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

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Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity Aims and scope Submit manuscript

Abstract

Purpose

Increased susceptibility towards anorexia nervosa (AN) was reported with reduced levels of neuronatin (NNAT) gene. We sought to investigate the most pathogenic rare-coding missense mutations, non-synonymous single-nucleotide polymorphisms (nsSNPs) of NNAT and their potential damaging impact on protein function through transcript level sequence and structure based in silico approaches.

Methods

Gene sequence, single nucleotide polymorphisms (SNPs) of NNAT was retrieved from public databases and the putative post-translational modification (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 that followed rule of drug-likeness were docked with most pathogenic variant of NNAT to estimate the drugs’ binding free energies.

Results

Conclusively, 10 transcript (201–205)-based nsSNPs from 3 rare-coding missense variants, i.e., rs539681368, rs542858994, rs560845323 out of 840 exonic SNPs were identified. 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 significant inhibition constant (Ki) with mutant models C30Y.

Conclusions

Mutant model (C30Y) exhibiting significant drug binding affinity and the commonest interaction observed at the acetylation site K59. Thus, based on these findings, we concluded that the identified nsSNP may serve as potential targets for various studies, diagnosis and therapeutic interventions.

Level of evidence

No level of evidence—open access bioinformatics research.

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Data availability

The data used in the article are given with the information from where the data were taken.

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Authors and Affiliations

  1. Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan

    Muhammad Bilal Azmi

  2. Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan

    Unaiza Naeem, Arisha Saleem & Areesha Jawed

  3. Department of Biochemistry, University of Karachi, Karachi, Pakistan

    Haroon Usman & Shamim Akhtar Qureshi

  4. Department of Biosciences, Mohammad Ali Jinnah University, Karachi, Pakistan

    M. Kamran Azim

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Contributions

MBA, UN, SAQ and MKA: conceived the study. UN, AS, AJ and HU: performed the literature search. UN, AS, AJ, HU and MBA: analyzed data. All authors contributed to interpretation of data, as well as drafting and approval of the manuscript.

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Correspondence to Muhammad Bilal Azmi.

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Azmi, M.B., Naeem, U., Saleem, A. et al. In silico identification of the rare-coding pathogenic mutations and structural modeling of human NNAT gene associated with anorexia nervosa. Eat Weight Disord 27, 2725–2744 (2022). https://doi.org/10.1007/s40519-022-01422-6

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