02909nas a2200241   4500008004100000245013300041210006900174300001200243490000700255520182100262653009602083653009302179653007402272653002302346653008902369100001802458700001502476700001602491700001502507700001502522700002402537856010602561       2008                        eng d00aUse of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans0 aUse of estimated evolutionary strength at the codon level improv  a198-2040 v293 aPredicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.10aAlgorithms Codon/genetics Computational Biology/*methods *DNA Mutational Analysis Databases10aHuman Humans Iduronic Acid/analogs & derivatives/metabolism *Point Mutation Polymorphism10aMolecular *Genetic Predisposition to Disease Genetic Variation Genome10aProtein *Evolution10aSingle Nucleotide Proteins/chemistry/*genetics Tumor Suppressor Protein p53/genetics1 aCapriotti, E.1 aArbiza, L.1 aCasadio, R.1 aDopazo, J.1 aDopazo, H.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1793514802245nas a2200253   4500008004100000245008600041210006900127300001100196490000800207520134200215653001501557653003601572653012101608653002301729100001801752700001601770700001401786700002401800700001501824700001901839700001301858700001401871856010601885       2005                        eng d00aDetecting remotely related proteins by their interactions and sequence similarity0 aDetecting remotely related proteins by their interactions and se  a7151-60 v1023 aThe function of an uncharacterized protein is usually inferred either from its homology to, or its interactions with, characterized proteins. Here, we use both sequence similarity and protein interactions to identify relationships between remotely related protein sequences. We rely on the fact that homologous sequences share similar interactions, and, therefore, the set of interacting partners of the partners of a given protein is enriched by its homologs. The approach was bench-marked by assigning the fold and functional family to test sequences of known structure. Specifically, we relied on 1,434 proteins with known folds, as defined in the Structural Classification of Proteins (SCOP) database, and with known interacting partners, as defined in the Database of Interacting Proteins (DIP). For this subset, the specificity of fold assignment was increased from 54% for position-specific iterative BLAST to 75% for our approach, with a concomitant increase in sensitivity for a few percentage points. Similarly, the specificity of family assignment at the e-value threshold of 10(-8) was increased from 70% to 87%. The proposed method would be a useful tool for large-scale automated discovery of remote relationships between protein sequences, given its unique reliance on sequence similarity and protein-protein interactions.10aAmino Acid10aComputational Biology Databases10aMolecular Protein Conformation Protein Folding Proteins/*genetics/*metabolism Proteomics/*methods *Sequence Homology10aProtein *Evolution1 aEspadaler, J.1 aAragues, R.1 aEswar, N.1 aMarti-Renom, M., A.1 aQuerol, E.1 aAviles, F., X.1 aSali, A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15883372