Editorial
Copyright ©The Author(s) 2015.
World J Med Genet. Aug 27, 2015; 5(3): 46-51
Published online Aug 27, 2015. doi: 10.5496/wjmg.v5.i3.46
Table 1 Examples of freely available, online tools for predicting the properties of variant proteins
CategoryNameWeblinkRef.
Structural analysisYASARA energy minimisationhttp://www.yasara.org/minimizationserver.htm[29]
LS-SNPls-snp.icm.jhu.edu/ls-snp-pdb/main[30]
GETAREAcurie.utmb.edu/getarea.html[31]
Stability predictionI-Mutant 3.0gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi[32,33]
mCSMbleoberis.bioc.cam.ac.uk/mcsm/[34]
SDM scoremordred.bioc.cam.ac.uk/~sdm/sdm.php[35,36]
Mupromupro.proteomics.ics.uci.edu[37]
iStablepredictor.nchu.edu.tw/iStable/[38]
PredictSNP 1.0loschmidt.chemi.muni.cz/predictsnp/[39]
Meta-SNPsnps.biofold.org/meta-snp/[40]
KD4Vdecrypthon.igbmc.fr/kd4v[41]
Fold-Xfoldx.crg.es[42]
PoPMuSiCdezyme.com/[43]
CUPSATcupsat.tu-bs.de[44,45]
GETAREAcurie.utmb.edu/getarea.html[31]
Binding affinity changesBeAtMuSiCbabylone.ulb.ac.be/beatmusic[46]
Aggregation tendency, amyloid formation and chaperone bindingTANGOtango.crg.es/[47]
WALTZhttp://www.switchlab.org/bioinformatics/waltz[48]
LIMBOhttp://www.switchlab.org/bioinformatics/limbo[49]
Sequence conservationClustal Omegahttp://www.ebi.ac.uk/tools/msa/clustalo/[50]
Scoreconshttp://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/valdar/scorecons_server.pl[51]
SIFTsift.jcvi.org/[52]
PROVEANprovean.jcvi.org/index.php[53]
LS-SNPls-snp.icm.jhu.edu/ls-snp-pdb/[30]
SNPs and GOsnps.biofold.org/snps-and-go/pages/help.html[54]
PANTHERhttp://www.pantherdb.org/tools/csnpscoreform.jsp[55]
GenMAPPhttp://www.genmapp.org[56]
PolyPhen 2genetics.bwh.harvard.edu/pph2/[57,58]
nsSNP Analyzersnpanalyzer.uthsc.edu[59]
FI mutation assessormutationassessor.org/v1[60]
YALE MU2Akrauthammerlab.med.yale.edu/mu2a[61]
Table 2 Examples of bioinformatics based predictions of the severity of variants associated with inherited metabolic diseases
DiseaseProteinCommentsRef.
AlkaptonuriaHomogentisate 1,2-dioxygenaseCombining a variety of computational approaches gave rise to the most accurate predictions[62]
Apparent mineralocorticoid excess11βHSD2The predicted degree of structural change in the enzyme correlates with disease severity[63]
Fabry diseaseGLAA purpose built program designed to detect protein instability outperformed existing, generic tools[64]
Fabry diseaseGLAA purpose built web interface allows prediction of a patient’s responsiveness to pharmacological chaperone therapy[65]
Gaucher diseaseGBASlightly different results were obtained with different programs; however, 22 out of 47 variants were predicted to be harmful by all seven programs used[28]
Glucose 6-phosphate dehydrogenase deficiencyG6PDHA combination of prediction tools suggested that protein stability is an important factor in this disease; novel potentially disease-associated variants were identified[66]
HyperargininemiaARG1Mutations affect residues in the active site, or protein stability, or quaternary structure[67]
MODY 2GCKVariations which decrease protein stability and/or occur in highly conserved regions of the protein are associated with disease[68]
Niemann-pick disease type CNPC1 and NPC2The majority of disease-associated variants were predicted to be less stable than wild-type[69]
PhenylketonuriaPAHProtein stability predicted to be most important factor in disease causation[10]
Pyruvate kinase deficiencyPK1 and PK2A combination of prediction tools suggested that protein stability is an important factor in this disease; novel potentially disease-associated variants were identified[66]
Type I galactosemiaGALTMain predicted effect is the loss of stability of GALT[70]
Type III galactosemiaGALEEffects on protein stability and degree of sequence conservation combined were required for good predictions[71]