Transcriptome scale analysis to decode the differential sucrose accumulation mechanisms in sugarcane under the effect of gibberellin.

In the present study, we analyzed GA3 (gibberellin)-treated sugarcane samples at the transcriptomic level to elucidate the differential expression of genes that influence sucrose accumulation. Previous research has suggested that GA3 application can potentially delay sink saturation by enhancing sink strength and demand, enabling the accommodation of more sucrose. To investigate the potential role of GA-induced modification of sink capacity in promoting higher sucrose accumulation, we sought to unravel the differential expression of transcripts and analyze their functional annotation. Several genes homologous to the sugar-phosphate/phosphate translocator, UTP-glucose-1-phosphate uridylyltransferase, and V-ATPases (vacuolar-type H+ ATPase) were identified as potentially associated with the increased sucrose content observed. A differentially expressed transcript was found to be identical to the mRNA of an unknown protein. Homology-based bioinformatics analysis suggested it to be a hydrolase enzyme, which could potentially act as a stimulator of sucrose buildup. The database of differentially expressed transcripts obtained in this study under the influence of GA3 represents a valuable addition to the sugarcane transcriptomics and functional genomics knowledge base.

Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces.

SARS-CoV-2, the virus responsible for COVID-19, posed a significant threat to the world. We analyzed COVID-19 dissemination data in the top ten Indian provinces by infection incidences using the Susceptible-Infectious-Removed (SIR) model, an Autoregressive Integrated Moving Average (ARIMA) time series model, a machine learning model based on the Random Forest, and distribution fitting. Outbreaks are expected to continue if the Basic Reproduction Number (R0) > 1, and infection waves are anticipated to end if the R0 < 1, as determined by the SIR model. Different parametric probability distributions are also fitted. Data collected from December 12, 2021, to March 31, 2022, encompassing data from both before and during the implementation of strict control measures. Based on the estimates of the model parameters, health agencies and government policymakers can develop strategies to combat the spread of the disease in the future, and the most effective technique can be recommended for real-world application for other outbreaks of COVID-19. The best method out of these could be also implemented further on the epidemiological data of other similar infectious agents.

A review on potential therapeutic targets for the treatment of leishmaniasis.

Leishmania, a protozoan parasite, is responsible for the occurrence of leishmaniasis, a disease that is prevalent in tropical regions. Visceral Leishmaniasis (VL), also known as kala-azar in Asian countries, is one of the most significant forms of VL, along with Cutaneous Leishmaniasis (CL) and Mucocutaneous Leishmaniasis (ML). Management of this condition typically entails the use of chemotherapy as the sole therapeutic option. The current treatments for leishmaniasis present several drawbacks, including a multitude of side effects, prolonged treatment duration, disparate efficacy across different regions, and the emergence of resistance. To address this urgent need, it is imperative to identify alternative treatments that are both safer and more effective. The identification of appropriate pharmacological targets in conjunction with biological pathways constitutes the initial stage of drug discovery. In this review, we have addressed the key metabolic pathways that represent potential pharmacological targets as well as prominent treatment options for leishmaniasis.

Comparative analysis of SilA-laccase mediated degradation of ciprofloxacin, norfloxacin and ofloxacin and interpretation of the possible catalytic mechanism.

Fluoroquinolones (FQs) are the most commonly used antimicrobial drugs and regardless of their advantages in the healthcare sector, the pollution of these antimicrobial drugs in the environment has big concerns about human and environmental health. The presence of these antibiotic drugs even at the lowest concentrations in the environment has resulted in the emergence and spread of antibiotic resistance. Hence, it is necessary to remediate these pollutants from the environment. Previously alkaline laccase (SilA) from Streptomyces ipomoeae has been demonstrated to show degrading potentials against two of the FQs, Ciprofloxacin (CIP) and Norfloxacin (NOR); however, the molecular mechanism was not elucidated in detail. In this study, we have analyzed the possible molecular catalytic mechanism of FQ degrading SilA-laccase for the degradation of the FQs, CIP, NOR and Ofloxacin (OFL) using three-dimensional protein structure modeling, molecular docking and molecular dynamic (MD) studies. The comparative protein sequence analysis revealed the presence of tetrapeptide conserved catalytic motif, His102-X-His104-Gly105. After evaluating the active site of the enzyme in depth using CDD, COACH and S-site tools, we have identified the catalytic triad composed of three conserved amino acid residues, His102, Val103 and Tyr108 with which ligands interacted during the catalysis process. By analyzing the MD trajectories, it is revealed that the highest degradation potential of SilA is for CIP followed by NOR and OFL. Ultimately, this study provides the possible comparative catalytic mechanism for the degradation of CIP, NOR and OFL by the SilA enzyme.Communicated by Ramaswamy H. Sarma.

