The role of protein dynamics in the evolution of new enzyme function

Changes in Active Site Loop Conformation Relate to the Transition toward a Novel Enzymatic Activity

Enzymatic promiscuity, the ability of enzymes to catalyze multiple, distinct chemical reactions, has been well documented and is hypothesized to be a major driver of the emergence of new enzymatic functions. Yet, the molecular mechanisms involved in the transition from one activity to another remain debated and elusive. Here, we evaluated the redesign of the active site binding cleft of lactonase SsoPox using structure-based design and combinatorial libraries. We created variants with largely improved catalytic abilities against phosphotriesters, the best ones being >1000-fold better compared to the wild-type enzyme. The observed shifts in activity specificity are large, and some variants completely lost their initial activity. The selected combinations of mutations have considerably reshaped the active site cavity via side chain changes but mostly through large rearrangements of the active site loops and changes to their conformations, as revealed by a suite of crystal structures. This suggests that a specific active site loop configuration is critical to the lactonase activity. Interestingly, analysis of high-resolution structures hints at the potential role of conformational sampling and its directionality in defining the enzyme activity profile.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Selection of a promiscuous minimalist cAMP phosphodiesterase from a library of de novo designed proteins

The ability of unevolved amino acid sequences to become biological catalysts was key to the emergence of life on Earth. However, billions of years of evolution separate complex modern enzymes from their simpler early ancestors. To probe how unevolved sequences can develop new functions, we use ultrahigh-throughput droplet microfluidics to screen for phosphoesterase activity amidst a library of more than one million sequences based on a de novo designed 4-helix bundle. Characterization of hits revealed that acquisition of function involved a large jump in sequence space enriching for truncations that removed >40% of the protein chain. Biophysical characterization of a catalytically active truncated protein revealed that it dimerizes into an α-helical structure, with the gain of function accompanied by increased structural dynamics. The identified phosphodiesterase is a manganese-dependent metalloenzyme that hydrolyses a range of phosphodiesters. It is most active towards cyclic AMP, with a rate acceleration of ~109 and a catalytic proficiency of >1014 M-1, comparable to larger enzymes shaped by billions of years of evolution.

Reconstructing dynamics correlation network to simultaneously improve activity and stability of 2,3-butanediol dehydrogenase by design of distal interchain disulfide bonds

The complete enzyme catalytic cycle includes substrate binding, chemical reaction and product release, in which different dynamic conformations are adopted. Due to the complex relationship among enzyme activity, stability and dynamics, the directed evolution of enzymes for improved activity or stability commonly leads to a trade-off in stability or activity. It hence remains a challenge to engineer an enzyme to have both enhanced activity and stability. Here, we have attempted to reconstruct the dynamics correlation network involved with active center to improve both activity and stability of a 2,3-butanediol dehydrogenase (2,3-BDH) by introducing inter-chain disulfide bonds. A computational strategy was first applied to evaluate the effect of introducing inter-chain disulfide bond on activity and stability of three 2,3-BDHs, and the N258C mutation of 2,3-BDH from Corynebacterium glutamicum (CgBDH) was proved to be effective in improving both activity and stability. In the results, CgBDH-N258C showed a different unfolding curve from the wild type, with two melting temperatures (Tm) of 68.3 °C and 50.8 °C, 19.7 °C and 2 °C higher than 48.6 °C of the wild type. Its half-life was also improved by 14.8-fold compared to the wild type. Catalytic efficiency (kcat/Km) of the mutant was increased by 7.9-fold toward native substrate diacetyl and 8.8-fold toward non-native substrate 2,5-hexanedione compared to the wild type. Molecular dynamics simulations revealed that an interaction network formed by Cys258, Arg162, Ala144 and the catalytic residues was reconstructed in the mutant and the dynamics change caused by the disulfide bond could be propagated through the interactions network. This improved the enzyme stability and activity by decreasing the flexibility and locking more "reactive" pose, respectively. Further construction of mutations including A144G showing a 44-fold improvement in catalytic efficiency toward meso-2,3-BD confirmed the role of modifying dynamics correlation network in tunning enzyme activity and selectivity. This study provided important insights into the relationship among dynamics, enzyme catalysis and stability, and will be useful in the designing new enzymes with co-evolution of stability, activity and selectivity.

Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors

How a protein's function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1,158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity, even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.

Remote loop evolution reveals a complex biological function for chitinase enzymes beyond the active site

Loops are small secondary structural elements that play a crucial role in the emergence of new enzyme functions. However, the evolutionary molecular mechanisms how proteins acquire these loop elements and obtain new function is poorly understood. To address this question, we study glycoside hydrolase family 19 (GH19) chitinase-an essential enzyme family for pathogen degradation in plants. By revealing the evolutionary history and loops appearance of GH19 chitinase, we discover that one loop which is remote from the catalytic site, is necessary to acquire the new antifungal activity. We demonstrate that this remote loop directly accesses the fungal cell wall, and surprisingly, it needs to adopt a defined structure supported by long-range intramolecular interactions to perform its function. Our findings prove that nature applies this strategy at the molecular level to achieve a complex biological function while maintaining the original activity in the catalytic pocket, suggesting an alternative way to design new enzyme function.

