Computational design of an amidase by combining the best electrostatic features of two promiscuous hydrolases

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

Exploring the Structurally Conserved Regions and Functional Significance in Bacterial N-Terminal Nucleophile (Ntn) Amide-Hydrolases

The initial adoption of penicillin as an antibiotic marked the start of exploring other compounds essential for pharmaceuticals, yet resistance to penicillins and their side effects has compromised their efficacy. The N-terminal nucleophile (Ntn) amide-hydrolases S45 family plays a key role in catalyzing amide bond hydrolysis in various compounds, including antibiotics like penicillin and cephalosporin. This study comprehensively analyzes the structural and functional traits of the bacterial N-terminal nucleophile (Ntn) amide-hydrolases S45 family, covering penicillin G acylases, cephalosporin acylases, and D-succinylase. Utilizing structural bioinformatics tools and sequence analysis, the investigation delineates structurally conserved regions (SCRs) and substrate binding site variations among these enzymes. Notably, sixteen SCRs crucial for substrate interaction are identified solely through sequence analysis, emphasizing the significance of sequence data in characterizing functionally relevant regions. These findings introduce a novel approach for identifying targets to enhance the biocatalytic properties of N-terminal nucleophile (Ntn) amide-hydrolases, while facilitating the development of more accurate three-dimensional models, particularly for enzymes lacking structural data. Overall, this research advances our understanding of structure-function relationships in bacterial N-terminal nucleophile (Ntn) amide-hydrolases, providing insights into strategies for optimizing their enzymatic capabilities.

Computational study of the mechanism of a polyurethane esterase A (PueA) from Pseudomonas chlororaphis

The effective management of plastic waste has become a global imperative, given our reliance on a linear model in which plastics are manufactured, used once, and then discarded. This has led to the pervasive accumulation of plastic debris in landfills and environmental contamination. Recognizing this issue, numerous initiatives are underway to address the environmental repercussions associated with plastic disposal. In this study, we investigate the possible molecular mechanism of polyurethane esterase A (PueA), which has been previously identified as responsible for the degradation of a polyester polyurethane (PU) sample in Pseudomonas chlororaphis, as an effort to develop enzymatic biodegradation solutions. After generating the unsolved 3D structure of the protein by AlphaFold2 from its known genome, the enzymatic hydrolysis of the same model PU compound previously used in experiments has been explored employing QM/MM molecular dynamics simulations. This required a preliminary analysis of the 3D structure of the apo-enzyme, identifying the putative active site, and the search for the optimal protein-substrate binding site. Finally, the resulting free energy landscape indicates that wild-type PueA can degrade PU chains, although with low-level activity. The reaction takes place by a characteristic four-step path of the serine hydrolases, involving an acylation followed by a diacylation step. Energetics and structural analysis of the evolution of the active site along the reaction suggests that PueA can be considered a promising protein scaffold for further development to achieve efficient biodegradation of PU.

Electrostatics as a Guiding Principle in Understanding and Designing Enzymes

Enzyme design faces challenges related to the implementation of the basic principles that govern the catalytic activity in natural enzymes. In this work, we revisit basic electrostatic concepts that have been shown to explain the origin of enzymatic efficiency like preorganization and reorganization. Using magnitudes such as the electrostatic potential and the electric field generated by the protein, we explain how these concepts work in different enzymes and how they can be used to rationalize the consequences of point mutations. We also discuss examples of protein design in which electrostatic effects have been implemented. For the near future, molecular simulations, coupled with the use of machine learning methods, can be used to implement electrostatics as a guiding principle for enzyme designs.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions

In enzyme mechanistic studies and mutant design, it is highly desirable to know the individual residue contributions to the reaction free energy and barrier. In this work, we show that such free energy contributions from each residue can be readily obtained by postprocessing ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulation trajectories. Specifically, through a mean force integration along the minimum free energy pathway, one can obtain the electrostatic, polarization, and van der Waals contributions from each residue to the free energy barrier. Separately, a similar analysis procedure allows us to assess the contribution from different collective variables along the reaction coordinate. The chorismate mutase reaction is used to demonstrate the utilization of these two trajectory analysis tools.

Mechanistic studies of a lipase unveil effect of pH on hydrolysis products of small PET modules

Biocatalysis is a key technology enabling plastic recycling. However, despite advances done in the development of plastic-degrading enzymes, the molecular mechanisms that govern their catalytic performance are poorly understood, hampering the engineering of more efficient enzyme-based technologies. In this work, we study the hydrolysis of PET-derived diesters and PET trimers catalyzed by the highly promiscuous lipase B from Candida antarctica (CALB) through QM/MM molecular dynamics simulations supported by experimental Michaelis-Menten kinetics. The computational studies reveal the role of the pH on the CALB regioselectivity toward the hydrolysis of bis-(hydroxyethyl) terephthalate (BHET). We exploit this insight to perform a pH-controlled biotransformation that selectively hydrolyzes BHET to either its corresponding diacid or monoesters using both soluble and immobilized CALB. The discoveries presented here can be exploited for the valorization of BHET resulting from the organocatalytic depolymerization of PET.

Modern non-polarizable force fields diverge in modeling the enzyme-substrate complex of a canonical serine protease

Classical molecular dynamics simulation is a powerful and established method of modern computational chemistry. Being able to obtain accurate information on molecular behavior is crucial to get valuable insights into structure-function relationships that translate into fundamental findings and practical applications. Active sites of enzymes are known to be particularly intricate, therefore, simpler non-polarizable force fields may provide an inaccurate description. In this work, we addressed this hypothesis in a case of a canonical serine triad protease trypsin in its complex with a substrate-mimicking inhibitor. We tested six modern and popular force fields to find that significantly diverging results may be obtained. Amber FB-15 and OPLS-AA/M turned out to model the active site incorrectly. Amber ff19sb and ff15ipq demonstrated mixed performance. The best performing force fields were CHARMM36m and Amber ff99sb-ildn, therefore, they are recommended for use with this and related systems. We speculate that a similar lack of cross-force field convergence may be characteristic of other enzymatic systems. Therefore, we advocate for careful consideration of different force fields in any study within the field of computational enzymology.