Protein asparagine deamidation prediction based on structures with machine learning methods

• Published in: PloS one Vol. 12; no. 7; p. e0181347
• Main Authors: Jia, Lei, Sun, Yaxiong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.07.2017; Public Library of Science (PLoS)
• Subjects: Acids; Algorithms; Amides - chemistry; Amino acid sequence; Amino acids; Artificial intelligence; Asparagine; Asparagine - chemistry; Biochemistry; Bioengineering; Biology and Life Sciences; Computer and Information Sciences; Computer Simulation; Corrosion resistance; Crystal structure; Crystallography; Data Mining; Data processing; Datasets; Drug development; Drug discovery; Engineering; Glycine; Glycosylation; High resolution; Hot spots; Immunoglobulins; Isomerization; Learning; Learning algorithms; Machine learning; Mathematical models; Methods; Models, Statistical; Molecular Structure; Neural networks; Oxidation; Peptides; Performance prediction; Physical Sciences; Prediction models; Protein engineering; Protein structure; Proteins; Quantum physics; Ranking; Research and Analysis Methods; Residues; ROC Curve; Safety engineering; Secondary structure; Software; Solvents - chemistry; Structural stability; Structure-function relationships; Supervised Machine Learning; Teaching methods; Taiwan

Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc....