Combinatorial Chemistry & High Throughput Screening - Volume 23, Issue 6, 2020

Objective: The world is under the grasp of dangerous post-antibiotics and antimicrobials attack where common infections may become untreatable, leading to premature deaths due to antimicrobial resistance (AMR). While an estimated 7,00,000 people die annually due to AMR, which is a public health threat to all communities in different parts of the world regardless of their economic status; however, this threat is serious in low- and middle-income countries having lack of sanitation and health infrastructure. The 68th World Health Assembly endorsed the Global Action Plan on antimicrobial resistance. Consequently, many countries started drafting and committing to National Action Plans against AMR. As strong as National Action Plans are in terms of prescribing rational use of antimicrobials, infection control practices, and related public health measures, without strong healthcare systems, these measures will have a limited impact on AMR in developing countries. Methods: The major reason for AMR is microbial quorum sensing (QS) that may strengthen the microbial community to generate inter-communication and virulence effects via quorum sensing mechanisms. Global stewardship to combat antimicrobial resistance aims to develop anti-quorum sensing compounds that can inhibit the biosynthetic pathway mediated different quorum sensing targets. Results: It may pave an effective attempt to minimize microbial quorum sensing mediated antimicrobial resistance. The present review describes QS mediated various potential target enzymes, their connection to AMR, and finds out the corresponding QS biosynthetic target inhibitors. Conclusion: These potential inhibitors can be derivatized to design and develop next-generation antimicrobial agents.

Background: The parasitic protozoal infections leishmaniasis, human African trypanosomiasis, and Chagas disease are neglected tropical diseases that pose serious health risks for much of the world’s population. Current treatment options suffer from limitations, but plantderived natural products may provide economically advantageous therapeutic alternatives. Several germacranolide sesquiterpenoids have shown promising antiparasitic activities, but the mechanisms of activity have not been clearly established. Objective: The objective is to use in silico screening of known antiparasitic germacranolides against recognized protozoal protein targets in order to provide insight into the molecular mechanisms of activity of these natural products. Methods: Conformational analyses of the germacranolides were carried out using density functional theory, followed by molecular docking. A total of 88 Leishmania protein structures, 86 T. brucei protein structures, and 50 T. cruzi protein structures were screened against 27 antiparasitic germacranolides. Results: The in-silico screening has revealed which of the protein targets of Leishmania spp., Trypanosoma brucei, and Trypanosoma cruzi are preferred by the sesquiterpenoid ligands.

Background: Tuberculosis is a disease with high incidence and high mortality rate, especially in Brazil. Although there are several medications available for treatment, in cases of resistance, there is a need to use more than one medication. Objective: Therefore, cases of toxicity increase and reports of resistance have been worrying the population. In addition, some medications have a short period of effectiveness. To achieve the goal, ligand-based and structure-based approaches were used. Methods: Thus, in an attempt to discover potent inhibitors against Mycobacterium tuberculosis enzymes, we sought to identify natural products with high therapeutic potential for the treatment of Tuberculosis through QSAR, Molecular Modeling and ADMET studies. Results: The results showed that the models generated from two sets of molecules with known activity against M. tuberculosis enzymes InhA and PS were able to select 11 and 8 compounds, respectively, between Lignans and Neolignans with 50 to 60% activity probability. In addition, molecular docking contributed to confirm the mechanism of action of compounds and increase the accuracy of methodologies. All molecules showed higher binding energy values for the drug Isoniazid. We conclude that compounds 33, 34, 110, 114 and 133 are promising for InhA target and compounds 07, 08, 19, 21, 42, 48, 75 and 141 for target PS. In addition, most molecules did not show any toxicity according to the evaluated parameters. Conclusion: Therefore, Lignans and Neolignans may be an alternative for the treatment of Tuberculosis.

Background: Escherichia coli various strains can cause alarmingly serious infections. Countries like Pakistan harbour the class of bacteria with one of the highest rates of resistance, but very little has been done to explore their genetic pool. Objectives: This study was designed to find out the frequency of virulence genes of Uropathogenic E. coli and their association with antibiotic resistance along with the evolutionary adaptation of the selected gene through the phylogenetic tree. Methods: Isolates from 120 urinary tract infected patients were collected. Antibiotic sensitivity was detected by the disk diffusion method and DNA extraction was done by the boiling lysis method followed by PCR-based detection of virulence genes. The final results were analysed using the chi-square test. Results: The isolates were found to be least susceptible to nalidixic acid, followed by ampicillin, cotrimoxazole, cefotaxime, ciprofloxacin, aztreonam, amoxicillin, gentamycin, nitrofurantoin and imipenem. The iucC was the most common virulence gene among the resistant isolates. About 86% of the collected samples were found to be multi-drug resistant. Statistical analysis revealed a significant association between the iucC gene and resistance to ampicillin (P=0.03) and amoxicillin (P=0.04), and also between fimH and resistance to aztreonam (P=0.03). Conclusion: This study unravels the uncharted virulence genes of UPEC in our community for the very first time. We report a high frequency of the iucC and fimH virulence genes. This, along with their positive association with resistance to beta-lactam antibiotics in the studied community, indicates their important role in the development of complicated UTIs.

