Probing the Transition State in Enzyme Catalysis by High-Pressure NMR Dynamics

Switching Go̅-Martini for Investigating Protein Conformational Transitions and Associated Protein-Lipid Interactions

Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport.

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.

Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. By applying generative models, we could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of enzyme activity and stability using natural enzyme sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

Insights into the Structure of Invisible Conformations of Large Methyl Group Labeled Molecular Machines from High Pressure NMR

Most proteins are highly flexible and can adopt conformations that deviate from the energetically most favorable ground state. Structural information on these lowly populated, alternative conformations is often lacking, despite the functional importance of these states. Here, we study the pathway by which the Dcp1:Dcp2 mRNA decapping complex exchanges between an autoinhibited closed and an open conformation. We make use of methyl Carr-Purcell-Meiboom-Gill (CPMG) NMR relaxation dispersion (RD) experiments that report on the population of the sparsely populated open conformation as well as on the exchange rate between the two conformations. To obtain volumetric information on the open conformation as well as on the transition state structure we made use of RD measurements at elevated pressures. We found that the open Dcp1:Dcp2 conformation has a lower molecular volume than the closed conformation and that the transition state is close in volume to the closed state. In the presence of ATP the volume change upon opening of the complex increases and the volume of the transition state lies in-between the volumes of the closed and open state. These findings show that ATP has an effect on the volume changes that are associated with the opening-closing pathway of the complex. Our results highlight the strength of pressure dependent NMR methods to obtain insights into structural features of protein conformations that are not directly observable. As our work makes use of methyl groups as NMR probes we conclude that the applied methodology is also applicable to high molecular weight complexes.

Predicting DNA-binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA-binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA-binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA-binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°-120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.

High Pressure CPMG and CEST Reveal That Cavity Position Dictates Distinct Dynamic Disorder in the PP32 Repeat Protein

Given the central role of conformational dynamics in protein function, it is essential to characterize the time scales and structures associated with these transitions. High pressure (HP) perturbation favors transitions to excited states because they typically occupy a smaller molar volume, thus facilitating characterization of conformational dynamics. Repeat proteins, with their straightforward architecture, provide good models for probing the sequence dependence of protein conformational dynamics. Investigations of chemical exchange by 15N CPMG relaxation dispersion analysis revealed that introduction of a cavity via substitution of isoleucine 7 by alanine in the N-terminal capping motif of the pp32 leucine-rich repeat protein leads to pressure-dependent conformational exchange detected on the 500 μs-2 ms CPMG time scale. Exchange amplitude decreased from the N- to C-terminus, revealing a gradient of conformational exchange across the protein. In contrast, introduction of a cavity in the central core of pp32 via the L60A mutation led to pressure-induced exchange on a slower (>2 ms) time scale detected by 15N-CEST analysis. Excited state 15N chemical shifts indicated that in the excited state detected by HP CEST, the N-terminal region is mostly unfolded, while the core retains native-like structure. These HP chemical exchange measurements reveal that cavity position dictates exchange on distinct time scales, highlighting the subtle, yet central role of sequence in determining protein conformational dynamics.

NMR spectroscopy, excited states and relevance to problems in cell biology - transient pre-nucleation tetramerization of huntingtin and insights into Huntington's disease

Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for analyzing three-dimensional structure and dynamics of macromolecules at atomic resolution. Recent advances have exploited the unique properties of NMR in exchanging systems to detect, characterize and visualize excited sparsely populated states of biological macromolecules and their complexes, which are only transient. These states are invisible to conventional biophysical techniques, and play a key role in many processes, including molecular recognition, protein folding, enzyme catalysis, assembly and fibril formation. All the NMR techniques make use of exchange between sparsely populated NMR-invisible and highly populated NMR-visible states to transfer a magnetization property from the invisible state to the visible one where it can be easily detected and quantified. There are three classes of NMR experiments that rely on differences in distance, chemical shift or transverse relaxation (molecular mass) between the NMR-visible and -invisible species. Here, I illustrate the application of these methods to unravel the complex mechanism of sub-millisecond pre-nucleation oligomerization of the N-terminal region of huntingtin, encoded by exon-1 of the huntingtin gene, where CAG expansion leads to Huntington's disease, a fatal autosomal-dominant neurodegenerative condition. I also discuss how inhibition of tetramerization blocks the much slower (by many orders of magnitude) process of fibril formation.

