Biology subjects

Murugan, N. A.

Publications and source records attributed to Murugan, N. A..

5 recordsLinked to original sources

bioRxiv2026

Sfh3 is a sterol-modulated PI/PI4P transfer protein that confers azole resistance in yeast by specifically mediating lanosterol/PI exchange

Lipid transfer proteins precisely distribute lipids within eukaryotic cells, supporting functions such as vesicular trafficking and cellular signaling. Here, we analyzed whether Sfh3 (Sec14 homolog 3) functions as a sterol/phosphatidylinositol (PI) exchanger to clarify its role in neutral lipid mobilization from lipid droplets and in azole resistance in yeast. Using real-time assays, we found that its ergosterol transfer capacity is not activated by PI and is inhibited by ergosterol biosynthesis intermediates, such as lanosterol. Next, we found that Sfh3 exhibits both PI and PI4P transfer activities, which sterols can modulate. Through kinetic, structure-function, and thermal stability analyses, we identified remarkable Sfh3 mutants and ranked Sfh3's relative affinity for its lipid ligands. This allowed us to show that Sfh3 performs selective lanosterol/PI exchange, provided it recognizes PI, in the presence of ergosterol, and that this activity is necessary for yeast resistance to miconazole. Collectively, our data show that Sfh3 is a sterol-regulated PI/PI4P transfer protein, potentially linking sterol metabolism and PI4P signaling during lipid droplet utilization. In azole-treated yeast, elevated lanosterol levels may shift Sfh3 into an exclusive PI/lanosterol exchange mode, promoting cellular detoxification and conferring drug resistance. By revealing unanticipated Sfh-mediated lipid transfer mechanisms, our study provides new insights into the Sec14 family.

biochemistry↗

bioRxiv2025

Chemical Dice Integrator (CDI): A Scalable Framework for Multimodal Molecular Representation Learning

The machine learning landscape for molecular property prediction is fragmented, with numerous Featurizers each capturing a narrow, specialized view of chemical structure. This heterogeneity forces a suboptimal choice of representation a priori, limiting model generalizability. We introduce the Chemical Dice Integrator (CDI), a hierarchical framework that unifies six orthogonal molecular representations, physicochemical (Mordred), topological (GROVER), visual (ImageMol), biological (Signaturizer), quantum-mechanical (MOPAC), and linguistic (ChemBERTa), into a single, coherent embedding. The framework consists of CDI-Basic, a two-tiered autoencoder that fuses these modalities, and CDI-Generalised, a Mamba State-Space Model (SSM) that learns a direct, efficient map from SMILES strings to the unified embedding space. Extensive benchmarking across 23 classification (171 tasks) and 10 regression datasets demonstrates that CDI embeddings consistently achieve superior predictive performance compared to individual Featurizers and standard feature aggregation methods. The CDI-Generalised model achieves this performance with exceptional computational efficiency, outperforming deep learning Featurizers in terms of speed and resource overhead. Furthermore, we demonstrate that the CDI embedding is chemically intuitive, allowing for the sensitive distinction of nuanced structural variants, such as chiral enantiomers and kekulized SMILES forms. By bridging multimodal chemical intelligence with scalable, sequence-based inference, CDI offers a strong foundation for molecular machine learning.

bioinformatics↗

bioRxiv2023

Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): An open access disease specific drug discovery portal

Computational drug discovery is intrinsically interdisciplinary and has to deal with the multifarious factors which are often dependent on the type of disease. Molecular Property Diagnostic Suite (MPDS) is a Galaxy based web portal which was conceived and developed as a disease specific web portal, originally developed for tuberculosis (MPDSTB). As specific computational tools are often required for a given disease, developing a disease specific web portal is highly desirable. This paper emphasises on the development of the customised web portal for COVID-19 infection and is referred to as MPDSCOVID-19. Expectedly, the MPDS suites of programs have modules which are essentially independent of a given disease, whereas some modules are specific to a particular disease. In the MPDSCOVID-19 portal, there are modules which are specific to COVID-19, and these are clubbed in SARS-COV-2 disease library. Further, the new additions and/or significant improvements were made to the disease independent modules, besides the addition of tools from galaxy toolshed. This manuscript provides a latest update on the disease independent modules of MPDS after almost 6 years, as well as provide the contemporary information and tool-shed necessary to engage in the drug discovery research of COVID-19. The disease independent modules include file format converter and descriptor calculation under the data processing module; QSAR, pharmacophore, scaffold analysis, active site analysis, docking, screening, drug repurposing tool, virtual screening, visualisation, sequence alignment, phylogenetic analysis under the data analysis module; and various machine learning packages, algorithms and in-house developed machine learning antiviral prediction model are available. The MPDS suite of programs are expected to bring a paradigm shift in computational drug discovery, especially in the academic community, guided through a transparent and open innovation approach. The MPDSCOVID-19 can be accessed at http://mpds.neist.res.in:8085.

bioinformatics↗

bioRxiv2023

Artificial Intelligence uncovers Evolutionarily Conserved Intracellular Allosteric Modulators of GPCR-Gα interface.

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for unbiased cavity identification, cavity-specific ligand design, synthesis, and validation across GPCR topology. Here, we developed Gcoupler, an AI-driven generalized computational toolkit that leverages an integrative approach combining de novo ligand design, statistical methods, Graph Neural Networks, and Bioactivity-based ligand prioritization for rationally predicting high-affinity ligands. Using Gcoupler, we interrogated intracellular metabolites that target and regulate the GPCR-G interface (Ste2p-Gpa1p), affecting pheromone-induced programmed cell death in yeast. Our computational analysis, complemented by experimental validations, including genetic screening, multi-omics, site-directed mutagenesis, biochemical assays, and physiological readouts, identified endogenous hydrophobic metabolites, notably sterols, as direct intracellular allosteric modulators of Ste2p. Molecular simulations coupled with biochemical signaling assessment in site-directed Ste2p mutants further confirmed that metabolites binding to GPCR-G obstruct downstream signaling, possibly via a cohesive effect. Finally, by utilizing isoproterenol-induced, GPCR-mediated human and neonatal rat cardiac hypertrophy models, we observed that elevated metabolite levels attenuate hypertrophic response, reinforcing the evolutionary relevance of this mechanism.

bioinformatics↗

bioRxiv2020

Naphthalene monoimide derivative ameliorates amyloid burden and cognitive decline in a transgenic mouse model of Alzheimer's disease

Alzheimers disease (AD) is a major neurodegenerative disorder and the leading cause of dementia worldwide. Predominantly, misfolding and aggregation of amyloid-{beta} (A{beta}) peptides associated with multifaceted toxicity is the neuropathological hallmark of AD pathogenesis and thus, primary therapeutic target to ameliorate neuronal toxicity and cognitive deficits. Herein, we report the design, synthesis and evaluation of small molecule inhibitors with naphthalene monoimide scaffold to ameliorate in vitro and in vivo amyloid induced neurotoxicity. The detailed studies established TGR63 as the lead candidate to rescue neuronal cells from amyloid toxicity. The in silico studies showed disruption of salt bridges and intermolecular hydrogen bonding interactions within A{beta}42 fibrils by the interaction of TGR63, causing destabilization of A{beta}42 assembly. Remarkably, TGR63 treatment showed a significant reduction in cortical and hippocampal amyloid burden in the progressive stages of APP/PS1 AD mice brain. Various behavioral tests demonstrated rescued cognitive deficits. The excellent biocompatibility, BBB permeability and therapeutic efficacy to reduce amyloid burden make TGR63 a promising candidate for the treatment of AD.

neuroscience↗