Novel recombinant aminoacylase from Paraburkholderia monticola capable of N-acyl-amino acid synthesis.

N-Acyl-amino acids can act as mild biobased surfactants, which are used, e.g., in baby shampoos. However, their chemical synthesis needs acyl chlorides and does not meet sustainability criteria. Thus, the identification of biocatalysts to develop greener synthesis routes is desirable. We describe a novel aminoacylase from Paraburkholderia monticola DSM 100849 (PmAcy) which was identified, cloned, and evaluated for its N-acyl-amino acid synthesis potential. Soluble protein was obtained by expression in lactose autoinduction medium and co-expression of molecular chaperones GroEL/S. Strep-tag affinity purification enriched the enzyme 16-fold and yielded 15 mg pure enzyme from 100 mL of culture. Biochemical characterization revealed that PmAcy possesses beneficial traits for industrial application like high temperature and pH-stability. A heat activation of PmAcy was observed upon incubation at temperatures up to 80 °C. Hydrolytic activity of PmAcy was detected with several N-acyl-amino acids as substrates and exhibited the highest conversion rate of 773 U/mg with N-lauroyl-L-alanine at 75 °C. The enzyme preferred long-chain acyl-amino-acids and displayed hardly any activity with acetyl-amino acids. PmAcy was also capable of N-acyl-amino acid synthesis with good conversion rates. The best synthesis results were obtained with the cationic L-amino acids L-arginine and L-lysine as well as with L-leucine and L-phenylalanine. Exemplarily, L-phenylalanine was acylated with fatty acids of chain lengths from C8 to C18 with conversion rates of up to 75%. N-lauroyl-L-phenylalanine was purified by precipitation, and the structure of the reaction product was verified by LC-MS and NMR. KEY POINTS: • A novel aminoacylase from Paraburkholderia monticola was cloned, expressed in E. coli and purified. • The enzyme PmAcy exhibits exceptional temperature and pH stability and a broad substrate spectrum. • Synthesis of acyl amino acids was achieved in good yields.

Molecular Mechanisms Underlying Medium-Chain Free Fatty Acid-Regulated Activity of the Phospholipase PlaF from Pseudomonas aeruginosa.

PlaF is a membrane-bound phospholipase A1 from Pseudomonas aeruginosa that is involved in remodeling membrane glycerophospholipids (GPLs) and modulating virulence-associated signaling and metabolic pathways. Previously, we identified the role of medium-chain free fatty acids (FFAs) in inhibiting PlaF activity and promoting homodimerization, yet the underlying molecular mechanism remained elusive. Here, we used unbiased and biased molecular dynamics simulations and free energy computations to assess how PlaF interacts with FFAs localized in the water milieu surrounding the bilayer or within the bilayer and how these interactions regulate PlaF activity. Medium-chain FFAs localized in the upper bilayer leaflet can stabilize inactive dimeric PlaF, likely through interactions with charged surface residues, as has been experimentally validated. Potential of mean force (PMF) computations indicate that membrane-bound FFAs may facilitate the activation of monomeric PlaF by lowering the activation barrier for changing into a tilted, active configuration. We estimated that the coupled equilibria of PlaF monomerization-dimerization and tilting at the physiological concentration of PlaF lead to the majority of PlaF forming inactive dimers when in a cell membrane loaded with decanoic acid (C10). This is in agreement with a suggested in vivo product feedback loop and gas chromatography-mass spectrometry profiling results, indicating that PlaF catalyzes the release of C10 from P. aeruginosa membranes. Additionally, we found that C10 in the water milieu can access the catalytic site of active monomeric PlaF, contributing to the competitive component of C10-mediated PlaF inhibition. Our study provides mechanistic insights into how medium-chain FFAs may regulate the activity of PlaF, a potential bacterial drug target.

Conserved Signal Transduction Mechanisms and Dark Recovery Kinetic Tuning in the Pseudomonadaceae Short Light, Oxygen, Voltage (LOV) Protein Family.

Light-Oxygen-Voltage (LOV) flavoproteins transduce a light signal into variable signaling outputs via a structural rearrangement in the sensory core domain, which is then relayed to fused effector domains via α-helical linker elements. Short LOV proteins from Pseudomonadaceae consist of a LOV sensory core and N- and C-terminal α-helices of variable length, providing a simple model system to study the molecular mechanism of allosteric activation. Here we report the crystal structures of two LOV proteins from Pseudomonas fluorescens - SBW25-LOV in the fully light-adapted state and Pf5-LOV in the dark-state. In a comparative analysis of the Pseudomonadaceae short LOVs, the structures demonstrate light-induced rotation of the core domains and splaying of the proximal A'α and Jα helices in the N and C-termini, highlighting evidence for a conserved signal transduction mechanism. Another distinguishing feature of the Pseudomonadaceae short LOV protein family is their highly variable dark recovery, ranging from seconds to days. Understanding this variability is crucial for tuning the signaling behavior of LOV-based optogenetic tools. At 37 °C, SBW25-LOV and Pf5-LOV exhibit adduct state lifetimes of 1470 min and 3.6 min, respectively. To investigate this remarkable difference in dark recovery rates, we targeted three residues lining the solvent channel entrance to the chromophore pocket where we introduced mutations by exchanging the non-conserved amino acids from SBW25-LOV into Pf5-LOV and vice versa. Dark recovery kinetics of the resulting mutants, as well as MD simulations and solvent cavity calculations on the crystal structures suggest a correlation between solvent accessibility and adduct lifetime.

Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime.

Naturally occurring and engineered flavin-binding, blue-light-sensing, light, oxygen, voltage (LOV) photoreceptor domains have been used widely to design fluorescent reporters, optogenetic tools, and photosensitizers for the visualization and control of biological processes. In addition, natural LOV photoreceptors with engineered properties were recently employed for optimizing plant biomass production in the framework of a plant-based bioeconomy. Here, the understanding and fine-tuning of LOV photoreceptor (kinetic) properties is instrumental for application. In response to blue-light illumination, LOV domains undergo a cascade of photophysical and photochemical events that yield a transient covalent FMN-cysteine adduct, allowing for signaling. The rate-limiting step of the LOV photocycle is the dark-recovery process, which involves adduct scission and can take between seconds and days. Rational engineering of LOV domains with fine-tuned dark recovery has been challenging due to the lack of a mechanistic model, the long time scale of the process, which hampers atomistic simulations, and a gigantic protein sequence space covering known mutations (combinatorial challenge). To address these issues, we used machine learning (ML) trained on scarce literature data and iteratively generated and implemented experimental data to design LOV variants with faster and slower dark recovery. Over the three prediction-validation cycles, LOV domain variants were successfully predicted, whose adduct-state lifetimes spanned 7 orders of magnitude, yielding optimized tools for synthetic (opto)biology. In summary, our results demonstrate ML as a viable method to guide the design of proteins even with limited experimental data and when no mechanistic model of the underlying physical principles is available.