Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes

Abstract

Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25–40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various in-silico tools. This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol (-7.56 kcal/mol), Hydroxyprogesterone caproate (-8.19 kcal/mol), Retosiban (-9.43 kcal/mol), Ritodrine (-7.39 kcal/mol) and Terbutaline (-6.87 kcal/mol). Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.

Analyzing molecular signatures in preeclampsia and fetal growth restriction: Identifying key genes, pathways, and therapeutic targets for preterm birth

“…Only eligible compounds were included and further subjected to docking analysis. According to the criteria previously reported by Azmi et al, in 2023, we selected different PTB drugs for reference and comparison (Azmi et al, 2023).…”

“…Furthermore, researchers have identified several candidate genes associated with PTB, given their involvement in diverse biological processes such as inflammation, immune response modulation, and uterine function, these biomarker genes play a crucial role in contributing to PTB. ( Wadon et al, 2020 ; Couceiro et al, 2021 ; Azmi et al, 2023 ). Variants of these genes may increase susceptibility to PTB.…”

“…The occurrence of PTB has many associated risks, and PE and IUGR have been reported as the main contributing consequences of PTB ( Wadon et al, 2020 ; Couceiro et al, 2021 ; Nowakowska et al, 2021 ; Azmi et al, 2023 ). To comprehensively understand the underlying mechanisms of conditions such as PTB-associated PE and IUGR, it is imperative to investigate the distinct expression patterns of gene biomarkers, whether upregulated or downregulated.…”

In-silico identification of deleterious non-synonymous SNPs of TBX1 gene: Functional and structural impact towards 22q11.2DS

“…The vast number of nsSNPs in the human genome necessitates an extensive investigation to understand their significance and potential associations with disease susceptibility [20]. In silico analysis employing streamlined bioinformatic tools has proven to be effective in investigating a large number of variants before experimental testing in vitro or in vivo assays [21][22][23][24]. These computational tools and databases offer a cost and labour-effective potential in exploring their implications for human health and disease.…”