Single-Particle Tracking of Thermomyces lanuginosus Lipase Reveals How Mutations in the Lid Region Remodel Its Diffusion.

The function of most lipases is controlled by the lid, which undergoes conformational changes at a water-lipid interface to expose the active site, thus activating catalysis. Understanding how lid mutations affect lipases' function is important for designing improved variants. Lipases' function has been found to correlate with their diffusion on the substrate surface. Here, we used single-particle tracking (SPT), a powerful tool for deciphering enzymes' diffusional behavior, to study Thermomyces lanuginosus lipase (TLL) variants with different lid structures in a laundry-like application condition. Thousands of parallelized recorded trajectories and hidden Markov modeling (HMM) analysis allowed us to extract three interconverting diffusional states and quantify their abundance, microscopic transition rates, and the energy barriers for sampling them. Combining those findings with ensemble measurements, we determined that the overall activity variation in the application condition is dependent on surface binding and lipase mobility when bound. Specifically, the L4 variant with a TLL-like lid and wild-type (WT) TLL displayed similar ensemble activity, but WT bound stronger to the surface than L4, while L4 had a higher diffusion coefficient and thus activity when bound to the surface. These mechanistic elements can only be de-convoluted by our combined assays. Our findings offer fresh perspectives on the development of the next iteration of enzyme-based detergent.

Single-Molecule Study of Thermomyces lanuginosus Lipase in a Detergency Application System Reveals Diffusion Pattern Remodeling by Surfactants and Calcium.

Lipases comprise one of the major enzyme classes in biotechnology with applications within, e.g., baking, brewing, biocatalysis, and the detergent industry. Understanding the mechanisms of lipase function and regulation is therefore important to facilitate the optimization of their function by protein engineering. Advances in single-molecule studies in model systems have provided deep mechanistic insights on lipase function, such as the existence of functional states, their dependence on regulatory cues, and their correlation to activity. However, it is unclear how these observations translate to enzyme behavior in applied settings. Here, single-molecule tracking of individual Thermomyces lanuginosus lipase (TLL) enzymes in a detergency application system allowed real-time direct observation of spatiotemporal localization, and thus diffusional behavior, of TLL enzymes on a lard substrate. Parallelized imaging of thousands of individual enzymes allowed us to observe directly the existence and quantify the abundance and interconversion kinetics between three diffusional states that we recently provided evidence to correlate with function. We observe redistribution of the enzyme's diffusional pattern at the lipid-water interface as well as variations in binding efficiency in response to surfactants and calcium, demonstrating that detergency effectors can drive the sampling of lipase functional states. Our single-molecule results combined with ensemble activity assays and enzyme surface binding efficiency readouts allowed us to deconvolute how application conditions can significantly alter protein functional dynamics and/or surface binding, both of which underpin enzyme performance. We anticipate that our results will inspire further efforts to decipher and integrate the dynamic nature of lipases, and other enzymes, in the design of new biotechnological solutions.

Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion.

Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term "diffusional fingerprinting." This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting's utility as a universal paradigm for SPT diffusional analysis and prediction.