SNP2Structure: A Public and Versatile Resource for Mapping and Three-Dimensional Modeling of Missense SNPs on Human Protein Structures

• Main Authors: Difei Wang, Lei Song, Varun Singh, Shruti Rao, Lin An, Subha Madhavan
• Format: Article
• Language: English
• Published: Elsevier 2015-01-01
• Series: Computational and Structural Biotechnology Journal
• Subjects: Active site mutations; Protein structure; Molecular modeling; Disease causing SNPs; SNP database
• Online Access: http://www.sciencedirect.com/science/article/pii/S2001037015000434

One of the long-standing challenges in biology is to understand how non-synonymous single nucleotide polymorphisms (nsSNPs) change protein structure and further affect their function. While it is impractical to solve all the mutated protein structures experimentally, it is quite feasible to model the mutated structures in silico. Toward this goal, we built a publicly available structure database resource (SNP2Structure, https://apps.icbi.georgetown.edu/snp2structure) focusing on missense mutations, msSNP. Compared with web portals with similar aims, SNP2Structure has the following major advantages. First, our portal offers direct comparison of two related 3D structures. Second, the protein models include all interacting molecules in the original PDB structures, so users are able to determine regions of potential interaction changes when a protein mutation occurs. Third, the mutated structures are available to download locally for further structural and functional analysis. Fourth, we used Jsmol package to display the protein structure that has no system compatibility issue. SNP2Structure provides reliable, high quality mapping of nsSNPs to 3D protein structures enabling researchers to explore the likely functional impact of human disease-causing mutations.