Background: RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part of RNA methylation. It has shown that ribose 2'-O-methylation plays an important role in immune recognition and other pathogenesis. Objective: We aim to design a computational method to identify 2'-O-methylation. Methods: Different from the experimental method, we propose a computational workflow to identify the methylation site based on the multi-feature extracting algorithm. Results: With a voting procedure based on 7 best feature-classifier combinations, we achieved Accuracy of 76.5% in 10-fold cross-validation. Furthermore, we optimized features and input the optimized features into SVM. As a result, the AUC reached to 0.813. Conclusion: The RNA sample, especially the negative samples, used in this study are more objective and strict, so we obtained more representative results than state-of-arts studies.

Background: As the pathogen of malaria, malaria parasite secretes a variety of proteins for its growth and reproduction. Objective: The identification of the secretory proteins of malaria parasite has crucial reference significance for the development of anti-malaria vaccines as well as medicine. Methods: In this study, a computational classification method was developed to identify the secreted proteins of Plasmodium. Amino acid composition, dipeptide composition, and tripeptide composition as well as reduced amino acids alphabets were proposed to illuminate protein sequences. We further used SVM to train and predict respectively and optimized the features. Results: 74 types of reduced amino acids alphabets were employed to predict secretory proteins. The results showed that the accuracy improved to 91.67% with 0.84 Mathew’s correlation coefficient (MCC) by dipeptide composition, and the highest prediction accuracy reached 92.26% after feature selection, which demonstrated that our method is prominent and reliable in the field of malaria parasite secreted proteins prediction. Conclusion: A intuitive web server iSP-RAAC (http://bioinfor.imu.edu.cn/isppseraac) was established for the convenience of most experimental scientists.

Objective: Next-generation sequencing (NGS) was performed to identify genes that were differentially expressed between normal thyroid tissue and papillary thyroid carcinoma (PTC). Materials and Methods: Six candidate genes were selected and further confirmed with quantitative real-time polymerase chain reaction (qRT-PCR), and immunohistochemistry in samples from 24 fresh thyroid tumors and adjacent normal tissues. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to investigate signal transduction pathways of the differentially expressed genes. Results: In total, 1690 genes were differentially expressed between samples from patients with PTC and the adjacent normal tissue. Among these, SFRP4, ZNF90, and DCN were the top three upregulated genes, whereas KIRREL3, TRIM36, and GABBR2 were downregulated with the smallest p values. Several pathways were associated with the differentially expressed genes and involved in cellular proliferation, cell migration, and endocrine system tumor progression, which may contribute to the pathogenesis of PTC. Upregulation of SFRP4, ZNF90, and DCN at the mRNA level was further validated with RT-PCR, and DCN expression was further confirmed with immunostaining of PTC samples. Conclusion: These results provide new insights into the molecular mechanisms of PTC. Identification of differentially expressed genes should not only improve the tumor signature for thyroid tumors as a diagnostic biomarker but also reveal potential targets for thyroid tumor treatment.

Background: We research the binding function proteins in Elymus nutans. Recognition for proteins is essential for study of biology. Machine learning methods have been widely used for the prediction of proteins. Methods: We used BLAST software for the function annotations of Elymus nutans. Besides, we used machine learning methods to recognize proteins which are not annotated by the software. In the process, we focused on identifying the proteins with binding functions. In our research, features are extracted by four algorithms, and then selected by mutual information estimator. Here three classifiers are constructed based on K-nearest neighbour algorithm and gradient boosting algorithm. Results and Conclusion: Experimental results show that there are 848 proteins with ATP binding function, 113 proteins with heme binding function, 315 proteins with zinc-ion binding function, 135 proteins with GTP binding function and 21 proteins with ADP binding function. Furthermore, we have successfully predicted the functions of 10 special protein sequences whose function annotations cannot be obtained by making sequence alignment with seven famous protein databases. Among them, seven sequences have ATP binding functions, one sequence has heme binding function, one sequence has zinc-ion binding function and the other one has GTP binding function.