Validating Fractalkine receptor as a target and identifying candidates for drug discovery against type 2 diabetes.

Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases employing abnormal levels of insulin. Enhancing the insulin production is greatly aided by the regulatory mechanisms of the Fractalkine receptor (CX3CR1) system in islet ß-cell function. However, elements including a high-fat diet, obesity, and ageing negatively impact the expression of CX3CR1 in islets. CX3CL1/CX3CR1 receptor-ligand complex is now recognized as a novel therapeutic target. It suggests that T2DM-related ß-cell dysfunction may result from lower amount of these proteins. We analyzed the differential expression of CX3CR1 gene samples taken from persons with T2DM using data obtained from the Gene Expression Omnibus database. Homology modeling enabled us to generate the three-dimensional structure of CX3CR1 and a possible binding pocket. The optimized CX3CR1 structure was subjected to rigorous screening against a massive library of 693 million drug-like molecules from the ZINC15 database. This screening process led to the identification of three compounds with strong binding affinity at the identified binding pocket of CX3CR1. To further evaluate the potential of these compounds, molecular dynamics simulations were conducted over a 50 ns time scale to assess the stability of the protein-ligand complexes. These simulations revealed that ZINC000032506419 emerged as the most promising drug-like compound among the three potent molecules. The discovery of ZINC000032506419 holds exciting promise as a potential therapeutic agent for T2D and other related metabolic disorders. These findings pave the way for the development of effective medications to address the complexities of T2DM and its associated metabolic diseases.

Combating Staphylococcus aureus biofilm formation: the inhibitory potential of tormentic acid and 23-hydroxycorosolic acid.

Plant extracts have been used to treat microbiological diseases for centuries. This study examined plant triterpenoids tormentic acid (TA) and 23-hydroxycorosolic acid (HCA) for their antibiofilm effects on Staphylococcus aureus strains (MTCC-96 and MTCC-7405). Biofilms are bacterial colonies bound by a matrix of polysaccharides, proteins, and DNA, primarily impacting healthcare. As a result, ongoing research is being conducted worldwide to control and prevent biofilm formation. Our research showed that TA and HCA inhibit S. aureus planktonic growth by depolarizing the bacterial membrane. In addition, zone of inhibition studies confirmed their effectiveness, and crystal violet staining and biofilm protein quantification confirmed their ability to prevent biofilm formation. TA and HCA exhibited substantial reductions in biofilm formation for S. aureus (MTCC-96) by 54.85% and 48.6% and for S. aureus (MTCC-7405) by 47.07% and 56.01%, respectively. Exopolysaccharide levels in S. aureus biofilm reduced significantly by TA (25 µg/mL) and HCA (20 µg/mL). Microscopy, bacterial motility, and protease quantification studies revealed their ability to reduce motility and pathogenicity. Furthermore, TA and HCA treatment reduced the mRNA expression of S. aureus virulence genes. In silico analysis depicted a high binding affinity of triterpenoids for biofilm and quorum-sensing associated proteins in S. aureus, with TA having the strongest affinity for TarO (- 7.8 kcal/mol) and HCA for AgrA (- 7.6 kcal/mol). TA and HCA treatment reduced bacterial load in S. aureus-infected peritoneal macrophages and RAW264.7 cells. Our research indicates that TA and HCA can effectively combat S. aureus by inhibiting its growth and suppressing biofilm formation.

