Análise comparativa in sílico da proteína SMN funcional e SMNΔ7: contribuições para o entendimento da atrofia muscular espinhal (AME)

Abstract

Spinal Muscular Atrophy (SMA) is a genetic neurodegenerative disease caused by mutations or deletions in the SMN1 gene, resulting in insufficient production of the motor neuron survival protein (SMN), essential for the maintenance of motor neurons. Despite the existence of the paralogous SMN2 gene, this gene mostly produces a truncated isoform of the protein (SMNΔ7), which is unstable and functionally compromised. Therefore, this work proposed an in silico comparative analysis between the functional SMN isoforms (derived from SMN1) and SMNΔ7 (derived from SMN2), aiming to investigate the structural differences that contribute to the instability of the truncated isoform and its relationship with the pathophysiology of SMA. Bioinformatics tools such as UniProt and NCBI were used to obtain primary sequences, Clustal Omega for alignment, AlphaFold predicted models for structural modeling, PSIPRED for secondary structure prediction, and PyMOL for detailed three-dimensional analysis. The DISOPRED3, DMPFold, DomPred, KFERQ Finder, and cNLS Mapper tools were also applied to evaluate regions of structural disorder, functional domains, chaperone-mediated autophagy degradation signals, and nuclear transport signals. The results demonstrated important structural losses in the SMNΔ7 isoform, such as the absence of the C-terminal YG-box domain, relevant for protein oligomerization and stability, and the redistribution of proline residues with possible implications for recognition for proteasomal degradation. The analysis also revealed an unexpected difference in the final sequence of the truncated isoform, with the addition of four amino acids not present in the functional isoform, suggesting a change in the stop codon or reading frame. These alterations significantly contribute to the instability of SMNΔ7, reinforcing the structural role of the protein in the pathogenesis of SMA. We conclude that bioinformatics applied to protein structure studies constitutes a valuable tool for understanding the molecular mechanisms involved in genetic diseases, such as SMA, contributing relevant theoretical insights for the development of therapeutic strategies based on restoring SMN protein stability. However, it is important to emphasize that these computational approaches have inherent limitations, and their complementation with specific laboratory experiments is essential to validate and further develop the findings obtained in silico. The integrated use of computational modeling and experimental analysis therefore represents a promising and necessary path for a more robust elucidation of complex biological processes.