In silico analysis of alpha‐synuclein protein variants and posttranslational modifications related to Parkinson's disease

Abstract

Parkinson's disease (PD) is among the most prevalent neurodegenerative disorders, affecting over 10 million people worldwide. The protein encoded by the SNCA gene, alpha‐synuclein (ASYN), is the major component of Lewy body (LB) aggregates, a histopathological hallmark of PD. Mutations and posttranslational modifications (PTMs) in ASYN are known to influence protein aggregation and LB formation, possibly playing a crucial role in PD pathogenesis. In this work, we applied computational methods to characterize the effects of missense mutations and PTMs on the structure and function of ASYN. Missense mutations in ASYN were compiled from the literature/databases and underwent a comprehensive predictive analysis. Phosphorylation and SUMOylation sites of ASYN were retrieved from databases and predicted by algorithms. ConSurf was used to estimate the evolutionary conservation of ASYN amino acids. Molecular dynamics (MD) simulations of ASYN wild‐type and variants A30G, A30P, A53T, and G51D were performed using the GROMACS package. Seventy‐seven missense mutations in ASYN were compiled. Although most mutations were not predicted to affect ASYN stability, aggregation propensity, amyloid formation, and chaperone binding, the analyzed mutations received relatively high rates of deleterious predictions and predominantly occurred at evolutionarily conserved sites within the protein. Moreover, our predictive analyses suggested that the following mutations may be possibly harmful to ASYN and, consequently, potential targets for future investigation: K6N, T22I, K34E, G36R, G36S, V37F, L38P, G41D, and K102E. The MD analyses pointed to remarkable flexibility and essential dynamics alterations at nearly all domains of the studied variants, which could lead to impaired contact between NAC and the C‐terminal domain triggering protein aggregation. These alterations may have functional implications for ASYN and provide important insight into the molecular mechanism of PD, supporting the design of future biomedical research and improvements in existing therapies for the disease.

An In Silico Analysis of Genetic Variants and Structural Modeling of the Human Frataxin Protein in Friedreich’s Ataxia

“…As shown in Figure 8 , wild-type FXN and I154F ( Figure 8 A) and W155R ( Figure 8 B) variants exhibited stable behavior after an initial period of structural instability. After approximately 150 ns, a plateau is observed in the RMSD values, suggesting that these proteins are in a stable average conformation, indicating that the system has reached equilibrium [ 11 , 32 , 53 ].…”

“…Analyze the structural displacement of an amino acid with its average position considering the vibrations caused by temperature. Each amino acid of the FXN protein had its value calculated and subsequently projected onto the surface of the protein structure, thus providing a three-dimensional representation of the protein's structural flexibility [11]. Consistent with the RMSF analysis, the B factor is used to describe the flexibility of protein residues, being used during the MD simulation to analyze the flexibility of its residues.…”

“…These processes are highly dependent on protein flexibility, which allows proteins to acquire their functional three-dimensional structures. Changes in protein flexibility can affect the efficiency and precision of these processes, leading to misfolded or dysfunctional proteins [11]. During the protein maturation process, proteins undergo a series of coordinated events, including folding, post-translational modification, and cofactor assembly.…”

“…These simulations help prioritize potentially deleterious mutations for future wet-bench investigations. Furthermore, in silico prediction helps in the study of mutations associated with diseases, guiding the development of more effective drugs for treatment and improving the understanding of the molecular mechanisms underlying associated pathologies [ 11 , 12 , 13 , 14 ].…”

“…To overcome these limitations, we implemented several strategies that were effective within the frameworks previously established by our research group [ 11 , 12 , 23 ]. These strategies include using robust algorithms, integrating diverse modeling approaches, and comparing the outcomes with existing experimental data [ 24 , 25 ].…”