Deciphering drug discovery and microbial pathogenesis research in tuberculosis during the two decades of postgenomic era using entity mining approach.

We examined literature on Mycobacterium tuberculosis (Mtb) subsequent to its genome release, spanning years 1999-2020. We employed scientometric mapping, entity mining, visualization techniques, and PubMed and PubTator databases. Most popular keywords, most active research groups, and growth in quantity of publications were determined. By gathering annotations from the PubTator, we determined direction of research in the areas of drug hypersensitivity, drug resistance (AMR), and drug-related side effects. Additionally, we examined the patterns in research on Mtb metabolism and various forms of tuberculosis, including skin, brain, pulmonary, extrapulmonary, and latent tuberculosis. We discovered that 2011 had the highest annual growth rate of publications, at 19.94%. The USA leads the world in publications with 18,038, followed by China with 14,441, and India with 12,158 publications. Studies on isoniazid and rifampicin resistance showed an enormous increase. Non-tuberculous mycobacteria also been the subject of more research in effort to better understand Mtb physiology and as model organisms. Researchers also looked at co-infections like leprosy, hepatitis, plasmodium, HIV, and other opportunistic infections. Host perspectives like immune response, hypoxia, and reactive oxygen species, as well as comorbidities like arthritis, cancer, diabetes, and kidney disease etc. were also looked at. Symptomatic aspects like fever, coughing, and weight loss were also investigated. Vitamin D has gained popularity as a supplement during illness recovery, however, the interest of researchers declined off late. We delineated dominant researchers, journals, institutions, and leading nations globally, which is crucial for aligning ongoing and evolving landscape of TB research efforts. Recognising the dominant patterns offers important information about the areas of focus for current research, allowing biomedical scientists, clinicians, and organizations to strategically coordinate their efforts with the changing priorities in the field of tuberculosis research.

Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models.

The global impact of COVID-19 has heightened concerns about emerging viral infections, among which monkeypox (MPOX) has become a significant public health threat. To address this, our study employs a comprehensive approach using three statistical techniques: Distribution fitting, ARIMA modeling, and Random Forest machine learning to analyze and predict the spread of MPOX in the top ten countries with high infection rates. We aim to provide a detailed understanding of the disease dynamics and model theoretical distributions using country-specific datasets to accurately assess and forecast the disease's transmission. The data from the considered countries are fitted into ARIMA models to determine the best time series regression model. Additionally, we employ the random forest machine learning approach to predict the future behavior of the disease. Evaluating the Root Mean Square Errors (RMSE) for both models, we find that the random forest outperforms ARIMA in six countries, while ARIMA performs better in the remaining four countries. Based on these findings, robust policy-making should consider the best fitted model for each country to effectively manage and respond to the ongoing public health threat posed by monkeypox. The integration of multiple modeling techniques enhances our understanding of the disease dynamics and aids in devising more informed strategies for containment and control.

Targeting caspase pathway by novel N-Me aziridine derivatives for hepatocellular carcinoma drug discovery.

Azaheterocycles are three-membered rings, known as aziridines, that occur naturally and have pharmaceutical applications.These compounds are present as several secondary metabolites produced by plants and microorganisms.Recent studies have demonstrated the effectiveness of aziridine derivatives (N-H/N-Me) as anticancer agents.We synthesized 18 compounds containing an N-Me enone aziridine group, the chemistry of which has been previously published. However, these compounds have drug-likeness properties; therefore, we aimed to demonstrate their drug-like properties using in silico and in vitro investigations.The molecular structures of the compounds were optimized using density functional theory (DFT). The ADMET parameters of the derivatives were calculated using SwissADME and PreADMET. Additionally, these derivatives were evaluated for their ability to bind to caspase-3 and caspase-9 and then subjected to molecular docking. The lead chemical AY128 maintained stable complexes with target proteins during molecular dynamics simulations, as evidenced by the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) parameters. In vitro cytotoxicity and ELISA tests showed that the novel aziridine derivatives, especially AY128, had strong anticancer activity against HepG2 hepatocellular carcinoma cells.Our study suggests that AY128 may be a potential drug candidate for hepatocellular carcinoma through the caspase-3 and caspase-9-dependent apoptotic pathways.Communicated by Ramaswamy H. Sarma.

