Biology subjects

Niranjan, V.

Publications and source records attributed to Niranjan, V..

6 recordsLinked to original sources

bioRxiv2024

Discovery of natural compounds as novel FMS-like tyrosine kinase 3 (FLT3) therapeutic inhibitors for the treatment of acute myeloid leukemia: an in silico approach

FLT3 mutations, observed in approximately 30-35% of Acute Myeloid Leukemia (AML) cases, drive leukemic proliferation and survival pathways, presenting a significant challenge in clinical management. To address this therapeutic need, we employed a comprehensive computational approach integrating pharmacophore screening, molecular docking, ADMET analysis, and molecular dynamics simulations to identify potent inhibitors targeting FLT3. Utilizing ligand-based pharmacophore models generated from experimentally proven FLT3 inhibitors from BindingDB, we screened over 400,000 natural compounds from the COCONUT database. Hits identified through pharmacophore screening underwent further evaluation via Lipinski and Golden triangle criteria to ensure drug-like properties. Molecular docking against the FLT3 receptor, combined with ADMET analyses, facilitated the prioritization of lead compounds. Subsequently, three promising candidates were subjected to molecular dynamics simulations to assess binding stability. Our findings reveal three top- performing compounds, demonstrating robust and stable binding affinity and favorable ADMET characteristics. These compounds hold promise as potential scaffolds or leads for developing novel FLT3 inhibitors in AML therapy.

bioinformatics↗

bioRxiv2024

In silico Exploration Natural Compounds for the Discovery of Novel DNMT3A Inhibitors as Potential Therapeutic Agents for Acute Myeloid Leukemia

Aberrant DNA methylation, a hallmark of acute myeloid leukemia (AML), is catalyzed by DNA methyltransferase 3A (DNMT3A). Approximately 20-30% of AML patients harbor DNMT3A mutations, leading to disrupted DNA methylation patterns and leukemogenesis. To identify potential therapeutic interventions, this study employed computational drug discovery. A pharmacophore model was constructed and utilized to screen a natural product database, yielding a set of promising compounds. Subsequent molecular docking, MM-GBSA calculations, and ADMET profiling identified two compounds, CNP0375130 and CNP0256178, as potential DNMT3A inhibitors. These compounds exhibited favorable binding affinities and demonstrated desirable drug-like properties. Molecular dynamics simulations confirmed stable protein-ligand interactions. These findings suggest that CNP0375130 and CNP0256178 may serve as promising lead compounds for the development of novel anti-leukemic therapies targeting DNMT3A, and contribute to the ongoing efforts to develop targeted therapies for leukemia.

bioinformatics↗

bioRxiv2024

Metagenomics to Metabolomics: Integrating Genomic Insights for Mulberry Crop Protection and Enhancement

Indian Mulberry (Morus indica) is vital in sericulture, with their leaves serving as the primary food source for silkworms. The soil microbiome surrounding mulberry trees plays a pivotal role in nutrient cycling and ecosystem functioning. This study utilizes metagenomic analysis to explore the taxonomic diversity and functional potential of microbial communities in mulberry soil, particularly focusing on the rhizosphere. Key bacterial species such as Pseudomonas, Frankia, Azosipirulum are identified, highlighting their importance in mulberry health and disease dynamics. The abundance distribution of these bacterial populations reveals significant trends, offering insights into mulberry agroecosystem microbial ecology. Understanding garden soil-derived microbial consortia provides a foundation for exploring their role in nutrient cycling and plant health. The study reveals the intricate web of interactions between mulberry and their surrounding soil microbiota and identification of metabolites. Leveraging high-throughput sequencing and bioinformatics, the research identifies potential metabolites as biofertilizers and biopesticides, aiming to improve agricultural sustainability. The findings underscore the critical role of soil microbes in maintaining soil fertility, supporting plant health, and enhancing ecosystem resilience. Despite limitations and gaps, the study contributes to advancing eco-friendly agricultural practices and promoting soil health in mulberry cultivation. Ultimately, the research transcends the laboratory, resonating with stakeholders as it unravels the genetic blueprints of soil life and sows seeds of sustainable progress.

genomics↗

bioRxiv2024

Targeting the G-quadruplex structure in the hTERT promoter: In silico screening of phytocompounds and replica exchange molecular dynamics simulations.

