Machine learning prediction of methionine and tryptophan photooxidation susceptibility

Abstract

Photooxidation of methionine (Met) and tryptophan (Trp) residues is common and includes major degradation pathways that often pose a serious threat to the success of therapeutic proteins. Oxidation impacts all steps of protein production, manufacturing, and shelf life. Prediction of oxidation liability as early as possible in development is important because many more candidate drugs are discovered than can be tested experimentally. Undetected oxidation liabilities necessitate expensive and time-consuming remediation strategies in development and may lead to good drugs reaching patients slowly. Conversely, sites mischaracterized as oxidation liabilities could result in overengineering and lead to good drugs never reaching patients. To our knowledge, no predictive model for photooxidation of Met or Trp is currently available. We applied the random forest machine learning algorithm to in-house liquid chromatography-tandem mass spectrometry (LC-MS/MS) datasets (Met, n = 421; Trp, n = 342) of tryptic therapeutic protein peptides to create computational models for Met and Trp photooxidation. We show that our machine learning models predict Met and Trp photooxidation likelihood with 0.926 and 0.860 area under the curve (AUC), respectively, and Met photooxidation rate with a correlation coefficient (Q 2 ) of 0.511 and root-mean-square error (RMSE) of 10.9%. We further identify important physical, chemical, and formulation parameters that influence photooxidation. Improvement of biopharmaceutical liability predictions will result in better, more stable drugs, increasing development throughput, product quality, and likelihood of clinical success.

In silico prediction of post-translational modifications in therapeutic antibodies

“… 9 More recent studies have successfully incorporated MD simulations and machine learning to predict Met and Trp oxidation. 29 , 59 However, specific factors that affect oxidation risk can vary depending on the type of liable residue and the type of oxidative stress. 60 The subsequent sections outline in silico prediction for Met, Trp, and His oxidation in more depth.…”

“…Machine learning models have been used to identify liable Met residues. 22 , 59 Yang and colleagues used the SASA of Met side chains derived from a random forest model to identify liable Met residues for 121 antibodies. 67 They observed a strong correlation between side-chain solvent exposure and experimental Met oxidation, but there were some false negatives.…”

“…More recent studies have explored in silico prediction for Met oxidation under light stress and forced oxidation by AAPH. 22 , 59 The mechanism for Met oxidation by peroxides and photolysis is different. Hydrogen peroxide oxidizes Met residues through nucleophilic substitution, whereas light exposure oxidizes Met by generating free radicals.…”

“…Neighboring aromatic residues can prevent Met oxidation by acting as scavengers for free radicals. 59 Forced oxidation by H 2 O 2 mimics exposure to trace peroxides from polysorbates in formulations, whereas photolysis is meant to mimic degradation due to light exposure during shipping and storage. 71 Therefore, different predictors for Met oxidation (e.g., WCN) should be selected for different types of forced oxidation (e.g., peroxides and photolysis).…”

Protein folding stabilities are a major determinant of oxidation rates for buried methionine residues

“…For example, a significant fraction of methionines are located near aromatic residues, which have been shown to reduce methionine oxidation rates in vivo (31,34). The fact that SA is a strong global predictor of methionine oxidation is supported by a number of studies showing that exposed methionines are typically oxidized faster than buried methionines (27,28,35,36). Indeed, this correlation has allowed methionine oxidation to be used as an experimental biochemical probe to monitor structural changes in proteins (37).…”