Identification and Validation of Functional miRNAs and Their Main Targets in Sorghum bicolor

MicroRNAs (miRNAs) are typically non-coding RNAs of 18-26 nucleotides (nts) that are produced endogenously and regulated post-transcriptionally through degradation or translational repression. Since miRNAs are evolutionarily conserved, their preservation is essential for important regulatory functions in plant development, growth, and responses to environmental stress. Sorghum bicolor (sbi) is a valuable food and fodder crop which is grown worldwide. A range of sbi miRNAs were identified so far as being connected to plant development and stress responses. Herein, we employed a variety of bioinformatics tools for miRNA profiling in sbi and a PCR-based platform for the validation of these miRNAs. In total, 74 new conserved sbi miRNAs from 52 miRNA families have been predicted. Using the psRNA Target method, 10613 different protein targets of these predicted miRNAs have been attained. These targets include 54 GO-terms which have substantial targets in the biological, molecular, and cellular processes. We particularly found that the sbi-miR1861c and sbi-miR5050 are involved to regulate sulphur compound biosynthetic process, while the significant spliceosomal complex is regulated by sbi-miR815b and sbi-miR7768b. Also, we report that the pre-ribosome, electron transport chain, cell communication, cellular respiration, protein localization, and photosynthesis are controlled by sbi-miR2907b, sbi-miR530, sbi-miR7749, sbi-miR1858a, sbi-mi7729a, and sbi-miR417, respectively. The identification and validation of these novel sbi miRNAs shall contribute a lot in improving the crop yield and ensure sustainable agriculture.

Potentials of Terpenoids as Inhibitors of Multiple Plasmodium falciparum Protein Drug Targets

PURPOSE

The resistance of parasite to readily affordable antimalarial drugs, the high cost of currently potent drugs, and the resistance of vector mosquitoes to insecticides threaten the possibility of malaria eradication in malaria endemic areas. Due to the fact that quinine and artemisinin were isolated from plants sources, researchers have been encouraged to search for new antimalarials from medicinal plants. This is especially the case in Africa where a large percentage of the population depends on medicinal plant to treat malaria and other ailments.

METHOD

In this study, we evaluated previously characterized Plasmodium-cidal compounds obtained from the African flora to identify their likely biochemical targets, for an insight into their possible antimalarial chemotherapy. Molecular docking study was first conducted, after which remarkable compounds were submitted for molecular dynamic (MD) simulations studies.

RESULTS

From a total of 38 Plasmodium-cidal compounds docked with confirmed Plasmodium falciparum protein drug targets [plasmepsin II (PMII), histo-aspartic protein (HAP) and falcipain-2 (FP)], two pentacyclic triterpene, cucurbitacin B and 3 beta-O-acetyl oleanolic acid showed high binding affinity relative to artesunate. This implies their capacity to inhibit the three selected P. falciparum target proteins, and consequently, antimalarial potential. From the MD simulations studies and binding free energy outcomes, results confirmed that the two compounds are stable in complex with the selected antimalarial targets; they also showed excellent binding affinities during the 100 ns simulation.

CONCLUSION

These results showed that cucurbitacin B and 3 beta-O-acetyl oleanolic acid are potent antimalarials and should be considered for further studies.

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Extracting "high ranking" or "prime protein targets" (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations - ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository ( http://DUD-E.decoys.org ) are matched against 1040 ligands using "forward modeling" machine learning for initial "data mining and modeling" (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called "reverse modeling" (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data.

Nutritional Profile, GC-MS Analysis and In-silico Anti-diabetic Phytocompounds Candidature of Jatropha gossypifolia Leaf Extracts

BACKGROUND

Diabetes mellitus (DM) is a metabolic disorder known to impair many physiological functions via reactive oxygen species (ROS). Aldose reductase, sorbitol dehydrogenase, dipeptidyl peptidase IV, α-amylase and α-glucosidase are pharmacotherapeutic protein targetsin type-2 diabetes mellitus (T2DM). Inhibitors of these enzymes constitute a new class of drugs used in the management and treatment of T2DM. Some reports have claimed that medicinal plant extracts that serves as food (and as an antioxidant source) can reduce these alterations by eliminating ROS caused by DM. Ethnobotanical survey claims Jatropha gossypifolia commonly called “fig-nut” and “Lapa- lapa” in the Yoruba land of South-western Nigeria, to be used for the treatment and management of diabetes, in addition to its nutritive value.

OBJECTIVE

The nutritional composition and in-silico antidiabetic potential of the bioactive constituents of J. gossypifolia leaf extracts were investigated.

METHODS

Proximate, minerals and gas chromatography-mass spectroscopy (GC-MS) analysis were carried out using standard procedures. Phytocompounds present in J. gossypifolia methanol (JGM) and ethyl acetate (JGE) leaf extracts were tested as potential antagonists of selected protein targets via in-silico techniques. Drug-likeness, pharmacokinetic properties and toxicity of the promising docked ligands were also predicted.

