03094nas a2200385   4500008004100000022001400041245013400055210006900189260001300258300001200271490000700283520182800290653001502118653001002133653002602143653002302169653002802192653002502220653003802245653002202283653001802305653001102323653001802334653001902352653003602371653001302407653003302420100002202453700002102475700001802496700002002514700002002534700002502554856012902579       2008                        eng d  a1098-100400aUse of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.0 aUse of estimated evolutionary strength at the codon level improv  c2008 Jan  a198-2040 v293 a<p>Predicting 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.</p>10aAlgorithms10aCodon10aComputational Biology10aDatabases, Protein10aDNA Mutational Analysis10aEvolution, Molecular10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aHumans10aIduronic Acid10aPoint Mutation10aPolymorphism, Single Nucleotide10aProteins10aTumor Suppressor Protein p531 aCapriotti, Emidio1 aArbiza, Leonardo1 aCasadio, Rita1 aDopazo, Joaquin1 aDopazo, Hernán1 aMarti-Renom, Marc, A  uhttps://www.clinbioinfosspa.es/content/use-estimated-evolutionary-strength-codon-level-improves-prediction-disease-related-0