then performed. Various MR analyses were used, including weighted inverse-variance, MR-Egger regression, robust regression, and penalized regression. The OR of elevated IL-18 associated with CHD was only 0.005 (95%CI -0.105~0.095; P-value=0.927). Similar results were obtained with the use of MR-Egger regression, suggesting that directional pleiotropy was unlikely biasing these results (intercept -0.050, P-value=0.220). Moreover, results from the robust regression and penalized regression analyses also revealed essentially similar findings. Conclusion: Our findings indicate that, by itself, IL-18 is unlikely to represent even a modest causal factor for CHD risk.

Background: Molecular characterization of insulin resistance, a growing health issue worldwide, will help to develop novel strategies and accurate biomarkers for disease diagnosis and treatment. Objective: Integrative analysis of gene expression profiling and gene regulatory network was exploited to identify potential biomarkers early in the development of insulin resistance. Methods: RNA was isolated from livers of animals at three weeks of age, and whole-genome expression profiling was performed and analyzed with Agilent mouse 4x44K microarrays. Differentially expressed genes were subsequently validated by qRT-PCR. Functional characterizations of genes and their interactions were performed by Gene Ontology (GO) analysis and gene regulatory network (GRN) analysis. Results: A total of 197 genes were found to be differentially expressed by fold change ≥2 and P < 0.05 in BKS-db +/+ mice relative to sex and age-matched controls. Functional analysis suggested that these differentially expressed genes were enriched in the regulation of phosphorylation and generation of precursor metabolites which are closely associated with insulin resistance. Then a gene regulatory network associated with insulin resistance (IRGRN) was constructed by integration of these differentially expressed genes and known human protein-protein interaction network. The principal component analysis demonstrated that 67 genes in IRGRN could clearly distinguish insulin resistance from the non-disease state. Some of these candidate genes were further experimentally validated by qRT-PCR, highlighting the predictive role as biomarkers in insulin resistance. Conclusion: Our study provides new insight into the pathogenesis and treatment of insulin resistance and also reveals potential novel molecular targets and diagnostic biomarkers for insulin resistance.

Background: Xanthine oxidase (XO; EC 1.17.3.2) has been considered as a potent drug target for the cure and management of pathological conditions prevailing due to high levels of uric acid in the bloodstream. The role of xanthine oxidase has been well established in the generation of hyperuricemia and gout due to its important role in catalytic oxidative hydroxylation of hypoxanthine to xanthine and further catalyses of xanthine to generate uric acid. In this research, syringic acid, a bioactive phenolic acid was explored to determine the capability of itself and its derivatives to inhibit xanthine oxidase. Objective: The study aimed to develop new xanthine oxidase inhibitors from natural constituents along with the antioxidant potential. Methods: In this report, we designed and synthesized syringic acid derivatives hybridized with alcohol and amines to form ester and amide linkage with the help of molecular docking. The synthesized compounds were evaluated for their antioxidant and xanthine oxidase inhibitory potential. Results: Results of the study revealed that SY3 produces very good xanthine oxidase inhibitory activity. All the compounds showed very good antioxidant activity. The enzyme kinetic studies performed on syringic acid derivatives showed a potential inhibitory effect on XO ability in a competitive manner with IC50 value ranging from 07.18μM-15.60μM and SY3 was revealed as the most active derivative. Molecular simulation revealed that new syringic acid derivatives interacted with the amino acid residues SER1080, PHE798, GLN1194, ARG912, GLN 767, ALA1078 and MET1038 positioned inside the binding site of XO. Results of antioxidant activity revealed that all the derivatives showed very good antioxidant potential. Conclusion: Molecular docking proved to be an effective and selective tool in the design of new syringic acid derivatives. This hybridization of two natural constituents could lead to desirable xanthine oxidase inhibitors with improved activity.

Background: Renal cell carcinoma (RCC) is the most common malignant tumor of the adult kidney. Objective: The aim of this study was to identify key genes signatures during RCC and uncover their potential mechanisms. Methods: Firstly, the gene expression profiles of GSE53757 which contained 144 samples, including 72 kidney cancer samples and 72 controls, were downloaded from the GEO database. And then differentially expressed genes (DEGs) between the kidney cancer samples and the controls were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key genes of DEGs. In addition, the classification model between the kidney cancer samples and the controls was built by Adaboost based on the selected key genes. Results: 213 DEGs including 80 up-regulated and 133 down-regulated genes were selected as the feature genes to build the classification model between the kidney cancer samples and the controls by CFS method. The accuracy of the classification model by using 5-folds cross-validation test and independent set test is 84.4% and 83.3%, respectively. Besides, TYROBP, CD4163, CAV1, CXCL9, CXCL11 and CXCL13 also can be found in the top 20 hub genes screened by proteinprotein interaction (PPI) network. Conclusion: It indicated that CFS is a useful tool to identify key genes in kidney cancer. Besides, we also predicted genes such as TYROBP, CD4163, CAV1, CXCL9, CXCL11 and CXCL13 that might target genes to diagnose the kidney cancer.

Background: Dairy safety has caused widespread concern in society. Unsafe dairy products have threatened people's health and lives. In order to improve the safety of dairy products and effectively prevent the occurrence of dairy insecurity, countries have established different prevention and control measures and safety warnings. Objective: The purpose of this study is to establish a dairy safety prediction model based on machine learning to determine whether the dairy products are qualified. Methods: The 34 common items in the dairy sampling inspection were used as features in this study. Feature selection was performed on the data to obtain a better subset of features, and different algorithms were applied to construct the classification model. Results: The results show that the prediction model constructed by using a subset of features including “total plate”, “water” and “nitrate” is superior. The SN, SP and ACC of the model were 62.50%, 91.67% and 72.22%, respectively. It was found that the accuracy of the model established by the integrated algorithm is higher than that by the non-integrated algorithm. Conclusion: This study provides a new method for assessing dairy safety. It helps to improve the quality of dairy products, ensure the safety of dairy products, and reduce the risk of dairy safety.

