Molecular Mechanism of Methanol Inhibition in CALB-Catalyzed Alcoholysis: Analyzing Molecular Dynamics Simulations by a Markov State Model

N-glycosylation-modifications-driven conformational dynamics attenuate substrate inhibition of d-lactonohydrolase

Achieving enzyme catalysis at high substrate concentrations is a substantial challenge in industrial biocatalysis, and the role of glycosylation in post-translational modifications that modulate enzyme substrate inhibition remains poorly understood. This study provides insights into the role of N-glycosylation in substrate inhibition by comparing the catalytic properties of d-lactonohydrolase (d-Lac) derived from Fusarium moniliforme expressed in prokaryotic and eukaryotic hosts. Experimental evidence indicates that recombinant d-Lac expressed in Pichia pastoris (PpLac-WT) exhibits higher hydrolysis rates at a substrate concentration of 400 g/L, with reduced substrate inhibition and enhanced stability compared to the recombinant d-Lac expressed in Escherichia coli (EcLac-WT). Mutant PpLac-M1 achieves a conversion rate of 40 % at a substrate concentration of 400 g/L, with a space-time yield of d-pantoic acid reaching 91.1 g/L/h. Proteomics analysis reveals that residues N29 and N278, located approximately 10-20 Å from the active site undergo N-glycosylation in PpLac-WT. Using microsecond-scale molecular dynamics simulations and Markov state models, we elucidate the effects of glycosylation on the conformational flexibility of two key loops at the entrance of the binding pocket. Specifically, the loops in PpLac-WT can transition between open and closed states, whereas those in EcLac-WT tend to remain open. In high substrate concentration conditions, the open state causes congestion, leading to substrate inhibition. Shortest-path map analysis confirms that substrate entry is dynamically controlled by residue N29 on the loops surrounding the active site. Our findings enhance the understanding of the effects of glycosylation on enzyme conformational dynamics and provide insights into mitigating inhibition at high substrate concentrations.

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau protein is a promising avenue for tauopathies therapy. To dissect the underlying aggregation mechanisms on Tau, we employ a set of molecular simulations and the Markov state model to determine the kinetics of ensemble of K18. K18 is the microtubule-binding domain of Tau protein and plays a vital role in the microtubule assembly, recycling processes, and amyloid fibril formation. Here, we efficiently explore the conformation of K18 with about 150 μs lifetimes in silico. Our results observe that all four repeat regions (R1-R4) are very dynamic, featuring frequent conformational conversion and lacking stable conformations, and the R2 region is more flexible than the R1, R3, and R4 regions. Additionally, it is worth noting that residues 300-310 in R2-R3 and residues 319-336 in R3 tend to form sheet structures, indicating that K18 has a broader functional role than individual repeat monomers. Finally, the simulations combined with Markov state models and deep learning reveal 5 key conformational states along the transition pathway and provide the information on the microsecond time scale interstate transition rates. Overall, this study offers significant insights into the molecular mechanism of Tau pathological aggregation and develops novel strategies for both securing tauopathies and advancing drug discovery.

Short-chain alcohols inactivate an immobilized industrial lipase through two different mechanisms

Broadly used in biocatalysis as acyl acceptors or (co)-solvents, short-chain alcohols often cause irreversible loss of enzyme activity. Understanding the mechanisms of inactivation is a necessary step toward the optimization of biocatalytic reactions and the design of enzyme-based sustainable processes. In this work, we explored the functional and structural response of an immobilized enzyme, Novozym 435, exposed to methanol, ethanol, and tert-butanol. N-435 consists of Candida antarctica lipase B (CALB) adsorbed on polymethacrylate beads and finds application in a variety of processes involving the presence of short-chain alcohols. The nature of the N-435 material required the development of an ad hoc method of structural analysis, based on Fourier transform infrared microspectroscopy, which was complemented by catalytic activity assays and by morphological observation by transmission electron microscopy. We found that the inactivation of N-435 is highly dependent on alcohol concentration and occurs through two different mechanisms. Short-chain alcohols induce conformational changes leading to CALB aggregation, which is only partially prevented by immobilization. Moreover, alcohol modifies the texture of the solid support promoting the enzyme release. Overall, knowledge of the molecular mechanisms underlying Novozym 435 inactivation induced by short-chain alcohols promises to overcome the limitations that usually occur during industrial processes. This article is protected by copyright. All rights reserved.

Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B

The binding free-energy profile of methanol to Candida antarctica lipase B (CALB) was calculated at infinite dilution and at a finite methanol concentration of 6.1 M using umbrella sampling molecular dynamics simulations with the OPLS all-atom force field. An additional validation of the results was performed by employing alchemical double decoupling simulations. The binding free-energy profiles have been used in a related research article to validate free-energy profiles obtained from direct counting simulations with the aim to use the kinetic information encoded in the latter. The data provided in this work will be useful to study concentration effects on binding, to test alternative free energy methods or to use the proposed simulation protocol for related systems.