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Computational modelling approaches as a potential platform to understand the molecular genetics association between Parkinson’s and Gaucher diseases

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Abstract

Gaucher’s disease (GD) is a genetic disorder in which glucocerebroside accumulates in cells and specific organs. It is broadly classified into type I, type II and type III. Patients with GD are at high risk of Parkinson’s disease (PD), and the clinical and pathological presentation of GD patients with PD is almost identical to idiopathic PD. Several experimental models like cell culture, animal models, and transgenic mice models were used to understand the molecular mechanism behind GD and PD association; however, such mechanism remains unclear. In this context, based on literature reports, we identified the most common mutations K198T, E326K, T369M, N370S, V394L, D409H, L444P, and R496H, in the Glucosylceramidase (GBA) protein that are known to cause GD1, and represent a risk of developing PD. However, to date, no computational analyses have designed to elucidate the potential functional role of GD mutations with increased risk of PD. The present computational pipeline allows us to understand the structural and functional significance of these GBA mutations with PD. Based on the published data, the most common and severe mutations were E326K, N370S, and L444P, which further selected for our computational analysis. PredictSNP and iStable servers predicted L444P mutant to be the most deleterious and responsible for the protein destabilization, followed by the N370S mutation. Further, we used the structural analysis and molecular dynamics approach to compare the most frequent deleterious mutations (N370S and L444P) with the mild mutation E326K. The structural analysis demonstrated that the location of E326K and N370S in the alpha helix region of the protein whereas the mutant L444P was in the starting region of the beta sheet, which might explain the predicted pathogenicity level and destabilization effect of the L444P mutant. Finally, Molecular Dynamics (MD) at 50 ns showed the highest deviation and fluctuation pattern in the L444P mutant compared to the two mutants E326K and N370S and the native protein. This was consistent with more loss of intramolecular hydrogen bonds and less compaction of the radius of gyration in the L444P mutant. The proposed study is anticipated to serve as a potential platform to understand the mechanism of the association between GD and PD, and might facilitate the process of drug discovery against both GD and PD.

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The authors acknowledge the management of Vellore Institute of Technology for the seed money and (BRAF) @ CDAC for providing the facilities required to perform this work.

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  1. School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    D. Thirumal Kumar & C. George Priya Doss

  2. College of Health Sciences, Department of Biomedical Sciences, Qatar University, Doha, Qatar

    Hend Ghasan Eldous, Zainab Alaa Mahgoub & Hatem Zayed

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Thirumal Kumar, D., Eldous, H.G., Mahgoub, Z.A. et al. Computational modelling approaches as a potential platform to understand the molecular genetics association between Parkinson’s and Gaucher diseases. Metab Brain Dis 33, 1835–1847 (2018). https://doi.org/10.1007/s11011-018-0286-3

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