Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements

@article{Raimondi2015ClusteringbasedMO,
  title={Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements},
  author={Daniele Raimondi and Gabriele Orlando and Wim F. Vranken},
  journal={Bioinformatics},
  year={2015},
  volume={31 8},
  pages={
          1219-25
        }
}
MOTIVATION Cysteine residues have particular structural and functional relevance in proteins because of their ability to form covalent disulfide bonds. Bioinformatics tools that can accurately predict cysteine bonding states are already available, whereas it remains challenging to infer the disulfide connectivity pattern of unknown protein sequences. Improving accuracy in this area is highly relevant for the structural and functional annotation of proteins. RESULTS We predict the intra-chain… CONTINUE READING
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Key Quantitative Results

  • We compared our method with state-of-the-art unsupervised predictors and achieve a performance improvement of 25-27% while requiring an order of magnitude less of aligned homologous sequences (∼10(3) instead of ∼10(4)).

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