<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Background</h3>
<p><italic>STAT3</italic>, a pleiotropic transcription factor, plays a critical role in the pathogenesis of autoimmunity, cancer, and many aspects of the immune system, as well as having a link with inflammatory bowel disease. Changes caused by non-synonymous single nucleotide polymorphisms (nsSNPs) have the potential to damage the protein's structure and function.</p>
</sec>
<sec><h3>Objective</h3>
<p>We identified disease susceptible single nucleotide polymorphisms (SNPs) in <italic>STAT3</italic> and predicted structural changes associated with mutants that disrupt normal protein–protein interactions using different computational algorithms.</p>
</sec>
<sec><h3>Methods</h3>
<p>Several <italic>in silico</italic> tools, such as SIFT, PolyPhen v2, PROVEAN, PhD-SNP, and SNPs&amp;GO, were used to determine nsSNPs of the <italic>STAT3</italic>. Further, the potentially deleterious SNPs were evaluated using I-Mutant, ConSurf, and other computational tools like DynaMut for structural prediction.</p>
</sec>
<sec><h3>Result</h3>
<p>417 nsSNPs of <italic>STAT3</italic> were identified, 6 of which are considered deleterious by <italic>in silico</italic> SNP prediction algorithms. Amino acid changes in V507F, R335W, E415K, K591M, F561Y, and Q32K were identified as the most deleterious nsSNPs based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein–protein interaction tools.</p>
</sec>
<sec><h3>Conclusion</h3>
<p>The in silico prediction analysis could be beneficial as a diagnostic tool for both genetic counseling and mutation confirmation. The 6 deleterious nsSNPs of <italic>STAT3</italic> may serve as potential targets for different proteomic studies, large population–based studies, diagnoses, and therapeutic interventions.</p>
</sec>
</abstract>

Background
STAT3, a pleiotropic transcription factor, plays a critical role in the pathogenesis of autoimmunity, cancer, and many aspects of the immune system, as well as having a link with inflammatory bowel disease. Changes caused by non-synonymous single nucleotide polymorphisms (nsSNPs) have the potential to damage the protein's structure and function.

Objective
We identified disease susceptible single nucleotide polymorphisms (SNPs) in STAT3 and predicted structural changes associated with mutants that disrupt normal protein–protein interactions using different computational algorithms.

Methods
Several in silico tools, such as SIFT, PolyPhen v2, PROVEAN, PhD-SNP, and SNPs&GO, were used to determine nsSNPs of the STAT3. Further, the potentially deleterious SNPs were evaluated using I-Mutant, ConSurf, and other computational tools like DynaMut for structural prediction.

Result
417 nsSNPs of STAT3 were identified, 6 of which are considered deleterious by in silico SNP prediction algorithms. Amino acid changes in V507F, R335W, E415K, K591M, F561Y, and Q32K were identified as the most deleterious nsSNPs based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein–protein interaction tools.

Conclusion
The in silico prediction analysis could be beneficial as a diagnostic tool for both genetic counseling and mutation confirmation. The 6 deleterious nsSNPs of STAT3 may serve as potential targets for different proteomic studies, large population–based studies, diagnoses, and therapeutic interventions.

In silico prediction of deleterious non-synonymous SNPs in <italic>STAT3</italic>

Asian Biomedicine

This work is licensed under the Creative Commons Attribution 4.0 International License.

In silico prediction of deleterious non-synonymous SNPs in 

Background
STAT3, a pleiotropic transcription factor, plays a critical role in the pathogenesis of autoimmunity, cancer, and many aspects of the immune system,...

In silico prediction of deleterious non-synonymous SNPs in STAT3

Article Category: Original article

Online veröffentlicht: 18. Okt. 2023

Seitenbereich: 185 - 199

DOI: https://doi.org/10.2478/abm-2023-0059

Schlüsselwörter
deleterious, In silico, prediction, single nucleotide polymorphism,

© 2023 Athira Ajith et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

In silico prediction of deleterious non-synonymous SNPs in STAT3

Article Category: Original article

Online veröffentlicht: 18. Okt. 2023

Seitenbereich: 185 - 199

DOI: https://doi.org/10.2478/abm-2023-0059

Schlüsselwörterdeleterious, In silico, prediction, single nucleotide polymorphism,

© 2023 Athira Ajith et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
deleterious, In silico, prediction, single nucleotide polymorphism,