Predicting protein homocysteinylation targets based on dihedral strain energy and pKa of cysteines.
A multitude of complex diseases have been linked to elevated homocysteine levels; however, till date there is no plausible explanation for a single amino acid's involvement in so many diseases. Since homocysteine is a reactive thiol amino acid and the majority of plasma homocysteine is protein thiol bound, we hypothesized that homocysteine might bind to accessible cysteine residues in target proteins, thereby modulating its structure or function or both. The parameters that dictate homocysteine-protein interaction are not well understood, and the few known homocysteine binding proteins were identified by a candidate protein approach. In this study, we identified potential homocysteine interacting proteins based on cysteine content, solvent accessibility of cysteine residues, and dihedral strain energies and pKa of these cysteines. Pathway mapping of the cysteine-rich proteins revealed that proteins in the coagulation cascade, notch receptor-mediated signaling, LDL endocytosis, programmed cell death, and extracellular matrix proteins were significantly over-represented with cysteine-rich proteins, and we believe that homocysteine has a high probability to bind to proteins in these pathways. In fact, several clinical studies have implicated high homocysteine levels to be associated with diseases like thrombosis, neural tube defects, and so forth, which result from dysfunction of one or more of the proteins identified in our study. Further, we successfully validated our prediction parameters on the proteins that have already been experimentally shown to bind homocysteine, and our structural analysis argues a plausible explanation for these prior reported protein interactions with homocysteine that could not be previously explained.
Sundaramoorthy
Elayanambi
E
Proteomics and Structural Biology Unit, Institute of Genomics and Integrative Biology, Delhi 110007, India.
Maiti
Souvik
S
Brahmachari
Samir K
SK
Sengupta
Shantanu
S
D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003545:Cysteine / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D030562:Databases, Protein / Q000706:statistics & numerical data; D006710:Homocysteine / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D006801:Humans; D006863:Hydrogen-Ion Concentration; D011485:Protein Binding; D015398:Signal Transduction; D013816:Thermodynamics
D006710:Homocysteine; D003545:Cysteine