arXiv subjects

Merla Sudha

Publications and source records attributed to Merla Sudha.

3 recordsLinked to original sources

arXiv2026

Multi-ligand simultaneous docking of Carica papaya leaf phytochemicals, Carpaine and Rutin, reveals multi-mechanism inhibition of cancer proteins BCL-2 and WWP1

Cancer remains a major global health concern due to chemotherapy resistance and toxicity from high-dose treatments. To overcome these challenges, new therapeutic strategies targeting key proteins in cancer progression are essential. This study evaluates two phytochemicals, Carpaine (Car) and Rutin (Rut), from Carica papaya leaves, for their potential in enhancing cancer therapy by targeting B-cell lymphoma 2 (BCL-2) and WW domain-containing protein 1 (WWP1) proteins. We assessed their additive, allosteric, and synergistic effects using molecular docking, multi-ligand simultaneous docking (MLSD), molecular dynamics (MD) simulations, and MMPBSA analysis. Car and Rut showed an additive effect on BCL-2 by binding at distinct regions within the same pocket. MLSD revealed an improved binding affinity of -13.13 +/- 0.08 kcal/mol, compared with individual ligands or the commercial inhibitor Venetoclax. For WWP1, Car bound near the H-site and Rut near the Le-site, exhibiting an allosteric effect that increased Car's binding affinity in MLSD to -15.59 +/- 0.39 kcal/mol. Furthermore, Rut combined with bortezomib (Bort) demonstrated a synergistic interaction with WWP1. Binding energies were -7.64 +/- 0.156 kcal/mol for Bort, -10.26 +/- 0.07 kcal/mol for Rut, and -15.59 +/- 0.39 kcal/mol for MLSD, suggesting a more stable complex through synergy. These results suggest Car and Rut, particularly in combination with Bort, as promising candidates against cancer-related proteins BCL-2 and WWP1. Further experimental validation is warranted to explore their therapeutic potential.

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arXiv2026

Advancing Alzheimer's Disease Treatment: Synergistic Ligand Combinations Targeting BACE1 via Multi-Ligand Simultaneous Docking

Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory loss, cognitive decline, and behavioral changes, primarily affecting the elderly. Current treatments focus on symptom management as no therapies effectively halt or reverse disease progression. Newer therapies targeting BACE1, a key enzyme in amyloid plaque formation, have shown promise in clinical trials, though they have been limited by side effects and insufficient efficacy in slowing cognitive decline. This is the first study where multi-ligand simultaneous docking (MLSD) was employed to identify potential synergistic inhibitor combinations from among thousands of small molecules that could yield better results than the current phase-III inhibitors of BACE1. The recent phase III drugs such as Atabecestat, Elenbecestat, Lanabecestat, and Verubecestat were considered to be standards for comparison in this study. A library of small molecules with known IC50 values against BACE1 was filtered for flagging Pan-Assay Interference (PAINS) and "Brenk" compounds. Single-ligand docking revealed individual ligand interactions with the protein, forming the basis for creating ligand pairs tested in MLSD. Among these combinations, CHEMBL4078427 and CHEMBL3656158, CHEMBL4078427 and CHEMBL3695732, Verubecestat and CHEMBL3656158, and CHEMBL4078427 and Lanabecestat demonstrated excellent binding affinity of -19.90 kcal/mol, -18.45 kcal/mol, -18.07 kcal/mol, and -17.67 kcal/mol with the protein, which is significantly greater than those of phase-III inhibitors, along with inter-ligand interactions indicative of a synergistic effect. These findings underscore the transformative potential of MLSD in identifying synergistic compound interactions, paving the way for novel combination therapies in the treatment of Alzheimer's disease.

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arXiv2025

Prediction of Novel CXCR7 Inhibitors Using QSAR Modeling and Validation via Molecular Docking

CXCR7, a G-protein-coupled chemokine receptor, has recently emerged as a key player in cancer progression, particularly in driving angiogenesis and metastasis. Despite its significance, currently, few effective inhibitors exist for targeting this receptor. In this study aimed to address this gap by developing a QSAR model to predict potential CXCR7 inhibitors, followed by validation through molecular docking. Using the Extra Trees classifier for QSAR modeling and employing a combination of physicochemical descriptors and molecular fingerprints, compounds were classified as active or inactive with a high accuracy of 0.85. The model could efficiently screen a large dataset, identifying several promising CXCR7 inhibitors. The predicted inhibitors were further validated through molecular docking studies, revealing strong binding affinities, with the best docking score of -12.24 +- 0.49 kcal/mol. Visualization of the docked structures in both 2D and 3D confirmed the interactions between the inhibitors and the CXCR7 receptor, reinforcing their potential efficacy.

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