%0 Journal Article
%A Plotnikov, Nikolay
V.
%A Singh, Satish Kumar
%A Rouse, Jason C.
%A Kumar, Sandeep
%D 2017
%T Quantifying the Risks of Asparagine Deamidation and
Aspartate Isomerization in Biopharmaceuticals by Computing Reaction
Free-Energy Surfaces
%U https://acs.figshare.com/articles/journal_contribution/Quantifying_the_Risks_of_Asparagine_Deamidation_and_Aspartate_Isomerization_in_Biopharmaceuticals_by_Computing_Reaction_Free-Energy_Surfaces/4565332
%R 10.1021/acs.jpcb.6b11614.s001
%2 https://acs.figshare.com/ndownloader/files/7396258
%K degradation sites
%K GDG sequence motifs
%K N 55 G
%K Several knowledge-based models
%K D 102 G
%K N 30 T
%K accessing reactive conformations
%K Reaction Free-Energy Surfaces
%K reference reactions
%K chemical reaction pathway
%K D 95 K
%K degradation rates
%K chemical degradation sites
%K DK
%K GNG
%K glial cell-derived neurotropic factor
%K aspartate isomerization degradation sites
%X Early
identification of asparagine deamidation and aspartate isomerization
degradation sites can facilitate the successful development of biopharmaceuticals.
Several knowledge-based models have been proposed to assess these
degradation risks. In this study, we propose a physics-based approach
to identify the degradation sites on the basis of the free-energy
barriers along the prechemical conformational step and the chemical
reaction pathway. These contributions are estimated from classical
and quantum mechanics/molecular mechanics molecular dynamics simulations.
The computed barriers are compared to those for reference reactions
in water within GNG and GDG sequence motifs in peptides (which demonstrate
the highest degradation rates). Two major factors decreasing the degradation
rates relative to the reference reactions are steric hindrance toward
accessing reactive conformations and replacement of water by less
polar side chains in the solvation shell of transition states. Among
the potential degradation sites in the complementarity-determining
region of trastuzumab and between two DK sites in glial cell-derived
neurotropic factor, this method identified N30T, N55G, D102G, and D95K, respectively, in
agreement with experiments. This approach can be incorporated in early
computational screening of chemical degradation sites in biopharmaceuticals.
%I ACS Publications