Publications

Alzheimer's disease is a progressive neurodegenerative condition marked by memory loss, cognitive decline, and behavioral changes, with no disease-halting therapies currently available. Recent BACE1-targeting drugs show clinical promise but face limitations including side effects and insufficient efficacy. This research represents the first application of multi-ligand simultaneous docking (MLSD) to identify synergistic inhibitor combinations. Researchers screened 15,641 small molecules with known IC50 values against BACE1, yielding binding affinities ranging from -11.32 to +10.85 kcal/mol. Five compounds with affinities of -11 kcal/mol or better were tested via MLSD. Four tested combinations, including CHEMBL4078427 with CHEMBL3656158 and lanabecestat, demonstrated superior binding affinities (-19.90 to -17.67 kcal/mol) with inter-ligand interactions indicating synergy. Molecular dynamics simulations confirmed enhanced BACE1 inhibition with improved stability metrics compared to single-ligand approaches, suggesting novel combination therapies for Alzheimer's treatment.

Leishmania spp., the causative agents of leishmaniasis, exhibit complex evolved mechanisms that allow long-term survival within the host despite antileishmanial treatment. This ability is closely associated with the presence of persister-like parasite populations that are phenotypically distinct from actively replicating forms. These subpopulations are capable of reversible drug tolerance without genetic resistance, which allows them to tolerate drug exposure and evade immune mechanisms. Persister-like parasites have been reported in multiple clinical manifestations such as visceral, cutaneous, and post-kala-azar dermal leishmaniasis. These persister-like parasites undergo metabolic slowdown, altered gene expression, and increased stress tolerance. These parasite-intrinsic changes are reinforced by host-parasite interactions within immune-regulated niches, where cytokine signaling, macrophage deactivation, and tissue-specific environments limit effective parasite elimination. Several molecular pathways, such as stress-activated kinases and dormancy-associated regulators, including phosphatases and L. donovani Zeta-toxin (Ld_ζ1) proteins, contribute to the maintenance of this persistent state. This review summarizes the current understanding of Leishmania persistence by integrating mechanistic insights, experimental and clinical observations, methodological challenges, and emerging molecular approaches. It also discusses implications for drug discovery and the limitations of conventional therapies that fail to target nonreplicating forms. Understanding persistence as a biological survival strategy is essential for interpreting treatment failure, relapse, and ongoing transmission and paves the way for more targeted and effective therapeutic interventions.

Schistosomiasis, a neglected tropical disease caused by Schistosoma parasites, remains a major global health challenge. The Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) is essential for parasite redox balance and immune evasion, making it a key therapeutic target. This study employs predictive Quantitative Structure-Activity Relationship (QSAR) modeling to identify potential SmTGR inhibitors. Using deep learning, a robust QSAR model was developed and validated, achieving high predictive accuracy. The predicted novel inhibitors were further validated through molecular docking studies, which demonstrated strong binding affinities, with the highest docking score of -10.76 ± 0.01 kcal/mol. Visualization of the docked structures in both 2D and 3D confirmed similar interactions for the inhibitors and commercial drugs, further supporting their therapeutic effectiveness and the predictive ability of the model. This study demonstrates the potential of QSAR modeling in accelerating drug discovery, offering a promising avenue for developing novel therapeutics targeting SmTGR to improve schistosomiasis treatment.

