Decoding Alzheimer’s Molecular Signatures through Bioinformatics and AI/ML-Assisted Structure-Based Discovery of SIRT2 Inhibitors for Alzheimer’s Disease

Decoding Alzheimer’s Molecular Signatures through Bioinformatics and AI/ML-Assisted Structure-Based Discovery of SIRT2 Inhibitors for Alzheimer’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decoding Alzheimer’s Molecular Signatures through Bioinformatics and AI/ML-Assisted Structure-Based Discovery of SIRT2 Inhibitors for Alzheimer’s Disease Basavaraj Mallikarjunayya Vastrad, Shivaling Pattanashetti, Chanabasayya Vastrad, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9039869/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alzheimer’s disease (AD) is one of the neurodegenerative disorder with complicated pathogenesis. The present study aimed to explore key pathways and genes in AD pathogenesis, which could be potential targets for novel AD treatments. Differentially expressed genes (DEGs) of RNA-sequencing dataset (GSE276756) obtained from Gene Expression Omnibus (GEO) were identified using the DESeq2 R bioconductor tool. Gene ontology (GO) and pathway enrichment analyses were performed. Subsequently, a protein–protein interaction (PPI) network was constructed and analyzed as well as modules were isolated from PPI network to identify hub genes. Then, the microRNAs (miRNAs), transcription factors (TFs) and drug molecules in AD were screened out from the miRNet and NetworkAnalyst database. The PPI network, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were constructed by Cytoscape software. Hub genes were verified based on receiver operating characteristic (ROC) curve analysis. Finally, QSAR model development, machine learning-guided virtual screening and molecular docking study were performed for screening of novel drug molecule. A total of 958 DEGs were identified (479 up regulated and 479 down regulated genes), which were mainly enriched in terms of protein metabolic process, developmental process, aerobic respiration and respiratory electron transport, and signal transduction. Ten hub genes including GNAI1, RAC1, FANCL, NHLRC1, RNF181, PRKCA, SRC, EGFR, KMT2D and ZNRF3 were identified as potential hub genes in AD from the PPI network and its modules. MiRNAs and TFs including hsa-miR-449a, hsa-miR-129-1-3p, ELK1 and HOXA5 were identified as potential biomarkers in AD from the miRNA-hub gene regulatory network and TF-hub gene regulatory network. Isoflurophate and Phosphonotyrosine were identified as potential drugs for the treatment of AD from the DrugBank database. ROC analysis confirmed the diagnostic value of hub genes. QSAR modeling, machine learning-guided virtual screening, and molecular docking were used to identify potential inhibitors of Sirtuin 2 for AD treatment, The findings of this study provide insights into the molecular pathogenesis of AD and might provide a basis for the discovery of effective therapeutic modalities for AD. Bioinformatics Computational Biology Drug Discovery, Design, & Development Cognitive Neuroscience Alzheimer’s disease bioinformatics analysis hub genes pathway GEO database Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9039869","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601225289,"identity":"772f50e8-f2be-4ec8-b719-198c84f604cf","order_by":0,"name":"Basavaraj Mallikarjunayya Vastrad","email":"","orcid":"https://orcid.org/0000-0003-2202-7637","institution":"Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, 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The present study aimed to explore key pathways and genes in AD pathogenesis, which could be potential targets for novel AD treatments. Differentially expressed genes (DEGs) of RNA-sequencing dataset (GSE276756) obtained from Gene Expression Omnibus (GEO) were identified using the DESeq2 R bioconductor tool. Gene ontology (GO) and pathway enrichment analyses were performed. Subsequently, a protein–protein interaction (PPI) network was constructed and analyzed as well as modules were isolated from PPI network to identify hub genes. Then, the microRNAs (miRNAs), transcription factors (TFs) and drug molecules in AD were screened out from the miRNet and NetworkAnalyst database. The PPI network, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were constructed by Cytoscape software. Hub genes were verified based on receiver operating characteristic (ROC) curve analysis. 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