Molecular evolution steered structural adaptations in the DNA polymerase III α subunit of halophilic bacterium Salinibacter ruber.

A significant portion of the earth has a salty environment, and the literature on bacterial survival mechanisms in salty environments is limited. During molecular evolution, halophiles increase acidic amino acid residues on their protein surfaces which leads to a negatively charged surface potential that helps them to maintain the protein integrity and protect them from denaturation by competing with salt ions. Through protein family analysis, we have investigated the molecular-level adaptive features of DNA polymerase III's catalytic subunit (alpha) and its structure-function relationship. This study throws light on the novel understanding of halophilic bacterial replication and the molecular basis of salt adaptation. Comparisons of the amino acid contents and electronegativity of halophilic and mesophilic bacterial proteins revealed adaptations that allow halophilic bacteria to thrive in high salt concentrations. A significantly lower isoelectric point of halophilic bacterial proteins indicates the acidic nature. Also, an abundance of disordered regions in halophiles suggests the requirement of the salt ions that play a crucial role in their stable protein folding. Despite having similar topology, mesophilic and halophilic proteins, a set of very prominent molecular modifications was observed in the alpha subunit of halophiles.

Frequent brushing of teeth inhibits the dissemination of the SARS-CoV-2: the biochemical mechanism.

The SARS-CoV-2 virus is primarily transmitted through direct or indirect contact with the mucous membranes of the mouth and nostrils. The presence of SARS-CoV-2 in sputum with a high viral load suggested that maintaining good oral hygiene could be critical in limiting COVID-19 disease. Brushing the teeth frequently and regularly with widely available amphiphilic detergent, sodium lauryl sulfate (SLS)-based toothpastes could help in preventing the spread of SARS-CoV-2. We proposed a community survey-based methodology followed by an in vitro biochemical strategy to test the virucidal potentiality of SLS, an amphiphilic detergent found in these toothpastes. Through biomolecular structure and docking analysis using models of spike protein and SLS, we showed a possible molecular mechanism of action for SLS-enabled viral particle inactivation.

Discovery of SDS-347 as a specific peptide competitive inhibitor of G9a with promising anti-cancer potential.

BACKGROUND: G9a is a histone H3K9 methyltransferase enzyme found highly upregulated in many cancers. H3 binds to the rigid I-SET domain and the cofactor, S-adenosyl methionine, binds to the flexible post-SET domain of G9a. Inhibition of G9a is known to inhibit the growth of cancer cell-lines. METHODS: Recombinant G9a and H3 were used to develop radioisotope-based inhibitor screening assay. The identified inhibitor was evaluated for isoform selectivity. The mode of enzymatic inhibition was studied by enzymatic assays and bioinformatics. Anti-proliferative activity of the inhibitor was studied in cancer cell lines by utilizing MTT assay. The mechanism of cell death was studied by western blotting and microscopy. RESULTS: We developed a robust G9a inhibitor screening assay that led to the discovery of SDS-347 as a potent G9a inhibitor with IC50 of 3.06 µM. It was shown to reduce the levels of H3K9me2 in cell-based assay. The inhibitor was found to be peptide competitive and highly specific as it did not show any significant inhibition of other histone methyltransferases and DNA methyltransferase. Docking studies showed that SDS-347 could form direct bonding interaction with Asp1088 of the peptide-binding site. SDS-347 showed anti-proliferative effect against various cancer cell lines especially the K562 cells. Our data suggested that SDS-347 mediated antiproliferative action via ROS generation, induction of autophagy and apoptosis. CONCLUSION: Overall, the findings of the current study include development of a new G9a inhibitor screening assay and identification of SDS-347, as a novel, peptide competitive and highly specific G9a inhibitor with promising anticancer potential.

Updates on drug designing approach through computational strategies: a review.

