Modeling the Oxidation of Methionine Residues by Peroxides in Proteins

• Published in: Journal of pharmaceutical sciences 2015-04, Vol.104 (4), p.1246-1255
• Main Authors: Chennamsetty, Naresh, Quan, Yong, Nashine, Vishal, Sadineni, Ikram, Lyngberg, Olav, Krystek, Stanley
• Format: Article
• Language: English
• Subjects: biophysical models; Chemistry, Pharmaceutical; computer aided drug design; Computer-Aided Design; Drug Design; in silico modeling; Methionine; Models, Chemical; molecular dynamics; Molecular Dynamics Simulation; molecular modeling; oxidation; Oxidation-Reduction; Peroxides - chemistry; physicochemical properties; Protein Conformation; Protein Denaturation; protein formulation; Protein Stability; protein structure; Proteins - chemistry; Solvents - chemistry; stabilization; Sulfoxides - chemistry; Water - chemistry
• ISSN: 0022-3549; 1520-6017
• DOI: 10.1002/jps.24340

We report the use of molecular modeling to predict the oxidation propensity of methionine residues in proteins. Oxidation of methionine to the sulfoxide form is one of the major degradation pathways for therapeutic proteins. Oxidation can occur during production, formulation, or storage of pharmaceuticals and it often reduces or eliminates biological activity. We use a molecular model based on atomistic simulations called 2-shell water coordination number to predict the oxidation rates for several model proteins and therapeutic candidates. In addition, we implement models that are based on static and simulation average of the solvent-accessible area (SAA) for either the side chain or the sulfur atom in the methionine residue. We then compare the results from the different models against the experimentally measured relative rates of methionine oxidation. We find that both the 2-shell model and the simulation-averaged SAA models are accurate in predicting the oxidation propensity of methionine residues for the proteins tested. We also find the appropriate parameter ranges where the models are most accurate. These models have significant predictive power and can be used to enable further protein engineering or to guide formulation approaches in stabilizing the unstable methionine residues.