In silico identification of the rare-coding pathogenic mutations and structural modeling of human NNAT gene associated with anorexia nervosa

Abstract

Purpose Increase susceptibility towards Anorexia nervosa (AN) was reported with reduced levels of NNAT gene. We sought to investigate the most pathogenic rare-coding missense mutations (nsSNPs) of NNAT and their potential damaging impact on protein function through transcript level sequence and structure based in silico approaches. Methods Gene sequence, SNPs of NNAT was retrieved from public databases and the putative post-translational modi cation (PTM) sites were analyzed. Distinctive in silico algorithms were recruited for transcript level SNPs analyses and to characterized high risk rare-coding nsSNPs along with their impact on protein stability function. Ab initio 3D-modeling of wild-type, alternate model prediction for most deleterious nsSNP, validation and recognition of druggable binding pockets were also performed. AN 3D therapeutic compounds followed rule of drug-likeness were docked with most pathogenic variant of NNAT to estimate the drugs' binding free energies.Results Conclusively, 10 transcripts (201-205) based nsSNPs from 3 rare-coding missense variants i.e., rs539681368, rs542858994, rs560845323 out of 840 exonic SNPs were identi ed. Transcript based functional impact analyses predicted rs539681368 (C30Y) from NNAT-204 as the high risk rare-coding pathogenic nsSNP, deviating protein functions. The 3D-modeling analysis of AN drugs' binding energies indicated lowest binding free energy (ΔG) and signi cant inhibition constant (K i ) with mutant models C30Y.Conclusions Mutant model (C30Y) exhibiting signi cant drug binding a nity and the commonest interaction observed at the acetylation site K59. Thus, based on these ndings, we concluded that the identi ed nsSNP may serve as potential targets for various studies, diagnosis and therapeutic interventions. Level of evidence No level of evidenceWhat Is Already Know On The Subject? Lombardi et al., 2019 and Ceccarrini et al in 2021 reported the role of con rmation of the involvement of the NNAT gene in the pathogenesis of AN. Further, its role in energy metabolic homeostasis has been clearly established through a vast spectrum of literature. What your study adds?Transcript level predictions of rare-coding high risk pathogenic human NNAT gene's SNPs by different in silico ML algorithms and their functional impact on protein stability. Characterizing the structural inference of rare-coding deleterious missense mutations and computing the molecular binding energies of most pathogenic 3D model of NNAT variant with therapeutic compounds of AN.

Circulating Neuronatin Levels Are Positively Associated with BMI and Body Fat Mass but Not with Psychological Parameters

“…This hypothesis is consistent with the findings of Lombardi et al [ 9 ], who linked single nucleotide polymorphisms (SNP) in the Nnat gene to increased susceptibility to AN. Subsequently, a recent in silico study [ 46 ] identified a Nnat SNP missense variant that strongly affects gene expression and is also hypothesized to interact with drugs commonly used in AN therapy, which could lead to implications for treatment efficacy. However, the extent to which the described variants lead to increased or decreased expression of Nnat gene products remains unclear.…”

In Silico Characterization of Withania coagulans Bioactive Compounds as Potential Inhibitors of Hydroxymethylglutaryl (HMG-CoA) Reductase of Mus musculus

“…DeepView/Swiss-PdbViewer was used for energy minimization of the obtained PDB structure of the Hmgcr gene . The model was validated through PROCHECK stereochemical assessment through an ERRAT quality factor, Ramachandran plot, Prosa-web (), and residual properties of the constructed model. , The dihedral angles φ against ψ of possible conformations of amino acids in protein structure had also been studied in the Ramachandran plot. The SAVES (Structure Validation Server: ) web server was used to know the probable structural errors and z -score. , PDBsum was also used to investigate the protein’s secondary structural details …”

“…sbg.ac.at/prosa.php), and residual properties of the constructed model. 23,24 The dihedral angles φ against ψ of possible conformations of amino acids in protein structure had also been studied in the Ramachandran plot. The SAVES (Structure Validation Server: https://saves.mbi.ucla.edu/) web server was used to know the probable structural errors and zscore.…”

“…The SAVES (Structure Validation Server: https://saves.mbi.ucla.edu/) web server was used to know the probable structural errors and zscore. 23,24 PDBsum was also used to investigate the protein's secondary structural details. 25 2.…”

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders

“…As an example, investigators recently sought to investigate the impact of mutations of the well‐recognized Neuronatin (NNAT) gene. Examination of the post‐translational modification (PTM) site was conducted by exploring the effects of structural modifications at NNAT's PTM‐acetylation site (K59) located in the cytoplasmic domain (Azmi et al, 2023). Using trained in silico ML algorithms and consensus classifiers, the research team showed that three structural modifications resulted in the highest structural destabilization and decreased molecular flexibility, which the authors posited could serve as a basis to explore further relationships of risk of AN onset.…”