Analyzing Cysteine Site Neighbors in Proteins to Reveal Dimethyl Fumarate Targets

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.