Prediction of Missense Mutation Functionality Depends on both the Algorithm and Sequence Alignment Employed

By S. Hicks, David A. Wheeler, Sharon E Plon, Marek Kimmel. 2011

226 citations recorded by the source corpus.

Multiple algorithms are used to predict the impact of missense mutations on protein structure and function using algorithm‐generated sequence alignments or manually curated alignments. We compared the accuracy with native alignment of SIFT, Align‐GVGD, PolyPhen‐2, and Xvar when generating functionality predictions of well‐characterized missense mutations (n = 267) within the BRCA1, MSH2, MLH1, and TP53 genes. We also evaluated the impact of the alignment employed on predictions from these algorithms (except Xvar) when supplied the same four alignments including alignments automatically generated by (1) SIFT, (2) Polyphen‐2, (3) Uniprot, and (4) a manually curated alignment tuned for Align‐GVGD. Alignments differ in sequence composition and evolutionary depth. Data‐based receiver operating characteristic curves employing the native alignment for each algorithm result in area under the curve of 78–79% for all four algorithms. Predictions from the PolyPhen‐2 algorithm were least dependent on the alignment employed. In contrast, Align‐GVGD predicts all variants neutral when provided alignments with a large number of sequences. Of note, algorithms make different predictions of variants even when provided the same alignment and do not necessarily perform best using their own alignment. Thus, researchers should consider optimizing both the algorithm and sequence alignment employed in missense prediction. Hum Mutat 32:1–8, 2011. © 2011 Wiley‐Liss, Inc.

Subfields: Cancer Research, Genetics, Molecular Biology

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Citations in the corpus

Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2 (2013, background): “Estimates from other sources arrive at similar numbers (for example, Hicks et al. 2011).”
SIFT web server: predicting effects of amino acid substitutions on proteins (2012, background): “Hicks et al . noted that SIFT exhibited low speciﬁcity when tested on 267 amino acid changes located in four genes (29).”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “However, Align-GVGD employs gene-speciﬁc alignments and its performance is severely affected when automatically generated alignments are used [Hicks et al., 2011].”
Genic Intolerance to Functional Variation and the Interpretation of Personal Genomes (2013, unknown): “Many approaches are available that attempt to prioritize mutations in terms of their prior probabilities of conferring risk of disease, notably including population allele frequency and measures of conservation at either the phylogenetic level [1] or in terms of amino acid characteristics [2–6].”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, background): “Several algorithms are very sensitive to the multiple sequence alignment being used [33].”
The integrated landscape of driver genomic alterations in glioblastoma (2013, background): “Each mutation targets highly conserved residues with probably (p.Lys629Gln, p.Ala776Thr, p.Ser881Leu and p.Asp999Glu) and possibly (p.Ala71Thr) damaging consequences 41 .”
A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness (2014, background): “Computational approaches therefore aim to identify which coding variations cause disease (Ng and Henikoff 2001; Stone and Sidow 2005; Adzhubei et al. 2010) within the limitations of bio-physical, statistical, and machine-learning models of protein function (Chun and Fay 2009; Hicks et al. 2011).”
Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation (2016, methodology): “Hicks et al. tested these hypotheses by comparing SIFT and PolyPhen-2 on alignments constructed by four different methods (Hicks et al. 2011).”
Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST‐Indel) (2015, background): “Given that poor specificity also limits the utility of methods aimed at assessing the pathogenicity of missense variants [Chan et al., 2007; Hicks et al., 2011; Thusberg et al., 2011; Shihab et al., 2013], the approach presented here might prove beneficial for variant classification in general.”