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EnglishThis work proposes a novel approach by which to consistently classify cysteine sites in proteins in terms of their reactivity toward dimethyl fumarate (DMF) and fumarate. Dimethyl fumarate-based drug products have been approved for use as oral treatments for psoriasis and relapsing-remitting multiple sclerosis. The adduction of DMF and its (re)active metabolites to certain cysteine residues in proteins is thought to underlie their effects. However, only a few receptors for these compounds have been discovered to date. Our approach takes advantage of the growing number of known DMF- and fumarate-sensitive proteins and sites to perform analyses by combining the concepts of network theory, for protein structure analyses, and machine-learning procedures. Wide-ranging and previously unforeseen variety is found in the analysis of the neighborhood composition (the first neighbors) of cysteine sites found in DMF- and fumarate-sensitive proteins. Furthermore, neighborhood composition has shown itself to be a network-type attribute that is endowed with remarkable predictive power when distinct classification algorithms are employed. In conclusion, when adopted in combination with other target identification/validation approaches, methods that are based on the analysis of cysteine site neighbors in proteins should provide useful information by which to decipher the mode of action of DMF-based drugs.
| Titolo: | Analyzing Cysteine Site Neighbors in Proteins to Reveal Dimethyl Fumarate Targets |
| Autori Riconosciuti: | |
| Autori: | Rosa A.C.; Benetti E.; Gallicchio M.; Boscaro V.; Cangemi L.; Dianzani C.; Miglio G. |
| Data di pubblicazione: | 2019 |
| Abstract: | This work proposes a novel approach by which to consistently classify cysteine sites in proteins in terms of their reactivity toward dimethyl fumarate (DMF) and fumarate. Dimethyl fumarate-based drug products have been approved for use as oral treatments for psoriasis and relapsing-remitting multiple sclerosis. The adduction of DMF and its (re)active metabolites to certain cysteine residues in proteins is thought to underlie their effects. However, only a few receptors for these compounds have been discovered to date. Our approach takes advantage of the growing number of known DMF- and fumarate-sensitive proteins and sites to perform analyses by combining the concepts of network theory, for protein structure analyses, and machine-learning procedures. Wide-ranging and previously unforeseen variety is found in the analysis of the neighborhood composition (the first neighbors) of cysteine sites found in DMF- and fumarate-sensitive proteins. Furthermore, neighborhood composition has shown itself to be a network-type attribute that is endowed with remarkable predictive power when distinct classification algorithms are employed. In conclusion, when adopted in combination with other target identification/validation approaches, methods that are based on the analysis of cysteine site neighbors in proteins should provide useful information by which to decipher the mode of action of DMF-based drugs. |
| Volume: | 19 |
| Fascicolo: | 4 |
| Pagina iniziale: | e1800301 - 1 |
| Pagina finale: | e1800301 - 7 |
| Digital Object Identifier (DOI): | 10.1002/pmic.201800301 |
| URL: | http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1615-9861 |
| Parole Chiave: | computational methods; cysteine reactivity; machine learning; residue interaction networks |
| Rivista: | PROTEOMICS |
| Appare nelle tipologie: | 03A-Articolo su Rivista |
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| Proteomics 2019.pdf | POSTPRINT (VERSIONE FINALE DELL’AUTORE) | Open Access Visualizza/Apri | ||
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