Integrated Transcriptomic and Structural Prioritization of Sulforaphane Targets in Neurodegeneration: A Multi-Dataset In Silico Study
- 1Mugla Sitki Kocman University
Abstract
Sulforaphane (SFN), a natural isothiocyanate from cruciferous vegetables, exerts neuroprotective effects through mechanisms extending beyond canonical Nrf2/ARE activation. Here, we developed a multi-dataset computational framework integrating transcriptomic analysis, multi-criteria target prioritization, and structural docking evaluation. R- and S-SFN were docked against 693 prioritized targets using AutoDock Vina, followed by cross-validation, brain expression profiling, and molecular dynamics simulations. Four genes (IGSF9, KCNQ5, KCTD12, and NFIX) were dysregulated across all four datasets. Among 693 targets, 88 showed favorable predicted docking scores (deltaG less than or equal to -4.0 kcal/mol), with 65 (73.9%) exhibiting expression in at least one GTEx brain region. Independent cross-validation showed significant enrichment of prioritized targets in an SFN-responsive Alzheimer disease transcriptomic dataset (OR = 1.44, 95% CI: 1.08-1.89, p = 0.0104). Complementary disease gene set enrichment confirmed that 33% of strong hits (29/88) overlap established neurodegeneration gene sets across AD, PD, and ALS - including ACHE, target of FDA-approved anti-AD drugs - with highly significant enrichment across all three disease categories (OR range: 67-127, all p less than 0.001). R-SFN showed a modest but statistically detectable preference over S-SFN across the 88 strong hits (Wilcoxon p = 0.0081; paired t-test p = 0.0047). TMEM45A, GRIK2, and FABP3 emerged as putative neurodegeneration-relevant targets. Molecular dynamics simulations supported the structural stability of R-SFN poses for TMEM45A, GRIK2, and FABP3, while MM-GBSA estimated favorable binding energies for GRIK2 (deltaG_bind = -19.78 kcal/mol) and FABP3 (deltaG_bind = -14.49 kcal/mol). These findings generate testable hypotheses regarding previously underexplored molecular targets that may contribute to the neuroprotective actions of SFN.
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BibTeX
@misc{ugur2026integrated,
title = {{Integrated Transcriptomic and Structural Prioritization of Sulforaphane Targets in Neurodegeneration: A Multi-Dataset In Silico Study}},
author = {S. Ugur and T. Onal-Suzek},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.20.760417},
url = {https://www.biorxiv.org/content/10.64898/2026.10.20.760417v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - Integrated Transcriptomic and Structural Prioritization of Sulforaphane Targets in Neurodegeneration: A Multi-Dataset In Silico Study
AU - S. Ugur
AU - T. Onal-Suzek
PY - 2026
DA - 2026/10/20
PB - bioRxiv
DO - 10.64898/2026.10.20.760417
UR - https://www.biorxiv.org/content/10.64898/2026.10.20.760417v1
AB - Sulforaphane (SFN), a natural isothiocyanate from cruciferous vegetables, exerts neuroprotective effects through mechanisms extending beyond canonical Nrf2/ARE activation. Here, we developed a multi-dataset computational framework integrating transcriptomic analysis, multi-criteria target prioritization, and structural docking evaluation. R- and S-SFN were docked against 693 prioritized targets using AutoDock Vina, followed by cross-validation, brain expression profiling, and molecular dynamics simulations. Four genes (IGSF9, KCNQ5, KCTD12, and NFIX) were dysregulated across all four datasets. Among 693 targets, 88 showed favorable predicted docking scores (deltaG less than or equal to -4.0 kcal/mol), with 65 (73.9%) exhibiting expression in at least one GTEx brain region. Independent cross-validation showed significant enrichment of prioritized targets in an SFN-responsive Alzheimer disease transcriptomic dataset (OR = 1.44, 95% CI: 1.08-1.89, p = 0.0104). Complementary disease gene set enrichment confirmed that 33% of strong hits (29/88) overlap established neurodegeneration gene sets across AD, PD, and ALS - including ACHE, target of FDA-approved anti-AD drugs - with highly significant enrichment across all three disease categories (OR range: 67-127, all p less than 0.001). R-SFN showed a modest but statistically detectable preference over S-SFN across the 88 strong hits (Wilcoxon p = 0.0081; paired t-test p = 0.0047). TMEM45A, GRIK2, and FABP3 emerged as putative neurodegeneration-relevant targets. Molecular dynamics simulations supported the structural stability of R-SFN poses for TMEM45A, GRIK2, and FABP3, while MM-GBSA estimated favorable binding energies for GRIK2 (deltaG_bind = -19.78 kcal/mol) and FABP3 (deltaG_bind = -14.49 kcal/mol). These findings generate testable hypotheses regarding previously underexplored molecular targets that may contribute to the neuroprotective actions of SFN.
ER -