Comparative multi-target computational profiling of ebelin lactone across cognition-associated proteins
- 1chool of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, India
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.
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BibTeX
@article{lokanadham2026comparative,
title = {{Comparative multi-target computational profiling of ebelin lactone across cognition-associated proteins}},
author = {Monica Lokanadham and Ramalingam Chidambaram},
journal = {Frontiers in Bioinformatics},
year = {2026},
month = oct,
doi = {10.3389/fbinf.2026.1952759},
url = {https://doi.org/10.3389/fbinf.2026.1952759},
note = {CC BY},
}
RIS
TY - JOUR
TI - Comparative multi-target computational profiling of ebelin lactone across cognition-associated proteins
AU - Monica Lokanadham
AU - Ramalingam Chidambaram
PY - 2026
DA - 2026/10/23
JO - Frontiers in Bioinformatics
DO - 10.3389/fbinf.2026.1952759
UR - https://doi.org/10.3389/fbinf.2026.1952759
AB - 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.
SN - 2673-7647
ER -