In:
ChemBioChem, Wiley, Vol. 22, No. 5 ( 2021-03-02), p. 904-914
Abstract:
Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside‐the‐box, predictions not found in other state‐of‐the‐art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.
Type of Medium:
Online Resource
ISSN:
1439-4227
,
1439-7633
DOI:
10.1002/cbic.202000612
Language:
English
Publisher:
Wiley
Publication Date:
2021
detail.hit.zdb_id:
2020469-3
SSG:
12