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Reference: Ryan M., Diekhans M., Lien S., Liu Y., Karchin R. LS-SNP/PDB: annotated non-synonymous SNPs mapped to protein data bank structures.Bioinformatics (2009) 25(11):1431-2. 

Hosted: Developed by the Karchin lab, Institute for computational medicine, Department of Biomedical Engineering, Johns Hopkins University. (http://ls-snp.icm.jhu.edu/ls-snp-pdb/)

Summary:
• LS-SNP/PDB is an updated version of LS-SNP. The algorithm uses features of protein structure to highlight the biological importance of missense variants. A number of structural details are provided for the variant but no actual prediction is given as to whether the substitution is likely to be disease-related. Therefore, interpretation of results will rely on the user having a good knowledge of protein structure and the constraints that determine biological significance.

Methodology:
• All missense variants in dbSNP are systemically mapped to a known protein structure.
• In the output, the protein structure is displayed along with surface rendering coloured by conservation and electrostatic potential.

Input:
The input options are in a panel on the left of the screen. The following options are available to identify SNPs:
• Search by:
     o dnSNP IDs (eg. Rs743616)
     o gene ID (eg. BRCA1)
     o genomic region (eg. ChrII:59196382-68215218)
     o KEGG pathway (eg. Hsa00600)
     o PDB code (eg. 2e8j)
     o UniProt ID (eg. P04637)
• Filters can be set to specify SNP properties:
     o Conservation in an alignment
     o Proximity to ligands or domain boundaries
     o Secondary structure type
     o Solvent accessibility
     o Substitution severity
     o PDB origin – X-ray crystallography or NMR (X-ray considered more accurate)

Upon submitting, a list of PDB files matching the query will be displayed. Clicking one will show the dbSNPs associated and, when selected, the display will show the properties of variant in relation to the protein structure.