SNAP: predict effect of non-synonymous polymorphisms on function

By Yana Bromberg, Burkhard Rost. 2007

875 citations recorded by the source corpus.

Many genetic variations are single nucleotide polymorphisms (SNPs). Non-synonymous SNPs are ‘neutral’ if the resulting point-mutated protein is not functionally discernible from the wild type and ‘non-neutral’ otherwise. The ability to identify non-neutral substitutions could significantly aid targeting disease causing detrimental mutations, as well as SNPs that increase the fitness of particular phenotypes. Here, we introduced comprehensive data sets to assess the performance of methods that predict SNP effects. Along we introduced SNAP (screening for non-acceptable polymorphisms), a neural network-based method for the prediction of the functional effects of non-synonymous SNPs. SNAP needs only sequence information as input, but benefits from functional and structural annotations, if available. In a cross-validation test on over 80 000 mutants, SNAP identified 80% of the non-neutral substitutions at 77% accuracy and 76% of the neutral substitutions at 80% accuracy. This constituted an important improvement over other methods; the improvement rose to over ten percentage points for mutants for which existing methods disagreed. Possibly even more importantly SNAP introduced a well-calibrated measure for the reliability of each prediction. This measure will allow users to focus on the most accurate predictions and/or the most severe effects. Available at http://www.rostlab.org/services/SNAP

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

Modeling aspects of the language of life through transfer-learning protein sequences (2019, background): “…aspects of protein function improved by combining evolutionary information and machine learning, including predictions of subcellular localization (aka cellular compartment or CC in GO [21, 22]), protein interaction sites [23 – 25], and the effects of sequence variation upon function [26, 27].”
Better prediction of functional effects for sequence variants (2015, positive): “Here, we introduced several concepts each of which importantly improved over our previous method, SNAP [11].”
PredictProtein—an open resource for online prediction of protein structural and functional features (2014, neutral): “Other views present additional annotations and predictions, e.g. functional landscapes of the effect of point mutations (SNAP2, Figure 1C), predicted GO terms (metastudent, Figure 1D) or subcellular localization (LocTree3, Figure 1E).”
Functional annotations improve the predictive score of human disease‐related mutations in proteins (2009, background): “…[Chasman and Adams, 2001; Cheng et al., 2008; Wang and Moult, 2001; Worth et al., 2007; Yue and Moult, 2005, 2006], (4) artificial neural networks [Bromberg and Rost, 2007; Ferrer-Costa et al., 2002, 2004, 2005], (5) decision trees [Dobson et al., 2006; Krishnan and Westhead, 2003], (6) random…”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “…database was used to benchmark our method against nine alternative computational prediction algorithms [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; Ramen-sky et al.,…”
Assessment of computational methods for predicting the effects of missense mutations in human cancers (2013, methodology): “To our knowledge the neural network-based tool SNAP (screening for non-acceptable polymorphisms) [27,28] spans the most comprehensive feature space.”
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation (2013, background): “Thus, during the last few years, several methods have been developed to predict the impact of nsSNPs on protein stability [10-16], protein function [17,18] and to detect their pathologic effect [19-29].”
Predict impact of single amino acid change upon protein structure (2012, methodology): “dataset of structural effect: (A, D) the new method introduced here, (B, E) SNAP [17] predicting impact on function, and (C, F) I-Mutant3 [21]”
The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics (2013, methodology): “The authors of both SNAP [5,6] and MuD [7] used very nearly balanced training data sets and noted the effect of using imbalanced data sets in their papers.”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Later, methods predicting aspects of protein function improved through 24 the combination of evolutionary information and machine learning, including predictions of sub25 cellular localization (aka cellular location [21, 22]), protein interaction sites [23-25], and the 26 effects of sequence variati”
SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features (2014, background): “Sequence conservation and predicted solvent accessibility have previously been shown to be useful in SAV phenotype prediction [5].”
Neutral and weakly nonneutral sequence variants may define individuality (2013, methodology): “Importantly, however, predictions do not have the neutrality problem; computational evaluation of variant effects (8, 9) is inexpensive and fast, allowing rapid testing of all possible point mutations without preselection.”
News from the protein mutability landscape. (2013, methodology): “, sorting intolerant from tolerant (SIFT) [22,23] or screening for non-acceptable polymorphisms (SNAP) [24,25]}, others predict the effect with respect to their pathogenicity (e.”