Lipase A from Bacillus subtilis: Substrate Binding, Conformational Dynamics, and Signatures of a Lid

New insights into exploring new functional enzymes through the enzyme promiscuity

Enzyme promiscuity, defined as the ability of enzymes to catalyze reactions beyond their primary physiological functions, has emerged as a pivotal concept in modern enzyme engineering. This review provides a comprehensive exploration of enzyme promiscuity and its implications for the discovery and development of novel functional enzymes. Through targeted strategies such as (semi-)rational design, directed evolution, and de novo design, enzyme promiscuity has been harnessed to broaden substrate scopes, enhance catalytic efficiencies, and adapt enzymes to diverse reaction conditions. These modifications often involve subtle alterations to the active site, which impact catalytic mechanisms and open new pathways for the synthesis and degradation of complex organic compounds. Striking a balance between maintaining native activity and enhancing promiscuous functions remains a significant challenge in enzyme engineering. Nevertheless, advances in structural biology and computational modeling offer promising strategies to overcome these obstacles. By elucidating the mechanistic basis of enzyme promiscuity, this review aims to deepen our understanding of this phenomenon. It underscores the necessity of further investigating the mechanisms underlying promiscuous enzymatic activity and highlights the importance of leveraging promiscuous enzymes to address industrial application demands and drive the development of next-generation biocatalysts.

Lipase Activation by Poly(Methyl Methacrylate) in Dispersed Solution: Mechanistic Insights by Fluorescence Spectroscopy

We investigated the mechanisms of polymer-lipase interactions that govern the catalytic activity of lipases in the presence of polymers. Using a combination of fluorescence correlation spectroscopy (FCS), activity analysis, fluorescence spectroscopy, and computational surface analysis, three model lipases-Thermomyces lanuginosus lipase (TLL), Candida antarctica lipase B (CalB), and Bacillus subtilis lipase A (BSLA), with different degrees of hydrophobic active site exposure were studied. Low-molecular-weight poly(methyl methacrylate) (PMMA), synthesized via ARGET ATRP, was employed to study the effect of unstructured polymers in dispersed solution on lipase activity. PMMA significantly enhanced TLL and BSLA hydrolytic activity, while no CalB activation was observed. FCS analysis indicated that this activation was facilitated by polymer lipase binding, a phenomenon observed with TLL and BSLA but not with CalB. Computational analysis further revealed that the surface properties of the lipases were critical for the lipases' susceptibility to activation by PMMA. Although CalB exhibited the largest total hydrophobic surface area, its homogeneous distribution prevented activation, whereas strong, localized hydrophobic interactions allowed PMMA to bind and activate TLL and BSLA. Supported by the quantitative correlation between elevated 8-anilino-1-naphthalenesulfonic acid (ANS) fluorescence in the presence of PMMA and lipase activity, the activation was attributed to locally increased hydrophobicity of the lipases upon polymer binding. These findings provide critical insights into the role of polymer interactions in lipase activation and stabilization, highlighting the potential for designing tailored polymer carriers to optimize enzyme performance in industrial and biotechnological applications.

Structural Insights into Cold-Active Lipase from Glaciozyma antarctica PI12: Alphafold2 Prediction and Molecular Dynamics Simulation

Cold-active enzymes have recently gained popularity because of their high activity at lower temperatures than their mesophilic and thermophilic counterparts, enabling them to withstand harsh reaction conditions and enhance industrial processes. Cold-active lipases are enzymes produced by psychrophiles that live and thrive in extremely cold conditions. Cold-active lipase applications are now growing in the detergency, synthesis of fine chemicals, food processing, bioremediation, and pharmaceutical industries. The cold adaptation mechanisms exhibited by these enzymes are yet to be fully understood. Using phylogenetic analysis, and advanced deep learning-based protein structure prediction tool Alphafold2, we identified an evolutionary processes in which a conserved cold-active-like motif is presence in a distinct subclade of the tree and further predicted and simulated the three-dimensional structure of a putative cold-active lipase with the cold active motif, Glalip03, from Glaciozyma antarctica PI12. Molecular dynamics at low temperatures have revealed global stability over a wide range of temperatures, flexibility, and the ability to cope with changes in water and solvent entropy. Therefore, the knowledge we uncover here will be crucial for future research into how these low-temperature-adapted enzymes maintain their overall flexibility and function at lower temperatures.

Protein representations: Encoding biological information for machine learning in biocatalysis

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

Designing surface exposed sites on Bacillus subtilis lipase A for spin-labeling and hydration studies

Spin-labeling with electron paramagnetic resonance spectroscopy (EPR) is a facile method for interrogating macromolecular flexibility, conformational changes, accessibility, and hydration. Within we present a computationally based approach for the rational selection of reporter sites in Bacillus subtilis lipase A (BSLA) for substitution to cysteine residues with subsequent modification with a spin-label that are expected to not significantly perturb the wild-type structure, dynamics, or enzymatic function. Experimental circular dichroism spectroscopy, Michaelis-Menten kinetic parameters and EPR spectroscopy data validate the success of this approach to computationally select reporter sites for future magnetic resonance investigations of hydration and hydration changes induced by polymer conjugation, tethering, immobilization, or amino acid substitution in BSLA. Analysis of molecular dynamic simulations of the impact of substitutions on the secondary structure agree well with experimental findings. We propose that this computationally guided approach for choosing spin-labeled EPR reporter sites, which evaluates relative surface accessibility coupled with hydrogen bonding occupancy of amino acids to the catalytic pocket via atomistic simulations, should be readily transferable to other macromolecular systems of interest including selecting sites for paramagnetic relaxation enhancement NMR studies, other spin-labeling EPR studies or any method requiring a tagging method where it is desirable to not alter enzyme stability or activity.

Hydrophobic Clusters Regulate Surface Hydration Dynamics of Bacillus subtilis Lipase A

The surface hydration diffusivity of Bacillus subtilis Lipase A (BSLA) has been characterized by low-field Overhauser dynamic nuclear polarization (ODNP) relaxometry using a series of spin-labeled constructs. Sites for spin-label incorporation were previously designed via an atomistic computational approach that screened for surface exposure, reflective of the surface hydration comparable to other proteins studied by this method, as well as minimal impact on protein function, dynamics, and structure of BSLA by excluding any surface site that participated in greater than 30% occupancy of a hydrogen bonding network within BSLA. Experimental ODNP relaxometry coupling factor results verify the overall surface hydration behavior for these BSLA spin-labeled sites similar to other globular proteins. Here, by plotting the ODNP parameters of relative diffusive water versus the relative bound water, we introduce an effective "phase-space" analysis, which provides a facile visual comparison of the ODNP parameters of various biomolecular systems studied to date. We find notable differences when comparing BSLA to other systems, as well as when comparing different clusters on the surface of BSLA. Specifically, we find a grouping of sites that correspond to the spin-label surface location within the two main hydrophobic core clusters of the branched aliphatic amino acids isoleucine, leucine, and valine cores observed in the BSLA crystal structure. The results imply that hydrophobic clustering may dictate local surface hydration properties, perhaps through modulation of protein conformations and samplings of the unfolded states, providing insights into how the dynamics of the hydration shell is coupled to protein motion and fluctuations.