Use of in silico approaches, synthesis and profiling of Pan-filovirus GP-1,2 preprotein specific antibodies

Intermolecular interactions of protein-protein complexes play a principal role in the process of discovering new substances used in the diagnosis and treatment of many diseases. Among such complexes of proteins, we have to mention antibodies; they interact with specific antigens of two genera of single-stranded RNA viruses belonging to the family Filoviridae-Ebolavirus and Marburgvirus; both cause rare but fatal viral hemorrhagic fever in Africa, with pandemic potential. In this research, we conduct studies aimed at the design and evaluation of antibodies targeting the filovirus glycoprotein precursor GP-1,2 to develop potential targets for the pan-filovirus easy-to-use rapid diagnostic tests. The in silico research using the available 3D structure of the natural antibody-antigen complex was carried out to determine the stability of individual protein segments in the process of its formation and maintenance. The computed free binding energy of the complex and its decomposition for all amino acids allowed us to define the residues that play an essential role in the structure and indicated the spots where potential antibodies can be improved. Following that, the study involved targeting six epitopes of the filovirus GP1,2 with two polyclonal antibodies (pABs) and 14 monoclonal antibodies (mABs). The evaluation conducted using Enzyme Immunoassays tested 62 different sandwich combinations of monoclonal antibodies (mAbs), identifying 10 combinations that successfully captured the recombinant GP1,2 (rGP). Among these combinations, the sandwich option (3G2G12* - (rGP) - 2D8F11) exhibited the highest propensity for capturing the rGP antigen.

Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.

Mononuclear binding and catalytic activity of europium(III) and gadolinium(III) at the active site of the model metalloenzyme phosphotriesterase

Lanthanide ions have ideal chemical properties for catalysis, such as hard Lewis acidity, fast ligand-exchange kinetics, high coordination-number preferences and low geometric requirements for coordination. As a result, many small-molecule lanthanide catalysts have been described in the literature. Yet, despite the ability of enzymes to catalyse highly stereoselective reactions under gentle conditions, very few lanthanoenzymes have been investigated. In this work, the mononuclear binding of europium(III) and gadolinium(III) to the active site of a mutant of the model enzyme phosphotriesterase are described using X-ray crystallography at 1.78 and 1.61 Å resolution, respectively. It is also shown that despite coordinating a single non-natural metal cation, the PTE-R18 mutant is still able to maintain esterase activity.

Flexible active-site loops fine-tune substrate specificity of hyperthermophilic metallo-oxidases

Hyperthermophilic ('superheat-loving') archaea found in high-temperature environments such as Pyrobaculum aerophilum contain multicopper oxidases (MCOs) with remarkable efficiency for oxidizing cuprous and ferrous ions. In this work, directed evolution was used to expand the substrate specificity of P. aerophilum McoP for organic substrates. Six rounds of error-prone PCR and DNA shuffling followed by high-throughput screening lead to the identification of a hit variant with a 220-fold increased efficiency (kcat/Km) than the wild-type for 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) without compromising its intrinsic activity for metal ions. The analysis of the X-ray crystal structure reveals four proximal mutations close to the T1Cu active site. One of these mutations is within the 23-residues loop that occludes this site, a distinctive feature of prokaryotic MCOs. The increased flexibility of this loop results in an enlarged tunnel and one additional pocket that facilitates bulky substrate-enzyme interactions. These findings underscore the synergy between mutations that modulate the dynamics of the active-site loop enabling enhanced catalytic function. This study highlights the potential of targeting loops close to the T1Cu for engineering improvements suitable for biotechnological applications.

Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation

Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.

Changes in an enzyme ensemble during catalysis observed by high-resolution XFEL crystallography

Enzymes populate ensembles of structures necessary for catalysis that are difficult to experimentally characterize. We use time-resolved mix-and-inject serial crystallography at an x-ray free electron laser to observe catalysis in a designed mutant isocyanide hydratase (ICH) enzyme that enhances sampling of important minor conformations. The active site exists in a mixture of conformations, and formation of the thioimidate intermediate selects for catalytically competent substates. The influence of cysteine ionization on the ICH ensemble is validated by determining structures of the enzyme at multiple pH values. Large molecular dynamics simulations in crystallo and time-resolved electron density maps show that Asp17 ionizes during catalysis and causes conformational changes that propagate across the dimer, permitting water to enter the active site for intermediate hydrolysis. ICH exhibits a tight coupling between ionization of active site residues and catalysis-activated protein motions, exemplifying a mechanism of electrostatic control of enzyme dynamics.

Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

Epistasis arises from shifting the rate-limiting step during enzyme evolution of a β-lactamase

Epistasis, the non-additive effect of mutations, can provide combinatorial improvements to enzyme activity that substantially exceed the gains from individual mutations. Yet the molecular mechanisms of epistasis remain elusive, undermining our ability to predict pathogen evolution and engineer biocatalysts. Here we reveal how directed evolution of a β-lactamase yielded highly epistatic activity enhancements. Evolution selected four mutations that increase antibiotic resistance 40-fold, despite their marginal individual effects (≤2-fold). Synergistic improvements coincided with the introduction of super-stochiometric burst kinetics, indicating that epistasis is rooted in the enzyme's conformational dynamics. Our analysis reveals that epistasis stemmed from distinct effects of each mutation on the catalytic cycle. The initial mutation increased protein flexibility and accelerated substrate binding, which is rate-limiting in the wild-type enzyme. Subsequent mutations predominantly boosted the chemical steps by fine-tuning substrate interactions. Our work identifies an overlooked cause for epistasis: changing the rate-limiting step can result in substantial synergy that boosts enzyme activity.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 Spike (S) protein. With this approach, we first identified Candidate Adaptive Polymorphisms (CAPs) of the SARS-CoV-2 Spike protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

The shortest path method (SPM) webserver for computational enzyme design

SPMweb is the online webserver of the Shortest Path Map (SPM) tool for identifying the key conformationally-relevant positions of a given enzyme structure and dynamics. The server is built on top of the DynaComm.py code and enables the calculation and visualization of the SPM pathways. SPMweb is easy-to-use as it only requires three input files: the three-dimensional structure of the protein of interest, and the two matrices (distance and correlation) previously computed from a Molecular Dynamics simulation. We provide in this publication information on how to generate the files for SPM construction even for non-expert users and discuss the most relevant parameters that can be modified. The tool is extremely fast (it takes less than one minute per job), thus allowing the rapid identification of distal positions connected to the active site pocket of the enzyme. SPM applications expand from computational enzyme design, especially if combined with other tools to identify the preferred substitution at the identified position, but also to rationalizing allosteric regulation, and even cryptic pocket identification for drug discovery. The simple user interface and setup make the SPM tool accessible to the whole scientific community. SPMweb is freely available for academia at http://spmosuna.com/.

Sequence - dynamics - function relationships in protein tyrosine phosphatases

Protein tyrosine phosphatases (PTPs) are crucial regulators of cellular signaling. Their activity is regulated by the motion of a conserved loop, the WPD-loop, from a catalytically inactive open to a catalytically active closed conformation. WPD-loop motion optimally positions a catalytically critical residue into the active site, and is directly linked to the turnover number of these enzymes. Crystal structures of chimeric PTPs constructed by grafting parts of the WPD-loop sequence of PTP1B onto the scaffold of YopH showed WPD-loops in a wide-open conformation never previously observed in either parent enzyme. This wide-open conformation has, however, been observed upon binding of small molecule inhibitors to other PTPs, suggesting the potential of targeting it for drug discovery efforts. Here, we have performed simulations of both enzymes and show that there are negligible energetic differences in the chemical step of catalysis, but significant differences in the dynamical properties of the WPD-loop. Detailed interaction network analysis provides insight into the molecular basis for this population shift to a wide-open conformation. Taken together, our study provides insight into the links between loop dynamics and chemistry in these YopH variants specifically, and how WPD-loop dynamic can be engineered through modification of the internal protein interaction network.

Mechanistic Insights into S-Depalmitolyse Activity of Cln5 Protein Linked to Neurodegeneration and Batten Disease: A QM/MM Study

Ceroid lipofuscinosis neuronal protein 5 (Cln5) is encoded by the CLN5 gene. The genetic variants of this gene are associated with the CLN5 form of Batten disease. Recently, the first crystal structure of Cln5 was reported. Cln5 shows cysteine palmitoyl thioesterase S-depalmitoylation activity, which was explored via fluorescent emission spectroscopy utilizing the fluorescent probe DDP-5. In this work, the mechanism of the reaction between Cln5 and DDP-5 was studied computationally by applying a QM/MM methodology at the ωB97X-D/6-31G(d,p):AMBER level. The results of our study clearly demonstrate the critical role of the catalytic triad Cys280-His166-Glu183 in S-depalmitoylation activity. This is evidenced through a comparison of the pathways catalyzed by the Cys280-His166-Glu183 triad and those with only Cys280 involved. The computed reaction barriers are in agreement with the catalytic efficiency. The calculated Gibb's free-energy profile suggests that S-depalmitoylation is a rate-limiting step compared to the preceding S-palmitoylation, with barriers of 26.1 and 25.3 kcal/mol, respectively. The energetics were complemented by monitoring the fluctuations in the electron density distribution through NBO charges and bond strength alterations via local mode stretching force constants during the catalytic pathways. This comprehensive protocol led to a more holistic picture of the reaction mechanism at the atomic level. It forms the foundation for future studies on the effects of gene mutations on both the S-palmitoylation and S-depalmitoylation steps, providing valuable data for the further development of enzyme replacement therapy, which is currently the only FDA-approved therapy for childhood neurodegenerative diseases, including Batten disease.

Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold .

Ratcheting synthesis

Synthetic chemistry has traditionally relied on reactions between reactants of high chemical potential and transformations that proceed energetically downhill to either a global or local minimum (thermodynamic or kinetic control). Catalysts can be used to manipulate kinetic control, lowering activation energies to influence reaction outcomes. However, such chemistry is still constrained by the shape of one-dimensional reaction coordinates. Coupling synthesis to an orthogonal energy input can allow ratcheting of chemical reaction outcomes, reminiscent of the ways that molecular machines ratchet random thermal motion to bias conformational dynamics. This fundamentally distinct approach to synthesis allows multi-dimensional potential energy surfaces to be navigated, enabling reaction outcomes that cannot be achieved under conventional kinetic or thermodynamic control. In this Review, we discuss how ratcheted synthesis is ubiquitous throughout biology and consider how chemists might harness ratchet mechanisms to accelerate catalysis, drive chemical reactions uphill and programme complex reaction sequences.

Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution

Enzyme evolution is characterized by constant alterations of the intramolecular residue networks supporting their functions. The rewiring of these network interactions can give rise to epistasis. As mutations accumulate, the epistasis observed across diverse genotypes may appear idiosyncratic, that is, exhibit unique effects in different genetic backgrounds. Here, we unveil a quantitative picture of the prevalence and patterns of epistasis in enzyme evolution by analyzing 41 fitness landscapes generated from seven enzymes. We show that >94% of all mutational and epistatic effects appear highly idiosyncratic, which greatly distorted the functional prediction of the evolved enzymes. By examining seemingly idiosyncratic changes in epistasis along adaptive trajectories, we expose several instances of higher-order, intramolecular rewiring. Using complementary structural data, we outline putative molecular mechanisms explaining higher-order epistasis along two enzyme trajectories. Our work emphasizes the prevalence of epistasis and provides an approach to exploring this phenomenon through a molecular lens.

Design of efficient artificial enzymes using crystallographically-enhanced conformational sampling

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing catalytic efficiency of de novo enzymes to a level comparable to their natural counterparts without relying on directed evolution. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models. Our work shows how computational design can generate efficient artificial enzymes by exploiting the true conformational ensemble to more effectively stabilize the transition state.

The impact of molecular variants, crystallization conditions and the space group on ligand-protein complexes: a case study on bacterial phosphotriesterase

A bacterial phosphotriesterase was employed as an experimental paradigm to examine the effects of multiple factors, such as the molecular constructs, the ligands used during protein expression and purification, the crystallization conditions and the space group, on the visualization of molecular complexes of ligands with a target enzyme. In this case, the ligands used were organophosphates that are fragments of the nerve agents and insecticides on which the enzyme acts as a bioscavenger. 12 crystal structures of various phosphotriesterase constructs obtained by directed evolution were analyzed, with resolutions of up to 1.38 Å. Both apo forms and holo forms, complexed with the organophosphate ligands, were studied. Crystals obtained from three different crystallization conditions, crystallized in four space groups, with and without N-terminal tags, were utilized to investigate the impact of these factors on visualizing the organophosphate complexes of the enzyme. The study revealed that the tags used for protein expression can lodge in the active site and hinder ligand binding. Furthermore, the space group in which the protein crystallizes can significantly impact the visualization of bound ligands. It was also observed that the crystallization precipitants can compete with, and even preclude, ligand binding, leading to false positives or to the incorrect identification of lead drug candidates. One of the co-crystallization conditions enabled the definition of the spaces that accommodate the substituents attached to the P atom of several products of organophosphate substrates after detachment of the leaving group. The crystal structures of the complexes of phosphotriesterase with the organophosphate products reveal similar short interaction distances of the two partially charged O atoms of the P-O bonds with the exposed β-Zn2+ ion and the buried α-Zn2+ ion. This suggests that both Zn2+ ions have a role in stabilizing the transition state for substrate hydrolysis. Overall, this study provides valuable insights into the challenges and considerations involved in studying the crystal structures of ligand-protein complexes, highlighting the importance of careful experimental design and rigorous data analysis in ensuring the accuracy and reliability of the resulting phosphotriesterase-organophosphate structures.

Conformational Switch of the 250s Loop Enables the Efficient Transglycosylation in GH Family 77

Amylomaltases (AMs) play important roles in glycogen and maltose metabolism. However, the molecular mechanism is elusive. Here, we investigated the conformational dynamics of the 250s loop and catalytic mechanism of Thermus aquaticus TaAM using path-metadynamics and QM/MM MD simulations. The results demonstrate that the transition of the 250s loop from an open to closed conformation promotes polysaccharide sliding, leading to the ideal positioning of the acid/base. Furthermore, the conformational dynamics can also modulate the selectivity of hydrolysis and transglycosylation. The closed conformation of the 250s loop enables the tight packing of the active site for transglycosylation, reducing the energy penalty and efficiently preventing the penetration of water into the active site. Conversely, the partially closed conformation for hydrolysis results in a loosely packed active site, destabilizing the transition state. These computational findings guide mutation experiments and enable the identification of mutants with an improved disproportionation/hydrolysis ratio. The present mechanism is in line with experimental data, highlighting the critical role of conformational dynamics in regulating the catalytic reactivity of GHs.

