Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed
Open accessAbstract
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.
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Several efforts have evaluated the performance of available prediction software, to compare them to each other and to assess their ability to predict known disease-causing variants.
“Several efforts have evaluated the performance of available prediction software, to compare them to each other and to assess their ability to predict 'known' disease-causing variants. 14-17”
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- Authors
- Hicks, S., Wheeler, D. A., Plon, S. E., & Kimmel, M.
- Affiliations
- Rice University (US)
- Baylor College of Medicine (US)
- Corresponding author
- Kimmel, Marek
- Venue
- Human Mutation
- Publisher
- Hindawi Limited
- Published
- 2011-04-07
- Volume
- 32
- Issue
- 6
- Pages
- 661-668
- ISSN
- 1059-7794 (ISSN-L)
- PMC
- PMC4154965
- OpenAlex
- W2039878336
- Cited by
- 223
- References
- 4
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 9 · 2023: 6 · 2022: 12 · 2021: 11 · 2020: 15 · 2019: 17 · 2018: 6 · 2017: 23 · 2016: 14 · 2015: 20 · 2014: 26 · 2013: 24 · 2012: 24
- FWCI
- 16.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Mutation (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5t32ca096520-18)
- National Cancer Institute (T32 CA-096520)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- sequence alignment, algorithm comparison, SIFT, PolyPhen-2, BRCA1, MSH2, MLH1, TP53, germline mutations, protein function prediction
- MeSH terms
- MutL Protein Homolog 1, Algorithms, Humans, Nuclear Proteins, Genes, Tumor Suppressor, Tumor Suppressor Protein p53, Sequence Alignment, Computational Biology, BRCA1 Protein, Mutation, Missense, Sequence Analysis, Protein, Adaptor Proteins, Signal Transducing, MutS Homolog 2 Protein
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