ABSTRACT
Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-ß (Aß), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.
ABSTRACT
BACKGROUND: Huntington's disease is a rare neurodegenerative illness of the central nervous system that is inherited in an autosomal dominant pattern. Mutant huntingtin protein is produced as a result of enlargement of CAG repeat in the N-terminal of the polyglutamine tract. AIM OF THE STUDY: Herein, we aim to investigate the mutations and their effects on the HTT gene and its genetic variants. Additionally, the protein-protein interaction of HTT with other proteins and receptor-ligand interaction with the three-dimensional structure of huntingtin protein were identified. METHODS: A comprehensive analysis of the HTT interactome and protein-ligand interaction has been carried out to provide a global picture of structure-function analysis of huntingtin protein. Mutations were analyzed and mutation verification tools were used to check the effect of mutation on protein function. RESULTS: The results showed, mutations in a single gene are not only responsible for causing a particular disease but may also cause other hereditary disorders as well. Moreover, the modification at the nucleotide level also cause the change in the specific amino acid which may disrupt the function of HTT and its interacting proteins contributing in disease pathogenesis. Furthermore, the interaction between MECP2 and BDNF lowers the rate of transcriptional activity. Molecular docking further confirmed the strong interaction between MECP2 and BDNF with highest affinity. Amino acid residues of the HTT protein, involved in the interaction with tetrabenazine were N912, Y890, G2385, and V2320. These findings proved, tetrabenazine as one of the potential therapeutic agent for treatment of Huntington's disease. CONCLUSION: These results give further insights into the genetics of Huntington's disease for a better understanding of disease models which will be beneficial for the future therapeutic studies.
