Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations

BACKGROUND: Recently, information derived by correlated mutations in proteins has regained relevance for predicting protein contacts. This is due to new forms of mutual information analysis that have been proven to be more suitable to highlight direct coupling between pairs of residues in protein st...

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Detalles Bibliográficos
Autores principales: Savojardo, Castrense, Fariselli, Piero, Martelli, Pier Luigi, Casadio, Rita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548674/
https://www.ncbi.nlm.nih.gov/pubmed/23368835
http://dx.doi.org/10.1186/1471-2105-14-S1-S10

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548674/
https://www.ncbi.nlm.nih.gov/pubmed/23368835
http://dx.doi.org/10.1186/1471-2105-14-S1-S10

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