In Silico Prediction Method for Protein Asparagine Deamidation

Abstract

In silico prediction methods were developed to predict protein asparagine (Asn) deamidation. The method is based on understanding deamidation mechanism on structural level with machine learning. Our structure-based method is more accurate than the sequence-based method which is still widely used in protein engineering process. In addition, molecular dynamics simulation was applied to study the time occupancy of nucleophilic attack distance, which is hypothesized as the most important step toward the rate-limiting succinimide intermediate formation. A more accurate prediction method for distinguishing potentially liable amino acid residues would allow their elimination or reduction as early as possible in the drug discovery process. It is possible that such quantitative protein structure-property relationship tools can also be applied to other protein hotspot predictions.

Keywords: Feature selection; Hotspot; Machine learning; Molecular dynamics; Prediction; Protein deamidation; Protein engineering; Structure.