Telomerase activity plays a crucial role in maintaining telomere length and cellular immortality, making it an attractive target for cancer therapy. The human telomerase reverse transcriptase (hTERT) promoter contains a G-rich region that can form G-quadruplex (G4) structures, which have been shown to regulate hTERT expression. In this study, we used in silico screening and molecular dynamics simulations to identify phytocompounds that can stabilize the G4 structure in the hTERT promoter. We performed shape-based and pharmacophore-based screening of a phytochemical database and identified two lead compounds with assistance from oleanolic acid and maslinic acid as controls which showed in vitro telomerase activity. Molecular docking and replica exchange molecular dynamics simulations for a temperature profile of 300K to 350K were used to evaluate the binding affinity and stability of these compounds with two different G4 conformations in the hTERT promoter. Our results suggest that astragaloside-1 can stabilize the parallel-stranded G4 conformation (2kze) in the hTERT promoter, while novel compounds may be required to stabilize the intramolecular G4 conformation (2kzd). Our study highlights the potential of in silico screening and molecular dynamics simulations in identifying lead compounds for targeting G4 structures.

bioinformatics↗

bioRxiv2022

Identification of Banana heat responsive long non-coding RNAs and their gene expression analysis.

Identification and characterization of long non-coding RNAs (lncRNAs) in the last decade has attained great attention because of their importance in gene regulatory functions in response to various plant stresses. Rising temperature is a potential threat to the agriculture world. Banana being an important economic crop, identification and characterization of genes and RNAs that regulate high temperature stress is imperative. As the prediction of lncRNAs in response to heat stress in banana is largely unknown, the present study was focused to identify the heat stress responsive lncRNA in banana (DH Pahang). Perl script was written to identify the novel transcripts, using the work flow. StringTie and CPC softwares were used and a total of 363 novel HS-lncRNA were identified in banana. Further, lncRNA were classified as 288 lincRNAs, 71 antisense LncRNA, 5 sense lncRNAs. The functional classification was done and transcripts were broadly classified into molecular function, cellular components and biological processes. Differential expression of lncRNA showed the varied patterns at different stages of heat stress. Finally, qPCR results confirmed DGE expression pattern of lncRNAs. Further, the Cytoscape analysis was performed which showed protein coding genes involved in membrane integrity and other signal transduction pathways. Taken together, these findings expand our understanding of lncRNAs as ubiquitous regulators under heat stress conditions in banana.

molecular biology↗

bioRxiv2021

Re-profiling of natural inhibitor via combinatorial drug screening: Brefeldin A variant design as an effective antagonist leading to EPAC2 structure modification and antibody design for identification

Drug discovery can be impactful with re-purposing and combinatorial strategies leading to potential pharmaceutical outputs. Epac2 has been a target of interest for various physiological conditions where suppression is inevitable to achieve desired therapeutic effect. Epac isoforms inhibition is crucial in vascular functions to prevent chronic inflammation leading to hypertension and myocardial infarction. An attempt utilizing brefeldin A, a natural inhibitor was subjected to substitution of 3 side chain groups with 43 fragments via combinatorial strategy. This resulted in generating a library of 79507 brefeldin A variants. High throughput virtual screening yielded 68,043 variants followed by precision docking providing 117 lead like brefeldin A variants. The best docked variant (3-((1R,2E,6R,10E,11aS,13S,14A)-6-(methylsulfonamido)-13-(3-methylureido)-4-oxo-4,6,7,8,9,11a,12,13,14,14a-decahydro-1H-cyclopenta[f][1]oxacyclotridecin-1-yl)-2,3-dihydro-1Himidazol-1-ium) has an increased binding efficiency of -10.841 kcal/mol. Simulation studies up to 200ns of complex lead to re-orientation of target tertiary structure resulted in RMSD change of 30.221 [A], suggesting the epac2 structure modification leading to unavailability of RAS-GEF domain and its interaction with Rap1b. A single domain antibody was designed to bind specifically to re-structured epac2 for potential identification over the native target structure. The resulting Brefeldin variant can be potentially labelled as a most effective antagonist against epac2 which induces theoretically irreversible structural re-conformation. This study also provides a robust in-silico workflow for searching of chemical space, generating and screening of combination libraries and the efficient utilization of known inhibitor.

bioinformatics↗