Background: Coxsackievirus infections are associated with cases of aseptic meningitis, encephalitis, myocarditis, and some chronic disease. Methods: A series of benzo[d][1,2,3]triazol-1(2)-yl derivatives (here named benzotriazol-1(2)-yl) (4a-i, 5a-h, 6a-e, g, i, j and 7a-f, h-j) were designed, synthesized and in vitro evaluated for cytotoxicity and antiviral activity against two important human enteroviruses (HEVs) members of the Picornaviridae family [Coxsackievirus B 5 (CVB-5) and Poliovirus 1 (Sb-1)]. Results: Compounds 4c (CC50 >100 μM; EC50 = 9 μM), 5g (CC50 >100 μM; EC50 = 8 μM), and 6a (CC50 >100 μM; EC50 = 10 μM) were found active against CVB-5. With the aim of evaluating the selectivity of action of this class of compounds, a wide spectrum of RNA (positive- and negativesense), double-stranded (dsRNA) or DNA viruses were also assayed. For none of them, significant antiviral activity was determined. Conclusion: These results point towards a selective activity against CVB-5, an important human pathogen that causes both acute and chronic diseases in infants, young children, and immunocompromised patients.

Background: The ecto-nucleoside triphosphate diphosphohydrolases (NTPDases) terminate nucleotide signaling via the hydrolysis of extracellular nucleoside-5'-triphosphate and nucleoside- 5'-diphosphate, to nucleoside-5'-monophosphate and composed of eight Ca2+/Mg2+ dependent ectonucleotidases (NTPDase1-8). Extracellular nucleotides are involved in a variety of physiological mechanisms. However, they are rapidly inactivated by ectonucleotidases that are involved in the sequential removal of phosphate group from nucleotides with the release of inorganic phosphate and their respective nucleoside. Ectonucleoside triphosphate diphosphohydrolases (NTPDases) represent the key enzymes responsible for nucleotides hydrolysis and their overexpression has been related to certain pathological conditions. Therefore, the inhibitors of NTPDases are of particular importance in order to investigate their potential to treat various diseases e.g., cancer, ischemia and other disorders of the cardiovascular and immune system. Methods: Keeping in view the importance of NTPDase inhibitors, a series of thiadiazolopyrimidones were evaluated for their potential inhibitory activity towards NTPDases by the malachite green assay. Results: The results suggested that some of the compounds were found as non-selective inhibitors of isozyme of NTPDases, however, most of the compounds act as potent and selective inhibitors. In case of substituted amino derivatives (4c-m), the compounds 4m (IC50 = 1.13 ± 0.09 μM) and 4g (IC50 = 1.72 ± 0.08 μM) were found to be the most potent inhibitors of h-NTPDase1 and 2, respectively. Whereas, compound 4d showed the best inhibitory potential for both h-NTPDase3 (IC50 = 1.25 ± 0.06 μM) and h-NTPDase8 (0.21 ± 0.02 μM). Among 5a-t derivatives, compounds 5e (IC50 = 2.52 ± 0.15 μM), 5p (IC50 = 3.17 ± 0.05 μM), 5n (IC50 = 1.22 ± 0.06 μM) and 5b (IC50 = 0.35 ± 0.001 μM) were found to be the most potent inhibitors of h-NTPDase1, 2, 3 and 8, respectively. Interestingly, the inhibitory concentration values of above-mentioned inhibitors were several folds greater than suramin, a reference control. In order to determine the binding interactions, molecular docking studies of the most potent inhibitors were conducted into the homology models of NTPDases and the putative binding analysis further confirmed that selective and potent compounds bind deep inside the active pocket of the respective enzymes. Conclusion: The docking analysis proposed that the inhibitory activity correlates with the hydrogen bonds inside the binding pocket. Thus, these derivatives are of interest and may further be investigated for their importance in medicinal chemistry.

Background: Bedaquiline is a novel anti-tuberculosis drug that inhibits Mycobacterial ATP synthase. However, studies have found that bedaquiline has serious side effects due to high lipophilicity. Recently, the complete structure of ATP synthase was first reported in the Journal of Science. Objective: The study aimed to design, synthesise and carry out biological evaluation of antituberculosis agents based on the structure of bedaquiline. Methods: The mode of action of bedaquiline and ATP synthase was determined by molecular docking, and a series of low lipophilic bedaquiline derivatives were synthesized. The inhibitory activities of bedaquiline derivatives towards Mycobacterium phlei 1180 and Mycobacterium tuberculosis H37Rv were evaluated in vitro. A docking study was carried out to elucidate the structureactivity relationship of the obtained compounds. The predicted ADMET properties of the synthesized compounds were also analyzed. Results: The compounds 5c3, 6a1, and 6d3 showed good inhibitory activities (MIC=15.62 ug.mL-1). At the same time, the compounds 5c3, 6a1, and 6d3 also showed good drug-like properties through molecular docking and ADMET properties prediction. Conclusion: The results of in vitro anti-tuberculosis activity assays, docking studies and ADMET predictions indicate that the synthesized compounds have potential antifungal activity, with compounds 6a1 being further optimized and developed as lead compounds.