Developing an effective medicine to combat cancer and elusive stem cells is crucial in the current scenario. Withaferin A and Garcinol, important phytoconstituents of Withania somnifera (Ashwagandha) and Garcinia indica (Kokum) respectively, known for their therapeutic efficiency, have been used for several decades for treating various disorders because of their anti-cancerous, anti-inflammatory and anti-invasive properties. This study investigates the potential of withaferin A and garcinol in inhibiting BCL-2 and AKT-1, crucial proteins contributing to cancer cell persistence by evading apoptosis, increased cell proliferation, and inflammation. Molecular docking techniques, including single docking and MLSD, were used to understand the binding interaction of the ligands with BCL-2 and AKT-1. MLSD highlighted inter-ligand interactions among withaferin A and garcinol against BCL-2, with a binding affinity of -11.88 ± 0.12 kcal/mol, surpassing the binding affinity of venetoclax (-9.73 ± 0.1 kcal/mol), a commercial inhibitor of BCL-2. For AKT-1, the binding affinity of withaferin A and garcinol (-13.74 ± 0.08 kcal/mol) surpassed the binding affinity of melatonin (-7.24 ± 0.06 kcal/mol), a commercial inhibitor of AKT-1. The MLSD results highlight the combined effects of garcinol and withaferin A, underscoring the importance of considering the interactions of bioactive compounds in the development of new medicines and strategies targeting cancer and elusive stem cells.

Leishmaniasis caused by Leishmania mexicana relies on Leishmania mexicana glucose transporter (LmGT) receptors, which play an important role in glucose and ribose uptake at different stages of the parasite's life cycle. Previous efforts to identify LmGT inhibitors have been primarily based on in vitro screening. However, this conventional method is limited by inefficiency, high cost, and lack of specificity, which leaves a significant gap in the development of targeted therapeutic candidates for LmGT. This study employs computational techniques to address this gap by developing a quantitative structure analysis relationship model, utilizing a support vector machine classifier to identify novel LmGt inhibitors. The QSAR model achieved an accuracy of 0.81 in differentiating active compounds. Molecular docking further validated the identified inhibitors, revealing strong binding affinities with a top score of -9.46. The docking analysis showed that the inhibitors formed multiple hydrogen bonds and occupied the same binding pockets as the Phase 3 drug candidate. The tested inhibitors were derived from natural sources, suggesting reduced side effects and improved biocompatibility. This combined approach demonstrates the power of computational models in accelerating drug discovery, with implications for more efficient and biocompatible therapies against Leishmania mexicana.

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. 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.

Endocrine-disrupting chemicals (EDCs) are compounds that can interfere with hormones and their receptors, affecting growth, reproduction, and other vital functions in humans and wildlife. Since laboratory tests are slow and costly, we created a computational screening method to predict which chemicals are likely to bind the estrogen receptor (ER), a key protein that responds to the hormone estrogen. We used a dataset of 224 compounds described by 2,944 molecular descriptors and fingerprints. These features were fed into a deep neural network (DNN) that included dropout and batch normalization to reduce overfitting and improve training stability. This QSAR model achieved 96.65% accuracy on training data and 91.30% on test data. For the active (ER-binding) class, the model produced a precision of 0.82, a recall of 0.88, and an ROC-AUC of 0.81. To validate predicted active structures, we performed molecular docking using the estrogen receptor structure (PDB ID: 5TOA). Several compounds predicted as active showed docking poses and interactions similar to Estradiol (the natural ligand), including expected hydrogen bonds and hydrophobic contacts. In short, this DNN-based QSAR plus docking workflow provides a fast, cost-effective way to detect chemicals that may act as EDCs by targeting the ER, supporting prioritized laboratory testing and improved environmental and biodiversity risk assessment.

Moringa oleifera, known for its medicinal properties, contains bioactive compounds such as polyphenols and flavonoids with diverse therapeutic potentials, including anti-cancer effects. This study investigates the efficacy of M. oleifera leaf phytochemicals in inhibiting BCL-2, a critical protein involved in cancer cell survival. For the first time, multi-ligand simultaneous docking (MLSD) has been employed to understand the anti-cancer properties of M. oleifera leaf extract. Molecular docking techniques, including single-ligand and MLSD, were used to assess binding interactions with BCL-2. Single-ligand docking revealed strong binding affinities for compounds such as niazinin, alpha carotene, hesperetin, apigenin, niaziminin B, and niazimicin A, with some compounds even surpassing Venetoclax, a commercial BCL-2 inhibitor. MLSD highlighted inter-ligand interactions among apigenin, hesperetin, and niazimicin A, exhibiting a binding affinity of -14.96 kcal/mol, indicating a synergistic effect. These results shed light on the potential synergistic effects of phytochemicals when using multi-ligand simultaneous docking, underscoring the importance of considering compound interactions in the development of therapeutic strategies.