Computational remodeling of an enzyme conformational landscape for altered substrate selectivity

Structural plasticity of enzymes dictates their function. Yet, our ability to rationally remodel enzyme conformational landscapes to tailor catalytic properties remains limited. Here, we report a computational procedure for tuning conformational landscapes that is based on multistate design of hinge-mediated domain motions. Using this method, we redesign the conformational landscape of a natural aminotransferase to preferentially stabilize a less populated but reactive conformation and thereby increase catalytic efficiency with a non-native substrate, resulting in altered substrate selectivity. Steady-state kinetics of designed variants reveals activity increases with the non-native substrate of approximately 100-fold and selectivity switches of up to 1900-fold. Structural analyses by room-temperature X-ray crystallography and multitemperature nuclear magnetic resonance spectroscopy confirm that conformational equilibria favor the target conformation. Our computational approach opens the door to targeted alterations of conformational states and equilibria, which should facilitate the design of biocatalysts with customized activity and selectivity.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

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.

Changes in an Enzyme Ensemble During Catalysis Observed by High Resolution XFEL Crystallography

Enzymes populate ensembles of structures with intrinsically different catalytic proficiencies that are difficult to experimentally characterize. We use time-resolved mix-and-inject serial crystallography (MISC) at an X-ray free electron laser (XFEL) to observe catalysis in a designed mutant (G150T) isocyanide hydratase (ICH) enzyme that enhances sampling of important minor conformations. The active site exists in a mixture of conformations and formation of the thioimidate catalytic intermediate selects for catalytically competent substates. A prior proposal for active site cysteine charge-coupled conformational changes in ICH is validated by determining structures of the enzyme over a range of pH values. A combination of large molecular dynamics simulations of the enzyme in crystallo and time-resolved electron density maps shows that ionization of the general acid Asp17 during catalysis causes additional conformational changes that propagate across the dimer interface, connecting the two active sites. These ionization-linked changes in the ICH conformational ensemble permit water to enter the active site in a location that is poised for intermediate hydrolysis. ICH exhibits a tight coupling between ionization of active site residues and catalysis-activated protein motions, exemplifying a mechanism of electrostatic control of enzyme dynamics.

Relaxation and single site multiple mutations to identify and control allosteric networks

Advances in Nuclear Magnetic Resonance (NMR) spectroscopy have allowed for the identification and characterization of movements in enzymes over the last 20 years that has also revealed the complexities of allosteric coupling. For example, many of the inherent movements of enzymes, and proteins in general, have been shown to be highly localized but nonetheless still coupled over long distances. Such partial couplings provide challenges to both identifying allosteric networks of dynamic communication and determining their roles in catalytic function. We have developed an approach to help identify and engineer enzyme function, called Relaxation And Single Site Multiple Mutations (RASSMM). This approach is a powerful extension of mutagenesis and NMR that is based on the observation that multiple mutations to a single site distal to the active site allosterically induces different effects to networks. Such an approach generates a panel of mutations that can also be subjected to functional studies in order to match catalytic effects with changes to coupled networks. In this review, the RASSMM approach is briefly outlined together with two applications that include cyclophilin-A and Biliverdin Reductase B.

Loop dynamics and the evolution of enzyme activity

In the early 2000s, Tawfik presented his 'New View' on enzyme evolution, highlighting the role of conformational plasticity in expanding the functional diversity of limited repertoires of sequences. This view is gaining increasing traction with increasing evidence of the importance of conformational dynamics in both natural and laboratory evolution of enzymes. The past years have seen several elegant examples of harnessing conformational (particularly loop) dynamics to successfully manipulate protein function. This Review revisits flexible loops as critical participants in regulating enzyme activity. We showcase several systems of particular interest: triosephosphate isomerase barrel proteins, protein tyrosine phosphatases and β-lactamases, while briefly discussing other systems in which loop dynamics are important for selectivity and turnover. We then discuss the implications for engineering, presenting examples of successful loop manipulation in either improving catalytic efficiency, or changing selectivity completely. Overall, it is becoming clearer that mimicking nature by manipulating the conformational dynamics of key protein loops is a powerful method of tailoring enzyme activity, without needing to target active-site residues.

AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design

The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately predicting the folded three-dimensional (3D) structure of proteins and enzymes has revolutionized the structural biology and protein design fields. The 3D structure indeed reveals key information on the arrangement of the catalytic machinery of enzymes and which structural elements gate the active site pocket. However, comprehending enzymatic activity requires a detailed knowledge of the chemical steps involved along the catalytic cycle and the exploration of the multiple thermally accessible conformations that enzymes adopt when in solution. In this Perspective, some of the recent studies showing the potential of AF2 in elucidating the conformational landscape of enzymes are provided. Selected examples of the key developments of AF2-based and DL methods for protein design are discussed, as well as a few enzyme design cases. These studies show the potential of AF2 and DL for allowing the routine computational design of efficient enzymes.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

Changes in Active Site Loop Conformation Relate to the Transition toward a Novel Enzymatic Activity

Enzymatic promiscuity, the ability of enzymes to catalyze multiple, distinct chemical reactions, has been well documented and is hypothesized to be a major driver for the emergence of new enzymatic functions. Yet, the molecular mechanisms involved in the transition from one activity to another remain debated and elusive. Here, we evaluated the redesign of the active site binding cleft of the lactonase SsoPox using structure-based design and combinatorial libraries. We created variants with largely improved catalytic abilities against phosphotriesters, the best ones being > 1,000-fold better compared to the wild-type enzyme. The observed shifts in activity specificity are large, ~1,000,000-fold and beyond, since some variants completely lost their initial activity. The selected combinations of mutations have considerably reshaped the active site cavity via side chain changes but mostly through large rearrangements of the active site loops, as revealed by a suite of crystal structures. This suggests that specific active site loop configuration is critical to the lactonase activity. Interestingly, analysis of high-resolution structures hints at the potential role of conformational sampling and its directionality in defining an enzyme activity profile.

