{
  "abstract": "BackgroundEbelin lactone (EL) is a bacoside-related triterpenoid derivative produced through acid hydrolysis and has previously shown binding affinity toward the M1 muscarinic and 5-HT2A receptors. However, its structural compatibility with proteins involved in synaptic plasticity, neuronal proteostasis, mitochondrial stress responses, and neurite remodeling has not been systematically evaluated.MethodsWe evaluated EL against proteins associated with cognition: Alpha-actinin-4 (ACTN4), Brain-derived neurotrophic factor (BDNF), Complexin-1 (CPLX1), Heat shock protein 72 (HSP70-1), Heat shock protein family member 9 (HSPA9), and Neuritin (NRN1). The workflow integrated ADMET-AI predictions, blind docking using GNINA with automated search space generation and Vina-based pose scoring, 100-ns GROMACS molecular dynamics for five selected complexes and their corresponding apo forms, trajectory analyses, principal component-based free-energy landscape analysis, and MM/GBSA binding-energy estimation with per-residue energy decomposition.ResultsHSPA9 showed the most favorable docking score of (−9.13) kcal mol−1, followed by ACTN4 (−8.65), BDNF (−8.57), HSP70-1 (−8.19), NRN1 (−7.87), and CPLX1 (−7.00). CPLX1 was not advanced to MD simulations because it had a small, predicted pocket and the least favorable score. Throughout the 100-ns MD simulations, the ACTN4 fragment had the lowest mean protein RMSD (0.163 ± 0.021 nm). In contrast, the full precursor model of BDNF showed the highest mean protein RMSD of 2.125 ± 0.388 nm. Comparison with the corresponding apo trajectories showed target-dependent changes, with NRN1–EL exhibiting lower mean RMSD and RMSF than apo NRN1, although its Rg and SASA values were higher. NRN1 exhibited the highest mean protein–ligand hydrogen-bond count (0.575 ± 0.501) and the most negative MM/GBSA binding-energy estimate (−39.21 ± 3.24 kcal mol−1), followed by HSPA9 (−37.93 ± 2.60) and BDNF (−33.83 ± 3.49). ADMET results indicated high lipophilicity (logP 7.018), low aqueous solubility, and mixed permeability-related signals.ConclusionBased on the integrated findings, NRN1 emerged as the overall most promising computational candidate for experimental follow-up. These findings require experimental validation.",
  "abstract_shown": true,
  "affiliations": [
    "chool of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, India"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Monica Lokanadham"
    },
    {
      "affs": [
        0
      ],
      "name": "Ramalingam Chidambaram"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dsm45wg8ns0js.bib",
  "cited_by": 0,
  "doi": "10.3389/fbinf.2026.1952759",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fbinf.2026.1952759/full",
  "id": "dsm45wg8ns0js",
  "issns": [
    "2673-7647"
  ],
  "landing_url": "https://doi.org/10.3389/fbinf.2026.1952759",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7223306381",
  "pdf_url": "https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1952759/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/dsm45wg8ns0js.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:47Z",
      "native_id": "78567af0732f448e9bd4261dd268e76f",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fbinf.2026.1952759"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "journal-openalex:1312|Genomics and Phylogenetic Studies",
      "subfield": "molecular-biology"
    },
    {
      "field": "medicine",
      "origin": "lcc:R858-859.7",
      "subfield": "general"
    }
  ],
  "title": "Comparative multi-target computational profiling of ebelin lactone across cognition-associated proteins",
  "title_text": "Comparative multi-target computational profiling of ebelin lactone across cognition-associated proteins",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:47Z",
  "url": "https://research.draconitic.com/paper/dsm45wg8ns0js",
  "venue": "Frontiers in Bioinformatics",
  "versions": []
}