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 over 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 demonstrated a synergistic interaction with WWP1, suggesting a more stable complex through synergy. These results suggest Car and Rut, particularly in combination with bortezomib, as promising candidates against cancer-related proteins BCL-2 and WWP1. Further experimental validation is warranted to explore their therapeutic potential.

India is the second-largest producer of peanuts, yielding over 10 million metric tonnes annually. Most are processed for oil, generating peanut meal, a protein-rich (40-55 wt%) byproduct. This study evaluates high-voltage electric discharge (HVED) with alkaline extraction (AE) as a sustainable method to enhance protein recovery. HVED uses high-voltage pulses to disrupt cellular structures, aiding protein release. Compared to AE (19.86%), microwave-assisted extraction (MAE, 33.62%), and ultrasound-assisted extraction (UAE, 48.51%), HVED-AE achieved the highest yield (89.86%) with lower energy input, optimized at 25 kV, 7.5 Ω, and 20 discharges. Protein integrity was confirmed by fluorescence spectral analysis. These findings demonstrate HVED-AE as a superior, eco-friendly extraction method supporting a circular economy. This aligns with the UN's Sustainable Development Goals (SDGs), including zero hunger (SDG 2), good health and well-being (SDG 3), industry, innovation, and infrastructure (SDG 9), and responsible consumption and production (SDG 12), promoting scalable plant-based protein recovery from agro-waste.

Efficient and sustainable protein extraction from agro-industrial by-products like peanut meal is crucial for maximizing their potential. This study introduces a novel, environmentally friendly technique known as High-Voltage Electrical Discharge (HVED) for protein extraction from peanut meal. Given the increasing demand for sustainable plant-based proteins, and with peanut meal being a significant global resource, green extraction methods are essential to minimize environmental impact. This study marks the first application of HVED for peanut meal protein extraction, offering a better alternative to other conventional methods. This method utilizes high-voltage electrical discharges to disrupt cell structures and liberate proteins without harsh chemicals or high temperatures. HVED pretreatment is followed by alkaline extraction (AE). This study optimizes AE parameters, such as pH and solid-to-solvent ratios, to enhance protein extraction efficiency, and compares the fluorescence properties of proteins extracted via AE only and with HVED pretreatment. Results showed that optimal conditions at pH 11 and a solid-to-solvent ratio of 1:25 produced the best protein extraction, with HVED pretreatment yielding a higher protein concentration than AE. Fluorescence spectroscopy revealed a bathochromic shift in both AE and HVED-treated samples, likely due to the alkaline medium. These findings highlight HVED's potential as an efficient and sustainable pretreatment method for protein extraction, promoting environmentally friendly processes.

Bioactive compounds have been utilised for their therapeutic effects in various biomedical applications for a long time. The growing global emphasis on health has driven a significant increase in demand for these compounds in recent years. However, using them in the conventional formats does not achieve the required bioavailability for the desired effects. Thus, there is a growing need for nanonizing bioactive compounds, as this significantly increases the surface area-to-volume ratio and enhances their pharmacological activities. This review aims to provide a comprehensive overview of the various techniques that can be used to prepare nanonized formulations of such compounds. This ranges from widely used techniques, such as wet-media milling and electrospinning, to relatively newer techniques, including supercritical fluid technology. Additionally, we have covered the various encapsulation layers that can be employed to protect these nanoformulations and facilitate the delivery of the nanomaterials. Delivery of such bioactive compounds can be achieved through invasive and non-invasive methods. After entering these compounds into the biological system, they are taken up by the cells via active or passive transport. However, despite these advancements, many challenges remain during the preparation of these formulations. During the preparation of nanomaterials, it thus becomes essential to address these challenges and carefully evaluate the different parameters before scaling up the manufacturing process.