RESULTS

Proximate, minerals and gas chromatography–mass spectroscopy (GC-MS) analysis were carried out using standard procedures. Phytocompounds present in J. gossypifolia methanol (JGM) and ethyl acetate (JGE) leaf extracts were tested for their potential antagonistic effects on selected protein targets via in-silico techniques. Drug-likeness, pharmacokinetic properties and toxicity of the promising docked ligands were also predicted. Results: The proximate and mineral analysis revealed

CONCLUSION

Benzene-1,2,4,5-tetramethyl from JGE extracts exhibited the most promising antidia- betic potential in-silico, suggesting its candidature as diabetes-target-protein inhibitor which may be developed for the treatment of type-2 diabetes mellitus.

Protein targets in Mycobacterium tuberculosis and their inhibitors for therapeutic implications: A narrative review

Advancement in the area of anti-tubercular drug development has been full-fledged, yet, a very less number of drug molecules have reached phase II clinical trials, and therefore "End-TB" is still a global challenge. Inhibitors to specific metabolic pathways of Mycobacterium tuberculosis (Mtb) gain importance in strategizing anti-tuberculosis drug discovery. The lead compounds that target DNA replication, protein synthesis, cell wall biosynthesis, bacterial virulence and energy metabolism are emerging as potential chemotherapeutic options against Mtb growth and survival within the host. In recent times, the in silico approaches have become most promising tools in the identification of suitable inhibitors for specific protein targets of Mtb. An update in the fundamental understanding of these inhibitors and the mechanism of interaction may bring hope to future perspectives in novel drug development and delivery approaches. This review provides a collective impression of the small molecules with potential antimycobacterial activities and their target pathways in Mtb such as cell wall biosynthesis, DNA replication, transcription and translation, efflux pumps, antivirulence pathways and general metabolism. The mechanism of interaction of specific inhibitor with their respective protein targets has been discussed. The comprehensive knowledge of such an impactful area of research would essentially reflect in the discovery of novel drug molecules and effective delivery approaches. This narrative review encompasses the knowledge of emerging targets and promising n that could potentially translate in to the anti-TB-drug discovery.

The potential value of amlexanox in the treatment of cancer: Molecular targets and therapeutic perspectives

Amlexanox (AMX) is an azoxanthone drug used for decades for the treatment of mouth aphthous ulcers and now considered for the treatment of diabetes and obesity. The drug is usually viewed as a dual inhibitor of the non-canonical IκB kinases IKK-ɛ (inhibitor-kappaB kinase epsilon) and TBK1 (TANK-binding kinase 1). But a detailed target profile analysis indicated that AMX binds directly to twelve protein targets, including different enzymes (IKK-ɛ, TBK1, GRK1, GRK5, PDE4B, 5- and 12-lipoxygenases) and non-enzyme proteins (FGF-1, HSP90, S100A4, S100A12, S100A13). AMX has been demonstrated to have marked anticancer effects in multiple models of xenografted tumors in mice, including breast, colon, lung and gastric cancers and in onco-hematological models. The anticancer potency is generally modest but largely enhanced upon combination with cytotoxic (temozolide, docetaxel), targeted (selumetinib) or biotherapeutic agents (anti-PD-1 and anti-CTLA4 antibodies). The multiple targets participate in the anticancer effects, chiefly IKK-ɛ/TBK1 but also S100A proteins and PDE4B. The review presents the molecular basis of the antitumor effects of AMX. The capacity of the drug to block nonsense-mediated mRNA decay (NMD) is also discussed, as well as AMX-induced reduction of cancer-related pain. Altogether, the analysis provides a survey of the anticancer action of AMX, with the implicated protein targets. The use of this well-tolerated drug to treat cancer should be further considered and the design of newer analogues encouraged.

Network pharmacology to investigate the pharmacological mechanisms of muscone in Xingnaojing injections for the treatment of severe traumatic brain injury

Background

Xingnaojing injections (XNJI) are widely used in Chinese medicine to mitigate brain injuries. An increasing number of studies have shown that XNJI may improve neurological function. However, XNJI's active ingredients and molecular mechanisms when treating traumatic brain injury (TBI) are unknown.

Methods

XNJI's chemical composition was acquisited from literature and the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. We used the "absorption, distribution, metabolism, and excretion" (ADME) parameter-based virtual algorithm to further identify the bioactive components. We then screened data and obtained target information regarding TBI and treatment compounds from public databases. Using a Venn diagram, we intersected the information to determine the hub targets. Cytoscape was used to construct and visualize the network. In accordance with the hub proteins, we then created a protein-protein interaction (PPI) network using STRING 11.0. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed according to the DAVID bioinformatics resource database (ver. 6.8). We validated the predicted compound's efficacy using the experimental rat chronic constriction injury (CCI) model. The neuronal apoptosis was located using the TUNEL assay and the related pathways' hub proteins were determined by PCR, Western blot, and immunohistochemical staining.

