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Cited by 2,890Genomics and Rare DiseasesGenomics and Phylogenetic StudiesGenetic Associations and Epidemiology

Predicting the Functional Effect of Amino Acid Substitutions and Indels

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Choi, Yongwook, Sims, Gregory E., Murphy, Sean, Miller, Jason R., Chan, Agnes P.·2012·PLoS ONE
DOI 10.1371/journal.pone.0046688PMC PMC3466303
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Abstract

As next-generation sequencing projects generate massive genome-wide sequence variation data, bioinformatics tools are being developed to provide computational predictions on the functional effects of sequence variations and narrow down the search of casual variants for disease phenotypes. Different classes of sequence variations at the nucleotide level are involved in human diseases, including substitutions, insertions, deletions, frameshifts, and non-sense mutations. Frameshifts and non-sense mutations are likely to cause a negative effect on protein function. Existing prediction tools primarily focus on studying the deleterious effects of single amino acid substitutions through examining amino acid conservation at the position of interest among related sequences, an approach that is not directly applicable to insertions or deletions. Here, we introduce a versatile alignment-based score as a new metric to predict the damaging effects of variations not limited to single amino acid substitutions but also in-frame insertions, deletions, and multiple amino acid substitutions. This alignment-based score measures the change in sequence similarity of a query sequence to a protein sequence homolog before and after the introduction of an amino acid variation to the query sequence. Our results showed that the scoring scheme performs well in separating disease-associated variants (n = 21,662) from common polymorphisms (n = 37,022) for UniProt human protein variations, and also in separating deleterious variants (n = 15,179) from neutral variants (n = 17,891) for UniProt non-human protein variations. In our approach, the area under the receiver operating characteristic curve (AUC) for the human and non-human protein variation datasets is ∼0.85. We also observed that the alignment-based score correlates with the deleteriousness of a sequence variation. In summary, we have developed a new algorithm, PROVEAN (Protein Variation Effect Analyzer), which provides a generalized approach to predict the functional effects of protein sequence variations including single or multiple amino acid substitutions, and in-frame insertions and deletions. The PROVEAN tool is available online at http://provean.jcvi.org.

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  • Most in-silico tools tend to have low specificity, over-predict missense changes as deleterious, and are not as reliable at predicting missense variants with a milder effect.

    “Most tools also tend to have low specificity, resulting in over-prediction of missense changes as deleterious and are not as reliable at predicting missense variants with a milder effect, 18”

    From Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (2015) · cites this paper as [18]

    In Results, Table 5 reports lower specificity than sensitivity for the three comparison tools on single-amino-acid substitutions, consistent with some neutral variants being predicted as deleterious. But the paper compares only three other tools, and its binary performance results do not establish that tools are less reliable specifically for missense variants with milder effects. No page numbers are available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., & Chan, A. P.
Affiliations
J. Craig Venter Institute (US)
Corresponding author
Chan, Agnes P.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2012-10-08
Volume
7
Issue
10
Pages
e46688
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0046688
PMC
PMC3466303
OpenAlex
W2089335658
Cited by
2,890
References
7
Citations by year
2026: 80 · 2025: 123 · 2024: 139 · 2023: 205 · 2022: 280 · 2021: 338 · 2020: 284 · 2019: 310 · 2018: 261 · 2017: 228 · 2016: 214 · 2015: 213 · 2014: 160 · 2013: 52 · 2012: 1
FWCI
82.10
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Institutes of Health
  • National Human Genome Research Institute (R01HG004701)
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 › Genetics › Genetic Associations and Epidemiology
Keywords
PROVEAN, protein sequence variation, amino acid substitutions, deleterious variant prediction, disease-associated variants, polymorphism, frameshift mutation, protein function prediction
MeSH terms
Animals, Humans, Mutation, Genome, Human, Computational Biology, Amino Acid Substitution, Databases, Genetic, INDEL Mutation

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