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

An extensive survey of technological applications in drug discovery and development, encompassing offline and online approaches, is presented in this review. The span of research issues that can be tackled using these advances is vast, opening new horizons for future innovations. The article is designed to incite further research investments into drug development procedures and bridge existing research voids by outlining multiple pharmaceutical products that resulted from employing systematic computational methodologies.

Drug repositioning to discover novel ornithine decarboxylase inhibitors against visceral leishmaniasis.

Visceral leishmaniasis (VL) is caused by Leishmania donovani (Ld), and most cases occur in Brazil, East Africa, and India. The treatment for VL is limited and has many adverse effects. The development of safer and more efficacious drugs is urgently needed. Drug repurposing is one of the best processes to repurpose existing drugs. Ornithine decarboxylase (ODC) is an important target against L. donovani in the polyamine biosynthesis pathway. In this study, we have modeled the 3D structure of ODC and performed high-throughput virtual screening of 8630 ZINC database ligands against Leishmania donovani ornithine decarboxylase (Ld ODC), selecting 45 ligands based on their high binding score. It is further validated through molecular docking simulation and the selection of the top two lead molecules (ceftaroline fosamil and rimegepant) for Molecular Dynamics (MD) simulation, Density functional theory (DFT), and molecular mechanics generalized born surface area (MMGBSA) analysis. The results showed that the binding affinities of ceftaroline fosamil, and rimegepant are, respectively, -10.719 and 10.159 kcal/mol. The docking complexes of the two lead compounds, ceftaroline fosamil, and rimegepant, with the target ODC, were found stable during molecular dynamics simulations. Furthermore, the analysis of MMGBSA revealed that these compounds had a high binding free energy. The DFT analysis showed that the top lead molecules were more reactive than the standard drug (pentamidine). In-silico findings demonstrated that ceftaroline fosamil, and rimegepant might be recognized as potent antagonists against ODC for the treatment of VL.

PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features.

Pattern recognition receptors (PRRs) recognize distinct features on the surface of pathogens or damaged cells and play key roles in the innate immune system. PRRs are divided into various families, including Toll-like receptors, retinoic acid-inducible gene-I-like receptors, nucleotide oligomerization domain-like receptors, and C-type lectin receptors. As these are implicated in host health and several diseases, their accurate identification is indispensable for their functional characterization and targeted therapeutic approaches. Here, we construct PRR-HyPred, a novel two-layer hybrid framework in which the first layer predicts whether a given sequence is PRR or non-PRR using a support vector machine, and in the second, the predicted PRR sequence is assigned to a specific family using a random forest-based classifier. Based on a 10-fold cross-validation test, PRR-HyPred achieved 83.4 % accuracy in the first layer and 95 % in the second, with Matthew's correlation coefficient values of 0.639 and 0.816, respectively. This is the first study that can simultaneously predict and classify PRRs into specific families. PRR-HyPred is available as a web portal at https://procarb.org/PRRHyPred/. We hope that it could be a valuable tool for the large-scale prediction and classification of PRRs and subsequently facilitate future studies.

Molecular Interactions of Zyesami with the SARS-CoV-2 nsp10/nsp16 Protein Complex.

BACKGROUND: SARS-CoV-2 emerged in late 2019 and caused COVID-19. Patients treated with Zyesami were found to have a 3-fold decrease in respiratory failure and improved clinical outcomes. It was reported that Zyesami inhibits RNA replication of SARS-CoV-2, including several non-structural proteins essential in viral RNA replication. SARS-CoV-2 is a distinctive virus that requires nsp10 and nsp16 for its methyltransferases activity which is crucial for RNA stability and protein synthesis. OBJECTIVE: We aimed the in silico determination of inhibitory consequences of Zyesami on the SARS-CoV-2 nsp10/nsp16 complex. Targeting SARS-CoV-2 nsp10/ nsp16 protein complex may be used to develop a drug against COVID-19. METHODS: I-TASSER was used for secondary structure prediction of Zyesami. CABS-dock was used to model Zyesami with SARS-CoV-2 nsp16 interaction. The docked complex was visualized using PyMol. The quality of the docking model was checked by using ProQdock. RESULTS: The 3D structure of SARS-CoV 2, nsp10/nsp16 showed that essential interactions exist between nsp10 and nsp16. Significant contact areas of Zyesami exist across amino acid residues of nsp10; Asn40-Thr47, Val57-Pro59, Gly69-Ser72, Cys77-Pro84, Lys93-Tyr96. In addition, polar contacts between nsp16 and Zyesami are Asn299-Ser440, Val297-Asn443, Gly149-Tyr437, Gln159-Lys430, Asn178- Arg429, Ser146-Arg429, Ser146-Arg429, Lys147-Arg429, Asr221-Thr422, Lys183-Asp423, Lys183-Asp423, and Gln219-Asp423 the residues are shown of nsp16 and Zyesami respectively. CONCLUSION: The structural bioinformatics analyses have indicated the potential binding specificity of Zyesami and nsp16. Data predict how the initial binding of Zyesami with nsp10 and nsp16 may occur. Moreover, this binding could significantly inhibit the 2 -O-MTase activity of the SARSCoV nsp10/16 complex.