Results

We identified 173 targets and 35 potential compounds belonging to XNJI. STRING analysis was used to illustrate the protein-protein interactions and show that muscone played a fundamental role in XNJI's efficacy. Enrichment analysis revealed critical signaling pathways in these components' potential protein targets, including PI3K/AKT1, NF-kB, and p53. Moreover, the hub proteins CASP3, BCL2L1, and CASP8 were also involved in apoptosis and were associated with PI3K/AKT, NF-kB, and p53 signaling pathways. We showed that muscone and XNJI were similarly effective 168 h after CCI, demonstrating that the muscone in XNJI significantly attenuated neuronal apoptosis through the PI3K/Akt1/NF-kB/P53 pathway.

Conclusion

We verified the neuroprotective mechanism in muscone for the first time in TBI. Network pharmacology offers a new approach for identifying the potential active ingredients in XNJI.

Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology

Recently, different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing significant amounts of chemical data obtaining outcomes to find potential drug candidates.

Credible Protein Targets and Curative Strategies for COVID-19: a Review

The pandemic of coronavirus infection 2019 (COVID-19) due to the serious respiratory condition created by the coronavirus 2 (SARS-CoV-2) presents a challenge to recognize effective strategies for management and treatment. In general, COVID-19 is an acute disease that can also be fatal, with an ongoing 10.2% case morbidity rate. Extreme illness may bring about death because of enormous alveolar damage and hemorrhage along with progressive respiratory failure. The rapidly expanding information with respect to SARS-CoV-2 research suggests a substantial number of potential drug targets. The most encouraging treatment to date is suggested to be with the help of remdesivir, hydroxychloroquine, and many such repurposed drugs. Remdesivir has a strong in vitro activity for SARS-CoV-2, yet it is not the drug of choice as affirmed by the US Food and Drug Administration and presently is being tried in progressing randomized preliminaries. The COVID-19 pandemic has been the worst worldwide general health emergency of this age and, possibly, since the pandemic influenza outbreak of 1918. The speed and volume of clinical preliminaries propelled to examine potential treatments for COVID-19 feature both the need and capacity to create abundant evidence even in the center of a pandemic. No treatments have been demonstrated as accurate and dependable to date. This review presents a concise precise of the targets and broad treatment strategies for the benefit of researchers.

VIRdb: a comprehensive database for interactive analysis of genes/proteins involved in the pathogenesis of vitiligo

Vitiligo is a chronic asymptomatic disorder affecting melanocytes from the basal layer of the epidermis which leads to a patchy loss of skin color. Even though it is one of the neglected disease conditions, people suffering from vitiligo are more prone to psychological disorders. As of now, various studies have been done in order to project auto-immune implications as the root cause. To understand the complexity of vitiligo, we propose the Vitiligo Information Resource (VIRdb) that integrates both the drug-target and systems approach to produce a comprehensive repository entirely devoted to vitiligo, along with curated information at both protein level and gene level along with potential therapeutics leads. These 25,041 natural compounds are curated from Natural Product Activity and Species Source Database. VIRdb is an attempt to accelerate the drug discovery process and laboratory trials for vitiligo through the computationally derived potential drugs. It is an exhaustive resource consisting of 129 differentially expressed genes, which are validated through gene ontology and pathway enrichment analysis. We also report 22 genes through enrichment analysis which are involved in the regulation of epithelial cell differentiation. At the protein level, 40 curated protein target molecules along with their natural hits that are derived through virtual screening. We also demonstrate the utility of the VIRdb by exploring the Protein-Protein Interaction Network and Gene-Gene Interaction Network of the target proteins and differentially expressed genes. For maintaining the quality and standard of the data in the VIRdb, the gold standard in bioinformatics toolkits like Cytoscape, Schrödinger's GLIDE, along with the server installation of MATLAB, are used for generating results. VIRdb can be accessed through "http://www.vitiligoinfores.com/".

Network pharmacology-based analysis on bioactive anti-diabetic compounds in Potentilla discolor bunge

ETHNOPHARMACOLOGICAL RELEVANCE

Potentilla discolor Bunge (PDB) is a commonly used herbal for alleviating diabetes mellitus and its complications. Although accumulating evidences show the anti-diabetic efficacy of PDB, the vital anti-diabetic compounds and their functional targets remain elusive.

AIM OF THE STUDY

To investigate the anti-diabetic ingredients and their functional mechanisms in PDB, gas chromatograph-mass spectrometry analysis was performed on PDB extract and 21 were testified as anti-diabetic compounds.

