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Reference: De Baets G, Van Durme J, Reumers J, Maurer-Stroh S, Vanhee P, Schymkowitz J, Rousseau F.  SNPeffect4.0: online prediction of molecular and structural effects of protein-coding variants.Nucleic Acids Research (2012) 40(1):D935-9. 
Hosted: (http://snpeffect.switchlab.org/)

Summary:
SNPeffect is a resource for phenotyping human SNPs using molecular characterisation and annotation of disease and polymorphism variants. It combines a database of characterised SNPs (>60,000) from UniProt and a tool for assessing new variants.

Methodology:
Several algorithms are applied to each wild-type and mutant proteins:
• TANGO:
     o Detects aggregation-prone regions by assessing hydrophobicity and beta-sheet formation propensity. TANGO difference between -50 and +50 are considered significant
• WALTZ:
     o An algorithm to predict amyloid-forming regions in protein sequences
• LIMBO:
     o A chaperone binding site predictor for Hsp70 chaperones
• FoldX:
     o If structural information is available, FoldX calculates the difference in free energy of the mutation
• Functional sites and structural features:
     o Using the Catalytic Site Atlas
     o Secondary structure annotations from UniProt
     o Transmembrane topology using tool from Centre for biological sequence analysis (CBS)
• Cellular processing and posttranslational modification:
     o Farnesylation, geranylgeranylation, myristoylation, GPI anchor and PTS1 targetting analysis
     o Subcellular localisation predicted using PSORT
• Domain annotation:
     o Using Pfam and SMART domain information


Input:
Initially there are 3 options:

1. The user can look-up a variant in the SNPeffect database and filter results based on disease, mutation type, gene name, UniProt ID and dbSNP ID.

2. When submitting a new SNPeffect job there is the choice of submitting the query:
• FASTA sequence
• PDB file
• PDB ID
• UniProt ID

If ‘FASTA sequence’ or ‘UniProt ID’ is selected, the user can alter the homology threshold from the default of 90%. This is where the tool attempts to find a structural homologue to the query sequence to run some of the predictive algorithms. The models accuracy will decrease as the threshold is lowered.

The user will receive a pdf report detailing the differences between the wild-type and mutant proteins in terms of the various predictive algorithms used. It will conclude with a summary of each prediction and the user can then assess the evidence for disease-association or not.

3. Meta-analysis can also be performed by selecting a disease and plotting the TANGO, WALTZ, LIMBO and FoldX scores of all variants, disease-associated variants or polymorphisms.