Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State.
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.
Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.|Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.|Structural Biology Research Center (SBRC), VIB, Brussels, Belgium.|Machine Learning Group, ULB, Brussels, Belgium.
Daniele
D
Raimondi
Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.|Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.|Structural Biology Research Center (SBRC), VIB, Brussels, Belgium.|Machine Learning Group, ULB, Brussels, Belgium.
Gabriele
G
Orlando
Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.|Structural Biology Research Center (SBRC), VIB, Brussels, Belgium.
Joris
J
Messens
Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.|Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.|Structural Biology Research Center (SBRC), VIB, Brussels, Belgium.
Wim F
WF
Vranken
D000465:Algorithms; D019943:Amino Acid Substitution; D003062:Codon; D019295:Computational Biology / Q000379:methods; D003545:Cysteine / Q000235:genetics*; D030541:Databases, Genetic; D056726:Genetic Association Studies; D006801:Humans; D042541:Intracellular Space / Q000378:metabolism; D008954:Models, Biological; D009154:Mutation; D010084:Oxidation-Reduction; D020641:Polymorphism, Single Nucleotide; D021381:Protein Transport; D015203:Reproducibility of Results; D012984:Software; D064878:Web Browser
D003062:Codon; D003545:Cysteine
cysteine mutation; cysteine oxidation prediction; disulfide bond; machine learning; molecular phenotype; single-nucleotide variants