Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed

• Published in: Human mutation Vol. 32; no. 6; pp. 661 - 668
• Main Authors: Hicks, Stephanie, Wheeler, David A., Plon, Sharon E., Kimmel, Marek
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.06.2011; John Wiley & Sons, Inc
• Subjects: Adaptor Proteins, Signal Transducing - genetics; Adaptor Proteins, Signal Transducing - metabolism; Algorithms; Align-GVGD; BRCA1; BRCA1 protein; BRCA1 Protein - genetics; BRCA1 Protein - metabolism; Computational Biology; Evolution; Genes, Tumor Suppressor; Humans; Missense mutation; MLH1; MLH1 protein; MSH2; MSH2 protein; multiple sequence alignment; Mutation, Missense - genetics; MutL Protein Homolog 1; MutS Homolog 2 Protein - genetics; MutS Homolog 2 Protein - metabolism; Nuclear Proteins - genetics; Nuclear Proteins - metabolism; Nucleotide sequence; p53 protein; PolyPhen-2; Protein structure; Sequence Alignment - methods; Sequence Analysis, Protein; SIFT; TP53; Tumor Suppressor Protein p53 - genetics; Tumor Suppressor Protein p53 - metabolism; Xvar
• ISSN: 1059-7794; 1098-1004; 1098-1004
• DOI: 10.1002/humu.21490

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.