Skip to content
#protein folding Open access

Engineered Phenylalanine Ammonia Lyases Enable Biocatalytic Synthesis of a Non-Canonical Phenylalanine Derivative at Scale.

Oct 2026 · ChemRxiv
Enzyme Catalysis and Immobilization

Abstract

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.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

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. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

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...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.