Differential gene expression analysis of RAGE-S100A6 complex for target selection and the design of novel inhibitors for anticancer drug discovery.

Receptor for advanced glycation end products (RAGE), a member of the immunoglobulin family, interactions with its ligands trigger downstream signaling and induce an inflammatory response linked to diabetes, inflammation, carcinogenesis, cardiovascular disease, and a variety of other human disorders. The interaction of RAGE and S100A6 has been associated with a variety of malignancies. For the control of RAGE-related illnesses, there is a great demand for more specialized drug options. To identify the most effective target for combating human malignancies associated with RAGE-S100A6 complex, we conducted single and differential gene expression analyses of S100A6 and RAGE, comparing normal and malignant tissues. Further, a structure-based virtual screening was conducted using the ZINC15 database. The chosen compounds were then subjected to a molecular docking investigation on the RAGE active site region, recognized by the various cancer-related RAGE ligands. An optimized RAGE structure was screened against a library of drug-like molecules. The screening results suggested that three promising compounds were presented as the top acceptable drug-like molecules with a high binding affinity at the RAGE V-domain catalytic region. We depicted that these compounds may be potential RAGE inhibitors and could be used to produce a successful medication against human cancer and other RAGE-related diseases based on their various assorted parameters, binding energy, hydrogen bonding, ADMET characteristics, etc. MD simulation on a time scale of 50 ns was used to test the stability of the RAGE-inhibitor complexes. Therefore, targeting RAGE and its ligands using these drug-like molecules may be an effective therapeutic approach.

Evaluating complete surface-associated and secretory proteome of Leishmania donovani for discovering novel vaccines and diagnostic targets.

The protozoa Leishmania donovani causes visceral leishmaniasis (kala-azar), the third most common vector-borne disease. The visceral organs, particularly the spleen, liver, and bone marrow, are affected by the disease. The lack of effective treatment regimens makes curing and eradicating the disease difficult. The availability of complete L. donovani genome/proteome data allows for the development of specific and efficient vaccine candidates using the reverse vaccinology method, while utilizing the unique sequential and structural features of potential antigenic proteins to induce protective T cell and B cell responses. Such shortlisted candidates may then be tested quickly for their efficacy in the laboratory and later in clinical settings. These antigens will also be useful for designing antigen-based next-generation sero-diagnostic assays. L. donovani's cell surface-associated proteins and secretory proteins are among the first interacting entities to be exposed to the host immune machinery. As a result, potential antigenic epitope peptides derived from these proteins could serve as competent vaccine components. We used a stepwise filtering-based in silico approach to identify the entire surface-associated and secretory proteome of L. donovani, which may provide rationally selected most exposed antigenic proteins. Our study identified 12 glycosylphosphatidylinositol-anchored proteins, 45 transmembrane helix-containing proteins, and 73 secretory proteins as potent antigens unique to L. donovani. In addition, we used immunoinformatics to identify B and T cell epitopes in them. Out of the shortlisted surface-associated and secretory proteome, 66 protein targets were found to have the most potential overlapping B cell and T cell epitopes (linear and conformational; MHC class I and MHC class II).