Biology subjects

Uttarkar, A.

Publications and source records attributed to Uttarkar, A..

6 recordsLinked to original sources

bioRxiv2026

An inspectable Grover algorithm-based amplitude-amplification workflow for k-mer discovery and motif scanning with genomic coordinate traceability

Motif discovery and motif scanning remain core primitives in genomics, but both tasks face combinatorial growth as k-mer length increases and as mismatch-tolerant searches expand the candidate space. In this work, we present MotifQu, a Python package and command-line tool that maps two common motif-analysis workflows-(i) scanning a genome for a user-specified consensus motif and (ii) de novo discovery of recurrent k-mers above a user-defined count threshold-onto Grover-style amplitude amplification circuits executed reproducibly via Qiskit Aer statevector simulation. We evaluate MotifQu on the SARS-CoV-2 reference contig (29,903 bp) and S. cerevisiae S288C reference genome, where the classical census identifies strongly non-uniform motif frequencies dominated by A/T-rich 5-mers and returns explicit coordinate lists across the full genome span. We further execute the same logical 10-qubit search on IBM hardware showing that while end-to-end execution is feasible, transpilation-induced circuit depth and entangling-gate volume produce a diffuse measurement distribution whose probabilities remain close to the uniform 1/1024 scale, limiting observable amplitude concentration under current NISQ constraints. Finally, we demonstrate the full discovery workflow on the S. cerevisiae S288C reference genome (12,157,105 bp) to illustrate search results (ranked motifs, probability summaries, circuit diagrams, and position plots) and to highlight the regime where a large, marked set reduces the sharpness of Grover amplification.

biophysics↗

bioRxiv2026

Scalable Decoupling Contact and Configuration Spaces in a Quantum-Enabled Protein Folding Workflow

Near-term quantum devices remain constrained by limited qubit counts, noise, and shallow circuit depth, restricting their direct application to peptide folding. This work addresses this limitation through a hybrid quantum-classical framework that separates global contact-space exploration from local configuration-space refinement. The pipeline was evaluated on the p53 transactivation peptide. The peptide was encoded using discrete torsional states and Miyazawa-Jernigan contact potentials. Global conformational search was performed using simulated annealing, followed by fragment-level CVaR-based variational quantum eigensolver refinement. Overlapping fragments were stitched and reconstructed into three-dimensional backbone coordinates for structural comparison. Three independent simulated annealing restarts produced negative-energy conformers. The workflow identified a best global conformer at E = -3.70 MJ using simulated annealing. Fragment-level CVaR-VQE refinement generated 30 candidate conformers across three overlapping fragments, the best assembled conformer reaching E = -2.58 MJ after DP stitching and post-stitch refinement. In this trial, the classical SA solution outperformed the VQE-assembled structure and was selected as the final output. The final 65-atom model was successfully reconstructed, but full-backbone alignment against 1YCR yielded an RMSD of 4.65 angstrom, improving to 2.90 angstrom after trimmed alignment. Our work demonstrates a practical quantum-enabled peptide folding workflow for short, flexible peptides, but the present structural accuracy is insufficient for claims of native fold recovery. Its main value is as an auditable framework for testing NISQ-compatible folding strategies.

biophysics↗

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↗