Predicting the functional impact of protein mutations: application to cancer genomics

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ID: 291032
2011
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Abstract
As large-scale re-sequencing of genomes reveals many protein mutations, especially in human cancer tissues, prediction of their likely functional impact becomes important practical goal. Here, we introduce a new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns. The information in these patterns is derived from aligned families and sub-families of sequence homologs within and between species using combinatorial entropy formalism. The score performs well on a large set of human protein mutations in separating disease-associated variants (∼19 200), assumed to be strongly functional, from common polymorphisms (∼35 600), assumed to be weakly functional (area under the receiver operating characteristic curve of ∼0.86). In cancer, using recurrence, multiplicity and annotation for ∼10 000 mutations in the COSMIC database, the method does well in assigning higher scores to more likely functional mutations ('drivers'). To guide experimental prioritization, we report a list of about 1000 top human cancer genes frequently mutated in one or more cancer types ranked by likely functional impact; and, an additional 1000 candidate cancer genes with rare but likely functional mutations. In addition, we estimate that at least 5% of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.
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openalex_W2111326065 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Boris Reva, Yevgeniy Antipin, Chris Sander
Journal Nucleic Acids Research
Year 2011
DOI
10.1093/nar/gkr407
URL
https://academic.oup.com/nar/article-pdf/39/17/e118/16776369/gkr407.pdf
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