TL;DR
The potential of machine learning–assisted protein engineering is demonstrated and an α2,6-SiaT variant with high expression levels for the biological synthesis of 6′-SL is provided.
The utility of ML-assisted evolution for engineering Rubisco with improved carboxylation efficiency and potential for enhancing crop productivity is demonstrated.
This work establishes the first deep learning-enabled RBS design platform for P. denitrificans, offering a robust tool for precise translational regulation and advancing synthetic biology applications in environmental biotechnology.
The rotary motor F1-ATPase has been extensively studied as a model molecular machine, yet rational engineering of its catalytic activity remains challenging because ATP hydrolysis is regulated by long-range intersubunit allostery and large conformational transitions. Here, we developed a homolog-guided engineering strategy to increase the maximum rotation rate of the thermophilic Bacillus PS3 F1-ATPase (TF1). Candidate mutation sites were first identified by comparing TF1 with the homologous enzymes bovine mitochondrial F1 (bMF1) and Paracoccus denitrificans F1 (PdF1), both of which exhibit higher maximum rotation rates than TF1. Systematic exploration of these sites identified four activity-enhancing hotspots, followed by focused hotspot exploration and machine-learning-assisted prioritization of combinatorial mutants. The best mutant, TF1(βY313L/βE332S), exhibited a 1.8-fold higher maximum rotation rate than TF1(WT) while retaining its functional thermostability. Interestingly, activity-enhancing substitutions were not limited to the residues conserved in both bMF1 and PdF1, indicating that the bMF1–PdF1 consensus substitutions effectively identify activity-enhancing hotspots rather than uniquely defining the optimal amino acid. Machine-learning-assisted exploration efficiently prioritized highly active mutants, although the predictive performance was limited by the relatively small training dataset and epistatic interactions among mutations. Kinetic and structural comparisons further provided mechanistic insights into the enhanced catalytic activity of the engineered mutant. Together, these results establish a practical strategy for engineering complex molecular motors by combining homolog-guided hotspot identification with focused hotspot exploration.
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.
Dalbavancin is a potent next-generation lipoglycopeptide antibiotic, but the biosynthesis of its essential precursor, A40926B0, is severely hindered in its native producer Nonomuraea gerenzanensis. Complex competing metabolic pathways and strict transcriptional repression in this strain lead to critical industrial bottlenecks, including extreme product heterogeneity and notoriously low yields. To address these challenges, this study presents a systematic strategy to construct a high-producing strain and optimize the fermentation process for A40926B0. First, random mutagenesis of the strain N. gerenzanensis IPB-9 was performed using a 10 MeV high-energy electron linear accelerator. Screening identified mutant M0318, achieving a 43.6% increase in A40926B0 titer (808.8 mg/L). Concurrently, RNA-Seq analysis of N. gerenzanensis L70 was processed and seven endogenous strong promoters were quantitatively characterized and validated by using an eGFP reporter system and RT-qPCR to expand the genetic toolbox. Applying this toolkit, the strongest identified promoter (orf1597*p) was engineered to overexpress dbv3, the core pathway-specific positive regulator, in the M0318 chassis. This rational intervention further boosted A40926B0 production to 1.03 g/L. Furthermore, a comprehensive bioprocess optimization was executed. Following single-factor evaluations of shake-flask parameters, a Box-Behnken design (BBD)-based response surface methodology (RSM) was employed for systematic medium formulation, successfully elevating the titer to 1.41 g/L. Finally, this optimized process was successfully scaled to a 15 L bioreactor and the final A40926B0 titer reached 2.27 g/L, representing a 303.2% increase over the initial process, laying a foundation for the industrial-scale production of A40926B0.
