Literature DB >> 27667481

Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State.

Daniele Raimondi1,2,3,4, Gabriele Orlando1,2,3,4, Joris Messens2,3, Wim F Vranken1,2,3.   

Abstract

Cysteines are among the rarest amino acids in nature, and are both functionally and structurally very important for proteins. The ability of cysteines to form disulfide bonds is especially relevant, both for constraining the folded state of the protein and for performing enzymatic duties. But how does the variation record of human proteins reflect their functional importance and structural role, especially with regard to deleterious mutations? We created HUMCYS, a manually curated dataset of single amino acid variants that (1) have a known disease/neutral phenotypic outcome and (2) cause the loss of a cysteine, in order to investigate how mutated cysteines relate to structural aspects such as surface accessibility and cysteine oxidation state. We also have developed a sequence-based in silico cysteine oxidation predictor to overcome the scarcity of experimentally derived oxidation annotations, and applied it to extend our analysis to classes of proteins for which the experimental determination of their structure is technically challenging, such as transmembrane proteins. Our investigation shows that we can gain insights into the reason behind the outcome of cysteine losses in otherwise uncharacterized proteins, and we discuss the possible molecular mechanisms leading to deleterious phenotypes, such as the involvement of the mutated cysteine in a structurally or enzymatically relevant disulfide bond.
© 2016 WILEY PERIODICALS, INC.

Entities:  

Keywords:  cysteine mutation; cysteine oxidation prediction; disulfide bond; machine learning; molecular phenotype; single-nucleotide variants

Mesh:

Substances:

Year:  2016        PMID: 27667481     DOI: 10.1002/humu.23129

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  3 in total

1.  The correlation between CRB1 variants and the clinical severity of Brazilian patients with different inherited retinal dystrophy phenotypes.

Authors:  Fabiana Louise Motta; Mariana Vallim Salles; Karita Antunes Costa; Rafael Filippelli-Silva; Renan Paulo Martin; Juliana Maria Ferraz Sallum
Journal:  Sci Rep       Date:  2017-08-17       Impact factor: 4.379

2.  Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Authors:  Daniele Raimondi; Gabriele Orlando; Wim F Vranken; Yves Moreau
Journal:  Sci Rep       Date:  2019-11-15       Impact factor: 4.379

3.  Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome.

Authors:  Daniele Raimondi; Gabriele Orlando; Francesco Tabaro; Tom Lenaerts; Marianne Rooman; Yves Moreau; Wim F Vranken
Journal:  Sci Rep       Date:  2018-11-19       Impact factor: 4.379

  3 in total

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