Biology subjects

Uttarkar, A.

Publications and source records attributed to Uttarkar, A..

4 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

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↗

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↗