Functional annotations improve the predictive score of human disease‐related mutations in proteins

By Remo Calabrese, Emidio Capriotti, Piero Fariselli, Pier Luigi Martelli, Rita Casadio. 2009

698 citations recorded by the source corpus.

Single nucleotide polymorphisms (SNPs) are the simplest and most frequent form of human DNA variation, also valuable as genetic markers of disease susceptibility. The most investigated SNPs are missense mutations resulting in residue substitutions in the protein. Here we propose SNPs&GO, an accurate method that, starting from a protein sequence, can predict whether a mutation is disease related or not by exploiting the protein functional annotation. The scoring efficiency of SNPs&GO is as high as 82%, with a Matthews correlation coefficient equal to 0.63 over a wide set of annotated nonsynonymous mutations in proteins, including 16,330 disease‐related and 17,432 neutral polymorphisms. SNPs&GO collects in unique framework information derived from protein sequence, evolutionary information, and function as encoded in the Gene Ontology terms, and outperforms other available predictive methods. Hum Mutat 30:1–8, 2009. © 2009 Wiley‐Liss, Inc.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “Some methods focus exclusively on the human genome [12,13] and some aspire to identify disease-causing variants [14-16].”
News from the protein mutability landscape. (2013, background): “, MutPred [26], SNPs&GO [27], Mutation Assesor [28] or MutationTaster [29]) and others yet predict the effect on protein structure directly [30] or cannot be easily fit into these categories (e.”
A new disease-specific machine learning approach for the prediction of cancer-causing missense variants (2011, methodology): “SNPs&GO [28] includes a new function annotation score calculated using GO terms and MutPred [25] evaluates the impact of a given variant considering the output of several machine learning approaches.”
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation (2013, methodology): “…is a good alternative to well-established tools like SIFT [18] and PolyPhen [23] since it returns high quality predictions as shown in previous works [19,20,40] and confirmed in the 2011 edition of the Critical Assessment of Genome Interpretation experiments (http://genomeinterpretation.org/).”
Performance of mutation pathogenicity prediction methods on missense variants (2011, background): “…structural and/or functional parameters and multiple sequence alignment derived information (MutPred [Li et al., 2009], nsSNPAnalyzer [Bao et al., 2005], PolyPhen [Ramensky et al., 2002], PolyPhen2 [Adzhubei et al., 2010], SNAP [Bromberg and Rost, 2007], and SNPs&GO [Calabrese et al., 2009]).”
Improving the prediction of disease-related variants using protein three-dimensional structure (2011, methodology): “The results in Table 3 show that SVM-3D has 2% higher accuracy and 3% higher correlation coefficient with respect to the PolyPhen2 and SVM-SEQ.”
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations (2014, methodology): “Therearemany computational tools for prediction of the effects of amino acid substitution on protein function, e.g., MutPred [10], nsSNPAnalyzer [11], PolyPhen-1 (PPH-1) [12], PolyPhen-2 (PPH-2) [13], SNAP [14], MAPP [15], PANTHER [16], PhD-SNP [17], SIFT [18] and SNPs&GO [19].”
Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. (2015, neutral): “Although seven out of nine of their compared methods (PolyPhen-2, SIFT, PANTHER, PhD-SNP, SNAP, SNPs&GO and MutPred) overlap our study, there are major differences between their study and ours.”
Collective judgment predicts disease-associated single nucleotide variants (2013, background): “In the last decade, several algorithms have been developed to predict disease-related and functionally deleterious variants [11-21].”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “…review [Thusberg et al., 2011] of nine other computational prediction methods [Adzhubei et al., 2010; Bao et al., 2005; Bromberg and Rost, 2007; Calabrese et al., 2009; Capriotti et al., 2006; Li et al., 2009; Mort et al., 2010; Ng and Henikoff, 2001; Ramensky et al., 2002; Thomas et al.,…”
DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins (2017, methodology): “These predictors generally consider different molecular or protein-level aspects that are closely related to possible mechanisms of pathogenicity, such as the evolutionary conservation of the mutated position (9,10), the stability change upon mutation (14), possible structural alterations (9,14) and”