期刊
MABS
卷 14, 期 1, 页码 -出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/19420862.2022.2143006
关键词
Chemical degradation; deamidation; isomerization; developability; proton affinity; therapeutic proteins; molecular modeling
In this study, the mechanisms of spontaneous degradation of Asn and Asp residues in 131 clinical-stage therapeutic antibodies were investigated using proton-affinity calculations and molecular dynamics simulations. The secondary structure of the backbone, the conformation of the side-chain rotamer, and the solvent accessibility were found to be key indicators of Asp isomerization and Asn deamidation. A mechanistic explanation for the influence of the identity of the n + 1 residue on the rate of degradation was proposed based on the analysis of backbone dihedral angles and N-H proton affinity calculations. Based on these findings, a physics-based classification model was proposed to predict the propensity of deamidation and isomerization in proteins.
Deamidation of asparagine (Asn) and isomerization of aspartic acid (Asp) residues are among the most commonly observed spontaneous post-translational modifications (PTMs) in proteins. Understanding and predicting a protein sequence's propensity for such PTMs can help expedite protein therapeutic discovery and development. In this study, we used proton-affinity calculations with semi-empirical quantum mechanics and microsecond long equilibrium molecular dynamics simulations to investigate mechanistic roles of structural conformation and chemical environment in dictating spontaneous degradation of Asn and Asp residues in 131 clinical-stage therapeutic antibodies. Backbone secondary structure, side-chain rotamer conformation and solvent accessibility were found to be key molecular indicators of Asp isomerization and Asn deamidation. Comparative analysis of backbone dihedral angles along with N-H proton affinity calculations provides a mechanistic explanation for the strong influence of the identity of the n + 1 residue on the rate of Asn/Asp degradation. With these findings, we propose a minimalistic physics-based classification model that can be leveraged to predict deamidation and isomerization propensity of proteins.
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