Second-Shell Residues Contribute to Catalysis by Predominately Preorganizing the Apo State in PafA

Residues beyond the first coordination shell are often observed to make considerable cumulative contributions in enzymes. Due to typically indirect perturbations of multiple physicochemical properties of the active site, however, their individual and specific roles in enzyme catalysis and disease-causing mutations remain difficult to predict and understand at the molecular level. Here we analyze the contributions of several second-shell residues in phosphate-irrepressible alkaline phosphatase of flavobacterium (PafA), a representative system as one of the most efficient enzymes. By adopting a multifaceted approach that integrates quantum-mechanical/molecular-mechanical free energy computations, molecular-mechanical molecular dynamics simulations, and density functional theory cluster model calculations, we probe the rate-limiting phosphoryl transfer step and structural properties of all relevant enzyme states. In combination with available experimental data, our computational results show that mutations of the studied second-shell residues impact catalytic efficiency mainly by perturbation of the apo state and therefore substrate binding, while they do not affect the ground state or alter the nature of phosphoryl transfer transition state significantly. Several second-shell mutations also modulate the active site hydration level, which in turn influences the energetics of phosphoryl transfer. These mechanistic insights also help inform strategies that may improve the efficiency of enzyme design and engineering by going beyond the current focus on the first coordination shell.

Rational design and topochemical synthesis of polymorphs of a polymer

Packing a polymer in different ways can give polymorphs of the polymer having different properties. β-Turn forming peptides such as 2-aminoisobutyric acid (Aib)-rich peptides adopt several conformations by varying the dihedral angles. Aiming at this, a β-turn-forming peptide monomer would give different polymorphs and these polymorphs upon topochemical polymerization would yield polymorphs of the polymer, we designed an Aib-rich monomer N₃-(Aib)₃-NHCH₂-C[triple bond, length as m-dash]CH. This monomer crystallizes as two polymorphs and one hydrate. In all forms, the peptide adopts β-turn conformations and arranges in a head-to-tail manner with their azide and alkyne units proximally placed in a ready-to-react alignment. On heating, both the polymorphs undergo topochemical azide-alkyne cycloaddition polymerization. Polymorph I polymerized in a single-crystal-to-single-crystal (SCSC) fashion and the single-crystal X-ray diffraction analysis of the polymer revealed its screw-sense reversing helical structure. Polymorph II maintains its crystallinity during polymerization but gradually becomes amorphous upon storage. The hydrate III undergoes a dehydrative transition to polymorph II. Nanoindentation studies revealed that different polymorphs of the monomer and the corresponding polymers exhibited different mechanical properties, in accordance with their crystal packing. This work demonstrates the promising future of the marriage of polymorphism and topochemistry for obtaining polymorphs of polymers.

Leveraging a Structural Blueprint to Rationally Engineer the Rieske Oxygenase TsaM

Rieske nonheme iron oxygenases use two metallocenters, a Rieske-type [2Fe-2S] cluster and a mononuclear iron center, to catalyze oxidation reactions on a broad range of substrates. These enzymes are widely used by microorganisms to degrade environmental pollutants and to build complexity in a myriad of biosynthetic pathways that are industrially interesting. However, despite the value of this chemistry, there is a dearth of understanding regarding the structure-function relationships in this enzyme class, which limits our ability to rationally redesign, optimize, and ultimately exploit the chemistry of these enzymes. Therefore, in this work, by leveraging a combination of available structural information and state-of-the-art protein modeling tools, we show that three "hotspot" regions can be targeted to alter the site selectivity, substrate preference, and substrate scope of the Rieske oxygenase p-toluenesulfonate methyl monooxygenase (TsaM). Through mutation of six to 10 residues distributed between three protein regions, TsaM was engineered to behave as either vanillate monooxygenase (VanA) or dicamba monooxygenase (DdmC). This engineering feat means that TsaM was rationally engineered to catalyze an oxidation reaction at the meta and ortho positions of an aromatic substrate, rather than its favored native para position, and that TsaM was redesigned to perform chemistry on dicamba, a substrate that is not natively accepted by the enzyme. This work thus contributes to unlocking our understanding of structure-function relationships in the Rieske oxygenase enzyme class and expands foundational principles for future engineering of these metalloenzymes.

