Computational analysis of non-synonymous SNPs in the human LCN2 gene

Abstract Background Lipocalin-2 (LCN2), a neutrophil gelatinase-associated protein, plays an important role in iron homeostasis, infection, and inflammation. Polymorphism in the LCN2 gene is linked to various diseases such as cardiovascular disease, renal damage, and colorectal and pancreatic cancer. Identifying deleterious functional non-synonymous SNPs in the LCN2 gene is crucial in understanding how these genetic variations affect its structure and function. Methods Several in silico tools such as SIFT, Polyphen-2, PROVEAN, PREDICT SNP, MAPP, and SNAP2 followed by I-MUTANT 2.0, MUpro, ConSurf, and NetsurfP-2.0, secondary structure of the protein by SOPMA and PSIPRED, while its interaction with other genes and proteins was analyzed using GeneMANIA and STRING, respectively, and AlphaFold for protein's 3D structure prediction. Results The study identified 6 potentially harmful nsSNPs (rs11556770, rs139418967, rs142623708, rs200107414, rs201365744, rs368926734) and their structure and function were analyzed using prediction tools. I-MUTANT 2.0 predicted an increase in stability with the nsSNPs rs139418967, while the other shows decrease in protein stability with the 6 nsSNPs (rs11556770, rs139418967, rs142623708, rs200107414, rs201365744, rs368926734) which was validated using MUpro. ConSurf identified the 6 high-risk nsSNPs to be in the conserved regions of the protein. The result showed that rs11556770, rs139418967, rs142623708, rs200107414, rs201365744, and rs368926734 were found to be highly conserved and the variant amino acids. According to NetsurfP-2.0 server, the result showed that rs11556770 (Q39H), rs139418967 (L6P), rs368926734 (Y135H) were predicted to be exposed and rs142623708 (M71I), rs200107414 (Y52C), rs368926734 (Y135) were buried. The PSIPRED server analysis indicated that the predominant secondary structure was a strand, with lesser occurrences of coil and helix. Conclusion Overall, the study identified detrimental nsSNPs of LCN2 using computational analysis which could be used for large population-based investigations and diagnosis.