Structure determination of high-energy states in a dynamic protein ensemble

Macromolecular function frequently requires that proteins change conformation into high-energy states1-4. However, methods for solving the structures of these functionally essential, lowly populated states are lacking. Here we develop a method for high-resolution structure determination of minorly populated states by coupling NMR spectroscopy-derived pseudocontact shifts5 (PCSs) with Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion6 (PCS-CPMG). Our approach additionally defines the corresponding kinetics and thermodynamics of high-energy excursions, thereby characterizing the entire free-energy landscape. Using a large set of simulated data for adenylate kinase (Adk), calmodulin and Src kinase, we find that high-energy PCSs accurately determine high-energy structures (with a root mean squared deviation of less than 3.5 angström). Applying our methodology to Adk during catalysis, we find that the high-energy excursion involves surprisingly small openings of the AMP and ATP lids. This previously unresolved high-energy structure solves a longstanding controversy about conformational interconversions that are rate-limiting for catalysis. Primed for either substrate binding or product release, the high-energy structure of Adk suggests a two-step mechanism combining conformational selection to this state, followed by an induced-fit step into a fully closed state for catalysis of the phosphoryl-transfer reaction. Unlike other methods for resolving high-energy states, such as cryo-electron microscopy and X-ray crystallography, our solution PCS-CPMG approach excels in cases involving domain rearrangements of smaller systems (less than 60 kDa) and populations as low as 0.5%, and enables the simultaneous determination of protein structure, kinetics and thermodynamics while proteins perform their function.

Adaptations for Pressure and Temperature in Dihydrofolate Reductases

Enzymes from extremophilic microbes that live in extreme conditions are generally adapted so that they function under those conditions, although adaptations for extreme temperatures and pressures can be difficult to unravel. Previous studies have shown mutation of Asp27 in Escherichia coli dihydrofolate reductase (DHFR) to Glu27 in Moritella profunda (Mp). DHFR enhances activity at higher pressures, although this may be an adaptation for cold. Interestingly, MpDHFR unfolds at ~70 MPa, while Moritella yayanosii (My) was isolated at depths corresponding to ~110 MPa, indicating that MyDHFR might be adapted for higher pressures. Here, these adaptations are examined using molecular dynamics simulations of DHFR from different microbes in the context of not only experimental studies of activity and stability of the protein but also the evolutionary history of the microbe. Results suggest Tyr103 of MyDHFR may be an adaptation for high pressure since Cys103 in helix F of MpDHFR forms an intra-helix hydrogen bond with Ile99 while Tyr103 in helix F of MyDHFR forms a hydrogen bond with Leu78 in helix E. This suggests the hydrogen bond between helices F and E in MyDHFR might prevent distortion at higher pressures.

Making the invisible visible: Toward structural characterization of allosteric states, interaction networks, and allosteric regulatory mechanisms in protein kinases

Despite the established view of protein kinases as dynamic and versatile allosteric regulatory machines, our knowledge of allosteric functional states, allosteric interaction networks, and the intrinsic folding energy landscapes is surprisingly limited. We discuss the latest developments in structural characterization of allosteric molecular events underlying protein kinase dynamics and functions using structural, biophysical, and computational biology approaches. The recent studies highlighted progress in making the invisible aspects of protein kinase 'life' visible, including the determination of hidden allosteric states and mapping of allosteric energy landscapes, discovery of new mechanisms underlying ligand-induced modulation of allosteric activity, evolutionary adaptation of kinase allostery, and characterization of allosteric interaction networks as the intrinsic driver of kinase adaptability and signal transmission in the regulatory assemblies.

Validating the CHARMM36m protein force field with LJ-PME reveals altered hydrogen bonding dynamics under elevated pressures

The pressure-temperature phase diagram is important to our understanding of the physics of biomolecules. Compared to studies on temperature effects, studies of the pressure dependence of protein dynamic are rather limited. Molecular dynamics (MD) simulations with fine-tuned force fields (FFs) offer a powerful tool to explore the influence of thermodynamic conditions on proteins. Here we evaluate the transferability of the CHARMM36m (C36m) protein force field at varied pressures compared with NMR data using ubiquitin as a model protein. The pressure dependences of J couplings for hydrogen bonds and order parameters for internal motion are in good agreement with experiment. We demonstrate that the C36m FF combined with the Lennard-Jones particle-mesh Ewald (LJ-PME) method is suitable for simulations in a wide range of temperature and pressure. As the ubiquitin remains stable up to 2500 bar, we identify the mobility and stability of different hydrogen bonds in response to pressure. Based on those results, C36m is expected to be applied to more proteins in the future to further investigate protein dynamics under elevated pressures.