Combining Protein Conformational Diversity and Phylogenetic Information Using CoDNaS and CoDNaS-Q

CoDNaS (http://ufq.unq.edu.ar/codnas/) and CoDNaS-Q (http://ufq.unq.edu.ar/codnasq) are repositories of proteins with different degrees of conformational diversity. Following the ensemble nature of the native state, conformational diversity represents the structural differences between the conformers in the ensemble. Each entry in CoDNaS and CoDNaS-Q contains a redundant collection of experimentally determined conformers obtained under different conditions. These conformers represent snapshots of the protein dynamism. While CoDNaS contains examples of conformational diversity at the tertiary level, a recent development, CoDNaS-Q, contains examples at the quaternary level. In the emerging age of accurate protein structure prediction by machine learning approaches, many questions remain open regarding the characterization of protein dynamism. In this context, most bioinformatics resources take advantage of distinct features derived from protein alignments, however, the complexity and heterogeneity of information makes it difficult to recover reliable biological signatures. Here we present five protocols to explore tertiary and quaternary conformational diversity at the individual protein level as well as for the characterization of the distribution of conformational diversity at the protein family level in a phylogenetic context. These protocols can provide curated protein families with experimentally known conformational diversity, facilitating the exploration of sequence determinants of protein dynamism. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Assessing conformational diversity with CoDNaS Alternate Protocol 1: Assessing conformational diversity at the quaternary level with CoDNaS-Q Basic Protocol 2: Exploring conformational diversity in a protein family Alternate Protocol 2: Exploring quaternary conformational diversity in a protein family Basic Protocol 3: Representing conformational diversity in a phylogenetic context.

Engineering the Active Site Lid Dynamics to Improve the Catalytic Efficiency of Yeast Cytosine Deaminase

Conformational dynamics is important for enzyme catalysis. However, engineering dynamics to achieve a higher catalytic efficiency is still challenging. In this work, we develop a new strategy to improve the activity of yeast cytosine deaminase (yCD) by engineering its conformational dynamics. Specifically, we increase the dynamics of the yCD C-terminal helix, an active site lid that controls the product release. The C-terminal is extended by a dynamical single α-helix (SAH), which improves the product release rate by up to ~8-fold, and the overall catalytic rate kcat by up to ~2-fold. It is also shown that the kcat increase is due to the favorable activation entropy change. The NMR H/D exchange data indicate that the conformational dynamics of the transition state analog complex increases as the helix is extended, elucidating the origin of the enhanced catalytic entropy. This study highlights a novel dynamics engineering strategy that can accelerate the overall catalysis through the entropy-driven mechanism.

Exploring Oxidoreductases from Extremophiles for Biosynthesis in a Non-Aqueous System

Organic solvent tolerant oxidoreductases are significant for both scientific research and biomanufacturing. However, it is really challenging to obtain oxidoreductases due to the shortages of natural resources and the difficulty to obtained it via protein modification. This review summarizes the recent advances in gene mining and structure-functional study of oxidoreductases from extremophiles for non-aqueous reaction systems. First, new strategies combining genome mining with bioinformatics provide new insights to the discovery and identification of novel extreme oxidoreductases. Second, analysis from the perspectives of amino acid interaction networks explain the organic solvent tolerant mechanism, which regulate the discrete structure-functional properties of extreme oxidoreductases. Third, further study by conservation and co-evolution analysis of extreme oxidoreductases provides new perspectives and strategies for designing robust enzymes for an organic media reaction system. Furthermore, the challenges and opportunities in designing biocatalysis non-aqueous systems are highlighted.

Creatinase: Using Increased Entropy to Improve the Activity and Thermostability

Improving protein thermostability in mutagenesis-based enzyme engineering was often achieved by enhancing interresidue interactions via mutation to increase the enthalpy penalty of unfolding. However, this approach may trade off the functional activity due to the loss of structural flexibility of the biomolecule. Here, by performing X-ray crystallography, enzymatic kinetic experiments, neutron scattering, and thermodynamical measurements, we compared the structures, catalytic behaviors, dynamics, and thermostability between a wild-type creatinase and its four-point mutant. We found that the mutant is an entropy-driven thermostable protein with higher structural flexibility, i.e., higher conformational entropy, in the folded state compared to the wild type. The increased conformational entropy of the mutant in the folded state can reduce the entropy gain during unfolding and thus renders it greater thermostability. Moreover, the increased structural flexibility, particularly around the catalytic site, can broaden the mutant's working temperature range and considerably improve its activity at ambient conditions, which is crucial for its application in diagnosing kidney diseases. Complementary all-atom molecular dynamics simulations indicated that the four mutations replaced several of the strong interresidue interactions (electrostatic interactions and hydrogen bonds) with weak hydrophobic interactions. These substitutions not only release the structural flexibility to promote the thermostability and enzymatic activity of the protein but they also preserve the protein structure from collapsing. Our findings may pave a route for the entropy-driven strategy to design proteins with high thermostability and activity.

Protection of catalytic cofactors by polypeptides as a driver for the emergence of primordial enzymes

Enzymes catalyze the chemical reactions of life. For nearly half of known enzymes, catalysis requires the binding of small molecules known as cofactors. Polypeptide-cofactor complexes likely formed at a primordial stage and became starting points for the evolution of many efficient enzymes. Yet, evolution has no foresight so the driver for the primordial complex formation is unknown. Here, we use a resurrected ancestral TIM-barrel protein to identify one potential driver. Heme binding at a flexible region of the ancestral structure yields a peroxidation catalyst with enhanced efficiency when compared to free heme. This enhancement, however, does not arise from protein-mediated promotion of catalysis. Rather, it reflects protection of bound heme from common degradation processes and a resulting longer life time and higher effective concentration for the catalyst. Protection of catalytic cofactors by polypeptides emerges as a general mechanism to enhance catalysis and may have plausibly benefited primordial polypeptide-cofactor associations.

