Gene silencing of Helicobacter pylori through newly designed siRNA convenes the treatment of gastric cancer.

BACKGROUND: Helicobacter pylori is a gastric pathogen that is responsible for causing chronic inflammation and increasing the risk of gastric cancer development. It is capable of persisting for decades in the harsh gastric environment because of the inability of the host to eradicate the infection. Several treatment strategies have been developed against this bacterium using different antibiotics. But the effectiveness of treating H. pylori has significantly decreased due to widespread antibiotic resistance, including an increased risk of gastric cancer. The small interfering RNAs (siRNA), which is capable of sequence-specific gene-silencing can be used as a new therapeutic approach for the treatment of a variety of such malignancies. In the current study, we rationally designed two siRNA molecules to silence the cytotoxin-associated gene A (CagA) and vacuolating cytotoxin A (VacA) genes of H. pylori for their significant involvement in developing cancer. METHODS: We selected a common region of all the available transcripts from different countries of CagA and VacA to design the siRNA molecules. The final siRNA candidate was selected based on the results from machine learning algorithms, off-target similarity, and various thermodynamic properties. RESULT: Further, we utilized molecular docking and all atom molecular dynamics (MD) simulations to assess the binding interactions of the designed siRNAs with the major components of the RNA-induced silencing complex (RISC) and results revealed the ability of the designed siRNAs to interact with the proteins of RISC complex in comparable to those of the experimentally reported siRNAs. CONCLUSION: These designed siRNAs should effectively silence the CagA and VacA genes of H. pylori during siRNA mediated treatment in gastric cancer.

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure-activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set's modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.

Pathogen-driven gene expression patterns lead to a novel approach to the identification of common therapeutic targets.

Developing a common medication strategy for disease control and management could be greatly beneficial. Investigating the differences between diseased and healthy states using differentially expressed genes aids in understanding disease pathophysiology and enables the exploration of protein-drug interactions. This study aimed to find the most common genes in diarrhea-causing bacteria such as Salmonella enterica serovar Typhimurium, Campylobacter jejuni, Escherichia coli, Shigella dysenteriae (CESS) to find new drugs. Thus, differential gene expression datasets of CESS were screened through computational algorithms and programming. Subsequently, hub and common genes were prioritized from the analysis of extensive protein-protein interactions. Binding predictions were performed to identify the common potential therapeutic targets of CESS. We identified a total of 827 dysregulated genes that are highly linked to CESS. Notably, no common gene interaction was found among all CESS bacteria, but we identified 3 common genes in both Salmonella-Escherichia and Escherichia-Campylobacter infections. Later, out of 73 protein complexes, molecular simulations confirmed 5 therapeutic candidates from the CESS. We have developed a new pipeline for identifying therapeutic targets for a common medication strategy against CESS. However, further wet-lab validation is needed to confirm their effectiveness.

Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability.

Genetic polymorphisms in DNA damage repair and tumor suppressor genes have been associated with increasing the risk of several types of cancer. Analyses of putative functional single nucleotide polymorphisms (SNP) in such genes can greatly improve human health by guiding choice of therapeutics. In this study, we selected nine genes responsible for various cancer types for gene enrichment analysis and found that BRCA1, ATM, and TP53 were more enriched in connectivity. Therefore, we used different computational algorithms to classify the nonsynonymous SNPs which are deleterious to the structure and/or function of these three proteins. The present study showed that the major pathogenic variants (V1687G and V1736G of BRCA1, I2865T and V2906A of ATM, V216G and L194H of TP53) might have a greater impact on the destabilization of the proteins. To stabilize the high-risk SNPs, we performed mutation site-specific molecular docking analysis and validated using molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) studies. Additionally, SNPs of untranslated regions of these genes affecting miRNA binding were characterized. Hence, this study will assist in developing precision medicines for cancer types related to these polymorphisms.

Newly designed analogues from SARS-CoV inhibitors mimicking the druggable properties against SARS-CoV-2 and its novel variants.

The emerging variants of SARS coronavirus-2 (SARS-CoV-2) have been continuously spreading all over the world and have raised global health concerns. The B.1.1.7 (United Kingdom), P.1 (Brazil), B.1.351 (South Africa) and B.1.617 (India) variants, resulting from multiple mutations in the spike glycoprotein (SGp), are resistant to neutralizing antibodies and enable increased transmission. Hence, new drugs might be of great importance against the novel variants of SARS-CoV-2. The SGp and main protease (Mpro) of SARS-CoV-2 are important targets for designing and developing antiviral compounds for new drug discovery. In this study, we selected seventeen phytochemicals and later performed molecular docking to determine the binding interactions of the compounds with the two receptors and calculated several drug-likeliness properties for each compound. Luteolin, myricetin and quercetin demonstrated higher affinity for both the proteins and interacted efficiently. To obtain compounds with better properties, we designed three analogues from these compounds and showed their greater druggable properties compared to the parent compounds. Furthermore, we found that the analogues bind to the residues of both proteins, including the recently identified novel variants of SARS-CoV-2. The binding study was further verified by molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) approaches by assessing the stability of the complexes. MD simulations revealed that Arg457 of SGp and Met49 of Mpro are the most important residues that interacted with the designed inhibitors. Our analysis may provide some breakthroughs to develop new therapeutics to treat the proliferation of SARS-CoV-2 in vitro and in vivo.

Molecular characterization and functional annotation of a hypothetical protein (SCO0618) of Streptomyces coelicolor A3(2).

Streptomyces coelicolor is a gram-positive soil bacterium which is well known for the production of several antibiotics used in various biotechnological applications. But numerous proteins from its genome are considered hypothetical proteins. Therefore, the present study aimed to reveal the functions of a hypothetical protein from the genome of S. coelicolor. Several bioinformatics tools were employed to predict the structure and function of this protein. Sequence similarity was searched through the available bioinformatics databases to find out the homologous protein. The secondary and tertiary structure were predicted and further validated with quality assessment tools. Furthermore, the active site and the interacting proteins were also explored with the utilization of CASTp and STRING server. The hypothetical protein showed the important biological activity having with two functional domain including POD-like_MBL-fold and rhodanese homology domain. The functional annotation exposed that the selected hypothetical protein could show the hydrolase activity. Furthermore, protein-protein interactions of selected hypothetical protein revealed several functional partners those have the significant role for the bacterial survival. At last, the current study depicts that the annotated hypothetical protein is linked with hydrolase activity which might be of great interest to the further research in bacterial genetics.