Progress in Prediction of Oxidation States of Cysteines via Computational Approaches

Predicting the oxidation states of cysteines computationally usually involves first extracting commonalities from the available data obtained by observation or statistical analysis, or utilizing artificial intelligence. Then the acquired knowledge is applied to sequences with the bonding states unknown. Accuracy as calculation was based on the percentage of correct predictions among all test cases. To date, artificial intelligence achieves the highest accuracy among all computational methods attempted. Here, the authors surveyed ?>15 research efforts published since the 1980s in predicting cysteine oxidation states via computational methods, from the initial attempt using neural networks to the present state of the art. Most of these research efforts have reported prediction accuracy, although some of them used datasets that differed from the others. The essential information used for the predictions was first summarized all to determine the factors impacting the problem.