MATERIALS AND METHODS

Subsequently their potential protein targets were also identified. The bioinformatics analysis was implemented by network pharmacology-based approaches. STRING analysis was performed to reveal enrichment of these target proteins, protein-protein interactions, pathways and related diseases. Cytoscape was used to determine the potential protein targets for these components in PDB, indicating that 21 anti-diabetic compounds in PDB regulate 33 diabetes-related proteins in 28 signal pathways and involve 21 kinds of diabetes-related diseases. Among the 21 potential anti-diabetic components predicted by network analysis, tricetin was firstly experimentally validated at the molecular and cellular level.

RESULTS

Results indicated that this active small-molecule compound may have beneficial effects on improving glucose uptake.

CONCLUSIONS

We envisage that network analysis will be useful in screening bioactive compounds of medicinal plants.

The proteome targets of intracellular targeting antimicrobial peptides

Antimicrobial peptides have been considered well-deserving candidates to fight the battle against microorganisms due to their broad-spectrum antimicrobial activities. Several studies have suggested that membrane disruption is the basic mechanism of AMPs that leads to killing or inhibiting microorganisms. Also, AMPs have been reported to interact with macromolecules inside the microbial cells such as nucleic acids (DNA/RNA), protein synthesis, essential enzymes, membrane septum formation and cell wall synthesis. Proteins are associated with many intracellular mechanisms of cells, thus protein targets may be specifically involved in mechanisms of action of AMPs. AMPs like pyrrhocoricin, drosocin, apidecin and Bac 7 are documented to have protein targets, DnaK and GroEL. Moreover, the intracellular targeting AMPs are reported to influence more than one protein targets inside the cell, suggesting for the multiple modes of actions. This complex mechanism of intracellular targeting AMPs makes them more difficult for the development of resistance. Herein, we have summarized the current status of AMPs in terms of their mode of actions, entry to cytoplasm and inhibition of macromolecules. To reveal the mechanism of action, we have focused on AMPs with intracellular protein targets. We have also included the use of high-throughput proteome microarray to determine the unidentified AMP protein targets in this review.

A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure

When evaluating compound similarity, addressing multiple sources of information to reach conclusions about common pharmaceutical and/or toxicological mechanisms of action is a crucial strategy. In this chapter, we describe a systems biology approach that incorporates analyses of hepatotoxicant data for 33 compounds from three different sources: a chemical structure similarity analysis based on the 3D Tanimoto coefficient, a chemical structure-based protein target prediction analysis, and a cross-study/cross-platform meta-analysis of in vitro and in vivo human and rat transcriptomics data derived from public resources (i.e., the diXa data warehouse). Hierarchical clustering of the outcome scores of the separate analyses did not result in a satisfactory grouping of compounds considering their known toxic mechanism as described in literature. However, a combined analysis of multiple data types may hypothetically compensate for missing or unreliable information in any of the single data types. We therefore performed an integrated clustering analysis of all three data sets using the R-based tool iClusterPlus. This indeed improved the grouping results. The compound clusters that were formed by means of iClusterPlus represent groups that show similar gene expression while simultaneously integrating a similarity in structure and protein targets, which corresponds much better with the known mechanism of action of these toxicants. Using an integrative systems biology approach may thus overcome the limitations of the separate analyses when grouping liver toxicants sharing a similar mechanism of toxicity.

Expression of the moss PpLEA4-20 gene in rice enhances membrane protection and client proteins stability

Green vegetative tissues of the moss Physcomitrella patens possess a powerful ability to tolerate severe drought stress. Proteomics analysis have revealed that a large number of late embryogenesis abundant (LEA) proteins were key players in the drought tolerance of the photosynthetic tissues. PpLEA4-20, a member of the moss LEA protein family, was selected for further function study using an ectopic expression method in rice. Through molecular identification via PCR, southern blotting and TAIL-PCR, we demonstrated that the PpLEA4-20 gene was transformed and inserted into a non-encoded region in chromosome 4 of rice and expressed stably in transgenic rice. Unexpectedly, PpLEA4-20 protein emerged as two high-expressed spots on 2-D gels generated from transgenic rice, suggesting that PpLEA4-20 proteins are complete compatible and might be modified in rice. Both growth and physiological analysis showed that seedlings of transgenic PpLEA4-20 rice displayed altered phenotypes and tolerance to salt. In addition, electrolyte leakage was reduced in transgenic PpLEA4-20 compared to wild type under stress conditions. Anti-aggregation analysis found that the PpLEA4-20 protein expressed in rice remained soluble at high temperature and in addition to some native proteins from transgenic PpLEA4-20 rice. Based on Nano LC MS/MS analysis, we identified several proteins from transgenic PpLEA4-20 rice of increased heat-stability. Our results provide evidence for a role of PpLEA4-20 in salt tolerance and stabilization of client proteins.