A small-sample, accelerated evolution strategy that integrates focused rational iterative site-specific mutagenesis (FRISM) with the EVOLVEpro model is reported, providing a robust, "lightweight" machine learning framework for the rapid development of new-to-nature photoenzymatic transformations.
Abstract
gem-Difluorophosphonates are pivotal structural motifs in pharmaceuticals and bioactive molecules. While photoenzymatic catalysis provides a powerful platform to overcome the challenges of enantioselective synthesis, engineering enzymes for non-natural transformations remains an arduous, labor-intensive process. Although predictive methods utilizing protein language models (PLMs) offer fitness landscape guidance, they often struggle to generalize across diverse protein families or accurately map sequence to catalytic activity. Here, we report a small-sample, accelerated evolution strategy that integrates focused rational iterative site-specific mutagenesis (FRISM) with the EVOLVEpro model. This synergistic approach identifies high-activity and enantiospecific variants through structure-based hotspot identification and active learning, requiring minimal experimental throughput. By screening only 40 variants over three evolutionary rounds, we identified four beneficial mutations whose combinations enable the synthesis of diverse fluorinated products with up to > 99% yield and 98:2 enantiomeric ratio (e.r.)-a 65% reduction in workload compared to exhaustive screening. Mechanistic investigations suggest an electron donor-acceptor (EDA)-complex-free radical addition pathway, terminated by the flavin semiquinone (FMNsq) or the active-site residue Y343. This study provides a robust, "lightweight" machine learning framework for the rapid development of new-to-nature photoenzymatic transformations.
Enzyme enantioselectivity remains a central challenge in the biocatalytic synthesis of chiral pharmaceuticals and fine chemicals. Conventional evolution methods, constrained by prolonged experimental cycles and computationally intensive processes, struggle to comprehensively explore complex protein sequences. Although machine learning (ML) has been successfully applied to optimize enzymatic properties, such as catalytic efficiency and stability, its potential in enantioselectivity engineering remains underexploited. This review systematically evaluates how machine learning integrates multimodal datasets, including sequence, structural, and functional performance data, to advance the discovery and engineering of naturally enantioselective enzymes, and enable the de novo design of artificial enzymes for reaction-relevant applications. Furthermore, key algorithmic frameworks are reviewed. Persistent challenges such as data heterogeneity and limited model generalization capabilities are also critically examined. Moreover, machine learning is increasingly expected to bridge molecular-level enantioselective design with pathway optimization and reactor-scale process engineering, enabling a more integrated and efficient chiral biomanufacturing pipeline. Overall, these advancements open new avenues for efficient chiral biosynthesis and provide theoretical foundations and technical blueprints for a paradigm shift toward intelligence-driven biocatalysis engineering.
Jie Gu, Yan Xu, Xiaoyan Sun et al.· ACS Synthetic Biology· 0 citations
Results indicate that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope, and suggest that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope.
Ravi G. Lal, Jason Yang, Ziyan Zhang et al.· bioRxiv· 0 citations
TRACER (terpene rearrangement annotation via co-attentive enzyme-product representation), a multimodal framework mapping the latent associations between sequence-derived enzyme representations and product chemotypes, establishes a predictive paradigm for the rational discovery and mechanistic elucidation of complex terpene architectures.
A systematic comparison of zero-shot ML models is provided and an iterative framework for integrating machine learning with directed evolution to accelerate enzyme engineering is established to accelerate enzyme engineering.
Daniel Gutierrez, Isa Madrigal Harrison, Aaron L. Feller et al.· bioRxiv· 0 citations
A machine-learning-assisted enzyme-engineering (MLEE) workflow that adds substrate-specific functional information to htFuncLib through an initial screening and sequencing round that may bypass the need for transition-state models and reduce the effort required for obtaining high-activity variants.
Li Wan, Mahdi Bagherpoor Helabad, Lena Fraedrich et al.· bioRxiv· 0 citations
Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual compounds rather than on accelerated broad-range exploration. In this work, we first evaluate the synthesizability and band gaps of 909 hypothetical ternary phosphosulfides by density functional theory. We find 19 previously unknown thermodynamically stable compounds, including the first Si- and Ge-based phosphosulfides. For rapid band gap prediction, we then develop a multi-fidelity machine learning model to translate semilocal density functional theory band gaps into experimentally calibrated band gaps. Importantly, we extend the accelerated material development workflow to the experimental domain by demonstrating a route to high-throughput synthesis and characterization of virtually any phosphosulfide material system. The method is based on thin-film combinatorial libraries and yields over 100 unique compositions in each experiment, enabling us to synthesize four distinct phosphosulfide compounds in only four combinatorial experiments without prior synthesis recipes and without compromising on material quality. Thus, we argue that accelerated materials development workflows combining theory, artificial intelligence, synthesis, and characterization can be viable even for experimentally challenging inorganic materials.
J. Sanz Rodrigo, Nicholas A. Kryger-Nelson, Lena A. Mittmann et al.· Small· 1 citation