Abstract
Parkinson’s is a debilitating neurodegenerative disorder, that greatly affects motor functions. It is characterized by the deposition of pathogenic amyloid form of alpha-synuclein protein, in tissues of brain, to cause its neurodegeneration. Effective therapeutics against this ruinous malady, is yet be formulated. Although, naturally occurring polyphenolic compounds have been known to exhibit disaggregation potency against alpha-synuclein aggregates, its mechanism behind disaggregation remains elusive yet. In the present study, through a robust feature selection pipeline we have elucidated the biochemical features of naturally occurring polyphenols that can be mathematically associated with its experimentally observed disaggregation effect against alpha-synuclein amyloid aggregates. Accordingly, 308 descriptors were computed for the polyphenols, out of which an iteratively increasing subset of various descriptors in various distinct combinations were taken in tandem to build and fit Multiple linear regression (MLR) models against their IC50 values. Approximately, 15000000 MLR models were contrived and evaluated for the feature selection process. Applying stringent criterion, an MLR model with six features: Largest Moment of Inertia of Mass, HOMO Energy, Sum Nucleophilic Reactivity Index of All C Atoms, Average Bond Length for an O Atom, Minimum Bond Length for a C-C Bond, and Average Bond Order for a C Atom, fitted against IC50 was elucidated with statistically significant R2 = 0.89, F stat = 38.29. The mathematical association postulated in this feature selection study between polyphenols’ aforementioned descriptors and its disaggregation potency can help researcher better perceive the sanative anti-aggregation nature of polyphenols and enable them develop effective therapeutics against Parkinson’s.
C. Gopalakrishnan and C. Xu—Contributed to the work equally.
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Acknowledgment
This study was supported by Science and technology innovation project of Shanxi province universities (2019L0683), Changzhi Medical College Startup Fund for PhD faculty (BS201922), Provincial university innovation and entrepreneurship training programs (2019403). And also, the authors would like to thank Vellore Institute of Technology (Deemed to be University) for providing “VIT SEED GRANT (VIT/SG/2020–21/43)” for carrying out this research work.
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Gopalakrishnan, C., Xu, C., Han, P., Ramalingam, R., Li, Z. (2021). Delineating QSAR Descriptors to Explore the Inherent Properties of Naturally Occurring Polyphenols, Responsible for Alpha-Synuclein Amyloid Disaggregation Scheming Towards Effective Therapeutics Against Parkinson’s Disorder. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_21
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