On Catalytic Kinetics of Enzymes

Classical enzyme kinetic theories are summarized and linked with modern discoveries here. The sequential catalytic events along time axis by enzyme are analyzed at the molecular level, and by using master equations, this writing tries to connect the microscopic molecular behavior of enzyme to kinetic data (like velocity and catalytic coefficient k) obtained in experiment: 1/k = t equals to the sum of the times taken by the constituent individual steps. The relationships between catalytic coefficient k, catalytic rate or velocity, the amount of time taken by each step and physical or biochemical conditions of the system are discussed, and the perspective and hypothetic equations proposed here regarding diffusion, conformational change, chemical conversion, product release steps and the whole catalytic cycle provide an interpretation of previous experimental observations and can be testified by future experiments.

Distinct conformational dynamics and allosteric networks in alpha tryptophan synthase during active catalysis

Experimental observations of enzymes under active turnover conditions have brought new insight into the role of protein motions and allosteric networks in catalysis. Many of these studies characterize enzymes under dynamic chemical equilibrium conditions, in which the enzyme is actively catalyzing both the forward and reverse reactions during data acquisition. We have previously analyzed conformational dynamics and allosteric networks of the alpha subunit of tryptophan synthase under such conditions using NMR. We have proposed that this working state represents a four to one ratio of the enzyme bound with the indole-3-glycerol phosphate substrate (E:IGP) to the enzyme bound with the products indole and glyceraldehyde-3-phosphate (E:indole:G3P). Here, we analyze the inactive D60N variant to deconvolute the contributions of the substrate- and products-bound states to the working state. While the D60N substitution itself induces small structural and dynamic changes, the D60N E:IGP and E:indole:G3P states cannot entirely account for the conformational dynamics and allosteric networks present in the working state. The act of chemical bond breakage and/or formation, or possibly the generation of an intermediate, may alter the structure and dynamics present in the working state. As the enzyme transitions from the substrate-bound to the products-bound state, millisecond conformational exchange processes are quenched and new allosteric connections are made between the alpha active site and the surface which interfaces with the beta subunit. The structural ordering of the enzyme and these new allosteric connections may be important in coordinating the channeling of the indole product into the beta subunit.

Local frustration determines loop opening during the catalytic cycle of an oxidoreductase

Local structural frustration, the existence of mutually exclusive competing interactions, may explain why some proteins are dynamic while others are rigid. Frustration is thought to underpin biomolecular recognition and the flexibility of protein-binding sites. Here, we show how a small chemical modification, the oxidation of two cysteine thiols to a disulfide bond, during the catalytic cycle of the N-terminal domain of the key bacterial oxidoreductase DsbD (nDsbD), introduces frustration ultimately influencing protein function. In oxidized nDsbD, local frustration disrupts the packing of the protective cap-loop region against the active site allowing loop opening. By contrast, in reduced nDsbD the cap loop is rigid, always protecting the active-site thiols from the oxidizing environment of the periplasm. Our results point toward an intricate coupling between the dynamics of the active-site cysteines and of the cap loop which modulates the association reactions of nDsbD with its partners resulting in optimized protein function.

Proteins-Based Nanocatalysts for Energy Conversion Reactions

In recent years, the incorporation of molecular enzymes into nanostructured frameworks to create efficient energy conversion biomaterials has gained increasing interest as a promising strategy owing to both the dynamic behavior of proteins for their electrocatalytic function and the unique properties of the synergistic interactions between proteins and nanosized materials. Herein, we review the impact of proteins on energy conversion fields and the contribution of proteins to the improved activity of the resulting nanocomposites. We address different strategies to fabricate protein-based nanocatalysts as well as current knowledge on the structure-function relationships of enzymes during the catalytic processes. Additionally, a comprehensive review of state-of-the-art bioelectrocatalytic materials for water-splitting reactions such as hydrogen evolution reaction (HER) and oxygen evolution reactions (OER) is afforded. Finally, we briefly envision opportunities to develop a new generation of electrocatalysts towards the electrochemical reduction of N₂ to NH₃ using theoretical tools to built nature-inspired nitrogen reduction reaction catalysts.