Phenylalanine ammonia lyases (PALs) are an enzyme class of high synthetic utility as they catalyze the synthesis of chiral non-canonical phenylalanine amino acids from readily available cinnamic acids using simple ammonia as the amine donor. However, the applications of PAL in chemical manufacturing have been rare due to the challenges in identifying PALs active under industrially relevant conditions. Herein, we report our efforts towards engineering a PAL enzyme from Planctomyces brasiliensis (PbPAL) for the synthesis of L-3-cyanophenylalanine as an intermediate in the synthesis of Enlicitide, the clinically approved macrocyclic peptide to lower cholesterol levels. Protein engineering identified several key mutations that resulted in an evolved PbPAL variant with >100-fold improved activity and enhanced recombinant expression in E. coli. The computational studies suggest the final variant has increased substrate accessibility to the active site, which enabled high conversion (up to 97%) to L-3-cyanophenylalanine with excellent enantioselectivity at low enzyme loading (3 w/w%), demonstrated at up to 250-gram scale. In addition, the final variant represents a broad substrate scope against a variety of heterocyclic cinnamic acids with high enantioselectivity. This work showcases how protein engineering can enable the biocatalytic synthesis of non-canonical amino acids at scale.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.