Network and pathway analyses highlighted SOD1 and TARDBP as key nodes in ALS-related mechanisms, such as oxidative stress, RNA metabolism, proteostasis, and mitochondrial impairment, and network-based functions were combined.
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder involving mutations in the
SOD1
and
TARDBP
genes that significantly influence its development. Nonsynonymous SNPs in these genes can disrupt protein stability, folding, and regulatory functions, leading to the loss of motor neurons. This study performed an in silico approach to assess the functional and structural effects of nsSNPs in
SOD1
and
TARDBP
. A total of 5,964
SOD1
and 10,630
TARDBP
variants were retrieved from public databases, filtered for the coding region with a MAF ≥ 0.001, and prioritized using CADD. Multiple approaches, including pathogenicity prediction, stability analysis, structural modeling, post-translational modification assessment, and network-based functions, were combined. 12 nsSNPs per gene met the inclusion criteria. Notably,
SOD1
variants V15G (rs1202989817) and I19M (rs1182088847) were consistently predicted to be deleterious, showing decreased stability indicated by negative ΔΔG values and localized structural disruptions without global misfolding. Conversely,
TARDBP
variants G335D (rs80356729) and I222T (rs1570722030) suggested destabilization but yielded mixed predictions regarding the disease association. Network and pathway analyses highlighted
SOD1
and
TARDBP
as key nodes in ALS-related mechanisms, such as oxidative stress, RNA metabolism, proteostasis, and mitochondrial impairment. These findings prioritize structurally destabilizing variants with potential pathogenic relevance in ALS and provide a computational framework for downstream experimental validation of these variants.
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TARDBP
can cause amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP). Recently, we identified somatic
TARDBP
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A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.