Novel computational deep learning strategy for neuroprotection identification reveals unique set of nicotine analogs as potential therapeutic compounds against Parkinson’s disease

Abstract

AbstractDopaminergic replacement has been used for Parkinson’s Disease (PD) treatment with positive effects on motor symptomatology but with low effects over disease progression and prevention. Different epidemiological studies have shown that nicotine consumption decreases PD prevalence through the activation of neuroprotective mechanisms. Nicotine-induced neuroprotection has been associated with the overstimulation of intracellular signaling pathways (SP) such as Phosphatidyl Inositol 3-kinase/Protein kinase-B (PI3K/AKT) through nicotinic acetylcholine receptors (e.g α7 nAChRs) and the over-expression of the anti-apoptotic gene Bcl-2. Considering its harmful effects (toxicity and dependency), the search for nicotine analogs with decreased secondary effects, but similar neuroprotective activity, remains a promissory field of study. In this work, a computational strategy integrating structural bioinformatics, signaling pathway (SP) manual reconstruction, and deep learning was performed to predict the potential neuroprotective activity of a series of 8 novel nicotine analogs over the behavior of PI3K/AKT. We performed a protein-ligand analysis between nicotine analogs and α7 nAChRs receptor using geometrical conformers, physicochemical characterization of the analogs and developed a manually curated neuroprotective dataset to analyze their potential activity. Additionally, we developed a predictive machine-learning model for neuroprotection in PD through the integration of Markov Chain Monte-Carlo transition matrix for the SP with synthetic training datasets of the physicochemical properties and structural dataset. Our model was able to predict the potential neuroprotective activity of seven new nicotine analogs based on the binomial Bcl-2 response regulated by the activation of PI3K/AKT. We present a new computational strategy to predict the pharmacological neuroprotective potential of nicotine analogs based on SP architecture, using deep learning and structural data. Our theoretical strategy can be further applied to the study new treatments related with SP deregulation and may ultimately offer new opportunities for therapeutic interventions in neurodegenerative diseases.Author SummaryParkinson’s disease is one of the most prevalent neurodegenerative diseases across population over age 50. Affecting controlled movements and non-motor symptoms, treatments for Parkinson prevention are indispensable to reduce patient’s population in the future. Epidemiological data provide evidence that nicotine have a neuroprotective effect decreasing Parkinson prevalence. By interacting with nicotine receptors in neurons and modulating signaling pathways expressing anti-apoptotic genes nicotine arise as a putative neuroprotective therapy. Nevertheless, toxicity and dependency prevent the use of nicotine as a suitable drug. Nicotine analogs, structurally similar compounds emerge as an alternative for Parkinson preventive treatment. In this sense we developed a quantitative strategy to predict the potential neuroprotective activity of nicotine analogs. Our model is the first approach to predict neuroprotection in the context of Parkinson and signaling pathways using machine learning and computational chemistry.