Single Nucleotide Polymorphisms (SNPs) represent a very large portion of all genetic variations. SNPs found in the coding regions of genes are often non-synonymous, changing a single amino acid in the encoded protein sequence. SNPs are either "neutral" in the sense that the resulting point-mutated protein is not functionally discernible from the wild-type, or they are "non-neutral" in that the mutant and wild-type differ in function. The ability to identify non-neutral substitutions in an ocean of SNPs could significantly aid targeting disease causing detrimental mutations, as well as SNPs that increase the fitness of particular phenotypes.

SNAP is a neural-network based method that uses in silico derived protein information (e.g. secondary structure, conservation, solvent accessibility, etc.) in order to make predictions regarding functionality of mutated proteins. The network takes protein sequences and lists of mutants as input, returning a score for each substitution. These scores can then be translated into binary predictions of effect (present/absent) and reliability indices (RI). SNAP returns its results to the user via e-mail.

NOTE: Reliability Indices are somewhat correlated with severity of change (as described in the paper referenced below). However, users are urged to take care in using them directly as severity classifiers.

From this site you can:

• Go to the submission page
• Find out how to use SNAP
• View the sample output of SNAP
• Get scores for known mutants.
• Get predictions for all substitutions at all HXK4_HUMAN residues mutants.
• Get predictions for residues reported in ASEdb
ASEdb is a database of alanine scans (binding energy changes due to subtitutions of type X to Ala). It can be accessed here and should be cited if used.

• SNAP: predict effect of non-synonymous polymorphisms on function.
  Yana Bromberg and Burkhard Rost
  Nucleic Acids Research, 2007, Vol. 35, No. 11 3823-3835 (PubMed)