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Predicting the Impact of Deleterious Mutations in the Protein Kinase Domain of FGFR2 in the Context of Function, Structure, and Pathogenesis—a Bioinformatics Approach

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Abstract

Fibroblast growth factor receptor 2 (FGFR2) controls a wide range of biological functions by regulating the cellular proliferation, survival, migration and differentiation. A growing body of preclinical data demonstrated that deregulation of the FGFR signalling through genetic modification was observed in various types of cancers. However, the extent to which genetic modifications interfere with gene regulation and their involvement in cancer susceptibility remains largely unknown. In this work, we performed in silico profiling of harmful non-synonymous single nucleotide polymorphisms (SNPs) in the protein kinase domain of FGFR2. Tolerance index, position-specific independent count score, change in free energy score (ΔΔG), Eris and FoldX indicated that seven mutations were found to be deleterious and may alter the protein function and structure. Furthermore, based on physico-chemical properties, two mutations K659N and R747H were found to be most deleterious in protein kinase domain and taken for further structural analysis. Docking study showed a complete loss of binding affinity followed by interference in hydrogen bonding and surrounding residues due to K659N and R747H mutations. In order to elucidate the mechanism behind the impact of mutation that can generate a ripple effect throughout the protein structure and ultimately affect the function, in-depth molecular dynamics simulation and principal component analysis were performed. The obtained results indicate that K659N and R747H mutations have a distinct effect on the dynamic behaviour of FGFR2 protein. Our strategy may be helpful for understanding SNP effects on proteins with function and their role in human genetic diseases and for the development of novel pharmacological strategies.

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Abbreviations

SNP:

Single nucleotide polymorphism

nsSNPs:

Non-synonymous single nucleotide polymorphisms

GWAS:

Genome-wide association studies

aa:

Amino acid

AS:

Apert syndrome

FGFRs:

Fibroblast growth factor receptor

FGFR2:

Fibroblast growth factor receptor 2

KD4v:

Comprehensible Knowledge Discovery System for Missense Variant

MD:

Molecular dynamics

MSV3d:

Missense variant mapped to 3D structure

NCBI:

National Centre for Biological Information

OMIM:

Online Mendelian Inheritance in Man

PSIC:

Position-specific independent count

ΔΔG :

Change in free energy

SIFT:

Sorting Intolerant From Tolerant

PolyPhen 2.0:

Polymorphism Phenotyping V 2.0

PCA:

Principal component analysis

PDB:

Protein Data Bank

BLAST:

Basic local alignment search tool

SVM:

Support vector machine

ILP:

Induction logic programming

RMSD:

Root mean square deviation

RMSF:

Root mean square fluctuation

SASA:

Solvent accessibility surface area

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Acknowledgments

The authors take this opportunity to thank the management of Vellore Institute of Technology University for providing the facilities and encouragement to carry out this work.

Conflict of Interest

The authors declare that we do not have a conflict of interest.

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Authors and Affiliations

  1. Medical Biotechnology Division, School of Biosciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India

    George Priya Doss C & B. Rajith

  2. Department of Bio-informatics, School of Computer and Information Sciences, Galgotias University, Greater Noida, India

    Chiranjib Chakraborty

Authors
  1. George Priya Doss C
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  2. B. Rajith
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  3. Chiranjib Chakraborty
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Correspondence to George Priya Doss C.

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C, G.P.D., Rajith, B. & Chakraborty, C. Predicting the Impact of Deleterious Mutations in the Protein Kinase Domain of FGFR2 in the Context of Function, Structure, and Pathogenesis—a Bioinformatics Approach. Appl Biochem Biotechnol 170, 1853–1870 (2013). https://doi.org/10.1007/s12010-013-0315-y

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  • DOI: https://doi.org/10.1007/s12010-013-0315-y

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