Tunnel Architectures in Enzyme Systems that Transport Gaseous Substrates

Enzyme Tunnel Dynamics and Catalytic Mechanism of Norcoclaurine Synthase: Insights from a Combined LiGaMD and DFT Study

This study conducts a systematic investigation into the catalytic mechanism of norcoclaurine synthase (NCS), a key enzyme in the biosynthesis of tetrahydroisoquinolines (THIQs) with therapeutic applications. By integration of LiGaMD and DFT calculations, the reaction pathway of NCS is mapped, providing detailed insights into its catalytic activity and selectivity. Our findings underscore the critical role of E103 in substrate capture and reveal the hitherto unappreciated influence of nonpolar residues M183 and L76 on tunnel dynamics. A prominent discovery is the identification of a high-energy barrier (44.2 kcal/mol) associated with the aromatic electrophilic attack, which pinpoints the rate-limiting step. Moreover, we disclose the existence of dual transition states leading to different products with the energetically favored six-membered ring formation consistent with experimental evidence. These mechanistic revelations not only refine our understanding of NCS but also advocate for a renewed emphasis on enzyme tunnel engineering for optimizing THIQs biosynthesis. The research sets the stage for translating these findings into practical enzyme modifications. Our results highlight the potential of NCS as a biocatalyst to overcome the limitations of current synthetic methodologies, such as low yields and environmental impacts, and provide a theoretical contribution to the efficient, eco-friendly production of THIQs-based pharmaceuticals.

Reinforcing Tunnel Network Exploration in Proteins Using Gaussian Accelerated Molecular Dynamics

Tunnels are structural conduits in biomolecules responsible for transporting chemical compounds and solvent molecules from the active site. They have been shown to be present in a wide variety of enzymes across all functional and structural classes. However, the study of such pathways is experimentally challenging, because they are typically transient. Computational methods, such as molecular dynamics (MD) simulations, have been successfully proposed to explore tunnels. Conventional MD (cMD) provides structural details to characterize tunnels but suffers from sampling limitations to capture rare tunnel openings on longer time scales. Therefore, in this study, we explored the potential of Gaussian accelerated MD (GaMD) simulations to improve the exploration of complex tunnel networks in enzymes. We used the haloalkane dehalogenase LinB and its two variants with engineered transport pathways, which are not only well-known for their application potential but have also been extensively studied experimentally and computationally regarding their tunnel networks and their importance in multistep catalytic reactions. Our study demonstrates that GaMD efficiently improves tunnel sampling and allows the identification of all known tunnels for LinB and its two mutants. Furthermore, the improved sampling provided insight into a previously unknown transient side tunnel (ST). The extensive conformational landscape explored by GaMD simulations allowed us to investigate in detail the mechanism of ST opening. We determined variant-specific dynamic properties of ST opening, which were previously inaccessible due to limited sampling of cMD. Our comprehensive analysis supports multiple indicators of the functional relevance of the ST, emphasizing its potential significance beyond structural considerations. In conclusion, our research proves that the GaMD method can overcome the sampling limitations of cMD for the effective study of tunnels in enzymes, providing further means for identifying rare tunnels in enzymes with the potential for drug development, precision medicine, and rational protein engineering.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Development and experimental validation of 3D QSAR models for the screening of thyroid peroxidase inhibitors using integrated methods of computational chemistry

The intricate network of glands and organs that makes up the endocrine system. Hormones are used to regulate and synchronize the nervous and physiological systems. The agents which perturbate an endocrine system are called endocrine disruptors and they can eventually affect cellular proliferation and differentiation in target tissues. A subclass of endocrine disruptors known as thyroid disruptors (TDs) or thyroid disrupting chemicals (TDCs) influence the hypothalamo-pituitary-thyroid axis or directly interfere with thyroid function by binding to thyroid hormone receptors. Thyroid hormone levels in circulation are now included in more test guidelines (OECD TG 441, 407, 408, 414, 421/422, 443/416). Although these might be adequate to recognize thyroid adversity, they are unable to explain the underlying mechanism of action. Thyroid peroxidase (TPO) and sodium iodide symporter (NIS), two proteins essential in the biosynthesis of thyroid hormones, are well-accepted molecular targets for inhibition. The screening of a large number of molecules using high throughput screening (HTS) requires a minimum quantity of sample, cost, and time consuming. Whereas 3-dimensional quantitative structure-activity relationship (3D-QSAR) analysis can screen the TDCs before synthesizing a compound. In the present study, the human TPO (hTPO) and NIS (hNIS) structures were modelled using homology modeling and the quality of the structures was validated satisfactorily using MD simulation for 100ns. Further, 190 human TPO inhibitors with IC50 were curated from Comptox and docked with the modelled structure of TPO using D238, H239 and D240 centric grid. The binding conformation of a molecule with low binding energy was used as a reference and the rest other molecules were aligned after generating the possible conformers. The activity-stratified partition was performed for aligned molecules and training set (139), test set (51) were defined. The machine learning models such as k Nearest Neighbor (kNN) and Random Forest (RF) models were built and validated using external experimental dataset containing 10 molecules. Among the 10 molecules, all 10 molecules were identified as TPO inhibitors and demonstrated 100 % accuracy qualitatively. To confirm the selective TPO inhibition all 10 molecules were docked with the modelled structure of hNIS and the results have demonstrated the selective TPO inhibition.

Tunnel engineering of gas-converting enzymes for inhibitor retardation and substrate acceleration

Carbon monoxide dehydrogenase (CODH), formate dehydrogenase (FDH), hydrogenase (H2ase), and nitrogenase (N2ase) are crucial enzymatic catalysts that facilitate the conversion of industrially significant gases such as CO, CO2, H2, and N2. The tunnels in the gas-converting enzymes serve as conduits for these low molecular weight gases to access deeply buried catalytic sites. The identification of the substrate tunnels is imperative for comprehending the substrate selectivity mechanism underlying these gas-converting enzymes. This knowledge also holds substantial value for industrial applications, particularly in addressing the challenges associated with separation and utilization of byproduct gases. In this comprehensive review, we delve into the emerging field of tunnel engineering, presenting a range of approaches and analyses. Additionally, we propose methodologies for the systematic design of enzymes, with the ultimate goal of advancing protein engineering strategies.

Diverse strategies adopted by nature for regulating purine biosynthesis via fine-tuning of purine metabolic enzymes

Purine nucleotides, generated by de novo synthesis and salvage pathways, are essential for metabolism and act as building blocks of genetic material. To avoid an imbalance in the nucleotide pool, nature has devised several strategies to regulate/tune the catalytic performance of key purine metabolic enzymes. Here, we discuss some recent examples, such as stress-regulating alarmones that bind to select pathway enzymes, huge ensembles like dynamic metabolons and self-assembled filaments that highlight the layered fine-control prevalent in the purine metabolic pathway to fulfill requisite purine demands. Examples of enzymes that turn-on only under allosteric control, are regulated via long-distance communication that facilitates transient conduits have additionally been explored.