A systematic in-silico functional and structural analysis reveals deleterious nsSNPs in the Human CSF1R Gene

Document Type : Original article

Authors

1 Department of Bioinformatics, Goswami Ganesh Dutta Sanatan Dharma College, Sector 32, Chandigarh, 160030, India

2 Department of Biophysics, Panjab University, Sector-25, Chandigarh, 160014, India

Abstract

Colony Stimulating Factor-1 Receptor (CSF1R) is a tyrosine kinase transmembrane receptor that plays a vital role in innate immunity and neurogenesis and controls the differentiation and maintenance of most tissue-resident macrophages. CSF1R mutations have been linked with many neurodegenerative diseases. In this work, we aim to identify the functional and structural impact of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) mutations on CSF1R, which could help understand the consequences of these mutational changes. A consensus-based prediction approach was used to screen the missense SNPs using six in-silico tools: SIFT, PROVEAN, PMut, MutPred, MISSENSE 3D, and FATHMM. SNPs found to be deleterious by more than five out of six tools were subjected to further analysis, such as protein secondary structure and domain architecture analysis by PSIPRED and NCBI-CDD, respectively. Mutant models of highly deleterious SNPs were modeled using PyMol, followed by energy minimization and Root Mean Square Deviation (RMSD) analysis and molecular dynamic (MD) simulation by YASARA, TM-ALIGN, and WebGro, respectively. Out of 780 missense SNPs screened, we found the four most deleterious SNPs (L301S, A770P, I775N, and F849S) that decreased the protein stability because of their presence in the conserved regions of wild-type CSF1R. Structural and functional studies revealed that these mutations could disrupt the protein's core and surface interactions, leading to destabilization and functional impairment. Moreover, the mutated proteins exhibited enhanced conformational flexibility and instability, as confirmed by MD simulation analysis.

Keywords

Files

Share

How to cite

Statistics