Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus – PubMed Black Hawk Supplements

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Alzheimer’s disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This…
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Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus - PubMed

Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus

Thana Sutthibutpong et al. ACS Omega. .

Abstract

Alzheimer’s disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion’s mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1

Overall workflow of this study. Multiple in silico techniques, (i.e., deep learning, molecular docking, and molecular dynamics simulations) were successively applied to identify potential AChE inhibitors and elucidate the mechanisms underlying the inhibition exerted by the compound candidates.

Figure 2
Figure 2

Binding energy scores estimated from molecular docking plotted against (a) the active probability score predicted by the deep learning models and (b) the logarithm of the active probability score (log P) for each of the 40 selected compounds.

Figure 3
Figure 3

Relative contributions of the van der Waals, electrostatics, polar solvation, apolar solvation, and entropy (−TΔS) terms to the total binding energy of two confirmed AChE inhibitors and two predicted AChE inhibitors.

Figure 4
Figure 4

Interaction analysis of the amino acid residues of AChE and the enzyme inhibitors (a) donepezil, (b) galanthamine, (c) erinacerin A, and (d) hericenone B generated by LigPlot 2.2. Red, pink, orange, and yellow dashed circles represent Subsite I (top region), Subsite II (outside region), Subsite III (inside region), and Subsite IV (bottom region), respectively.

Figure 5
Figure 5

(Left) Binding configurations of the inhibitors into different subsites of the active site gorge of AChE: subsite I (top region; red), subsite II (outside region; pink), subsite III (inside region; orange), subsite IV (bottom region; brown), and catalytic residues (green). (Right) Per-residue binding energy decomposition between the important AChE residues and (a) donepezil, (b) galanthamine, (c) erinacerin A, and (d) hericenone B.

Figure 6
Figure 6

Extracted atomic features plotted against the binding energy estimated by molecular docking calculations with the highest correlations: (a) symbol = C, (b) degree = 3, and (c) total number of hydrogens = 0.

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References

    1. Hyman B. T.; Phelps C. H.; Beach T. G.; Bigio E. H.; Cairns N. J.; Carrillo M. C.; Dickson D. W.; Duyckaerts C.; Frosch M. P.; Masliah E.; et al. National institute on aging-Alzheimer’s association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimer’s Dementia 2012, 8 (1), 1–13. 10.1016/j.jalz.2011.10.007. – DOI – PMC – PubMed
    1. Blennow K.; de Leon M. J.; Zetterberg H. Alzheimer’s disease. Lancet 2006, 368 (9533), 387–403. 10.1016/S0140-6736(06)69113-7. – DOI – PubMed
    1. Masters C. L.; Simms G.; Weinman N. A.; Multhaup G.; McDonald B. L.; Beyreuther K. Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc. Natl. Acad. Sci. U.S.A. 1985, 82 (12), 4245–4249. 10.1073/pnas.82.12.4245. – DOI – PMC – PubMed
    1. Hardy J.; Selkoe D. J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002, 297 (5580), 353–356. 10.1126/science.1072994. – DOI – PubMed
    1. Medeiros R.; Baglietto-Vargas D.; LaFerla F. M. The role of tau in Alzheimer’s disease and related disorders. CNS Neurosci. Ther. 2011, 17 (5), 514–524. 10.1111/j.1755-5949.2010.00177.x. – DOI – PMC – PubMed

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Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus – PubMed