Conformational exchange divergence along the evolutionary pathway of eosinophil-associated ribonucleases

The evolutionary role of conformational exchange in the emergence and preservation of function within structural homologs remains elusive. While protein engineering has revealed the importance of flexibility in function, productive modulation of atomic-scale dynamics has only been achieved on a finite number of distinct folds. Allosteric control of unique members within dynamically diverse structural families requires a better appreciation of exchange phenomena. Here, we examined the functional and structural role of conformational exchange within eosinophil-associated ribonucleases. Biological and catalytic activity of various EARs was performed in parallel to mapping their conformational behavior on multiple timescales using NMR and computational analyses. Despite functional conservation and conformational seclusion to a specific domain, we show that EARs can display similar or distinct motional profiles, implying divergence rather than conservation of flexibility. Comparing progressively more distant enzymes should unravel how this subfamily has evolved new functions and/or altered their behavior at the molecular level.

Role of distal sites in enzyme engineering

The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.

A conserved SH3-like fold in diverse putative proteins tetramerizes into an oxidoreductase providing an antimicrobial resistance phenotype

We present a potential mechanism for emergence of catalytic activity that is essential for survival, from a non-catalytic protein fold. The type B dihydrofolate reductase (DfrB) family of enzymes were first identified in pathogenic bacteria because their dihydrofolate reductase activity is sufficient to provide trimethoprim (TMP) resistance. DfrB enzymes are described as poorly evolved as a result of their unusual structural and kinetic features. No characterized protein shares sequence homology with DfrB enzymes; how they evolved to emerge in the modern resistome is unknown. In this work, we identify DfrB homologues from a database of putative and uncharacterized proteins. These proteins include an SH3-like fold homologous to the DfrB enzymes, embedded in a variety of additional structural domains. By means of functional, structural and biophysical characterization, we demonstrate that these distant homologues and their extracted SH3-like fold can display dihydrofolate reductase activity and confer TMP resistance. We provide evidence of tetrameric assembly and catalytic mechanism analogous to that of DfrB enzymes. These results contribute, to our knowledge, the first insights into a potential evolutionary path taken by this SH3-like fold to emerge in the modern resistome following introduction of TMP. This article is part of the theme issue 'Reactivity and mechanism in chemical and synthetic biology'.

Reactivity and mechanism in chemical and synthetic biology

Physical organic chemistry and mechanistic thinking provide a strong intellectual framework for understanding the chemical logic of evolvable informational macromolecules and metabolic transformations in living organisms. These concepts have also led to numerous successes in designing and applying tools to delineate biological function in health and disease, chemical ecology and possible alternative chemistries employed by extraterrestrial life. A symposium at the 2020 Pacifichem meeting was scheduled in December 2020 to discuss designing and exploiting expanded genetic alphabets, methods to understand the biosynthesis of natural products and re-engineering primary metabolism in bacteria. The COVID-19 pandemic led to postponement of in-person discussions, with the symposium eventually being held on 20-21 December 2021 as an online event. This issue is a written record of work presented on biosynthetic pathways and enzyme catalysis, engineering microorganisms with new metabolic capabilities, and the synthesis of non-canonical, nucleobases for medical applications and for studies of alternate chemistries for living organisms. The variety of opinion pieces, reviews and original research articles provide a starting point for innovations that clarify how complex biological systems emerge from the rules of chemical reactivity and mechanism. This article is part of the themed issue 'Reactivity and mechanism in chemical and synthetic biology'.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

Rational identification of a high catalytic efficiency leucine dehydrogenase and process development for efficient synthesis of l-phenylglycine

Enzymatic asymmetric synthesis of chiral amino acids has great industrial potential. However, the low catalytic efficiency of high-concentration substrates limits their industrial application. Herein, using a combination of substrate catalytic efficiency prediction based on "open to closed" conformational change and substrate specificity prediction, a novel leucine dehydrogenase (TsLeuDH), with high substrate catalytic efficiency toward benzoylformic acid (BFA) for producing l-phenylglycine (l-Phg), was directly identified from 4695 putative leucine dehydrogenases in a public database. The specific activity of TsLeuDH was determined to be as high as 4253.8 U mg^-1 . Through reaction process optimization, a high-concentration substrate (0.7 m) was efficiently and completely converted within 90 min in a single batch, without any external coenzyme addition. Moreover, a continuous flow-feeding approach was designed using gradient control of the feed rate to reduce substrate accumulation. Finally, the highest overall substrate concentration of up to 1.2 m BFA could be aminated to l-Phg with conversion of >99% in 3 h, demonstrating that this new combination of enzyme process development is promising for large-scale application of l-Phg.

Protein dynamics provide mechanistic insights about epistasis among common missense polymorphisms

Sequencing of the protein coding genome has revealed many different missense mutations of human proteins and different population frequencies of corresponding haplotypes, which consist of different sets of those mutations. Here, we present evidence for pairwise intramolecular epistasis (i.e., nonadditive interactions) between many such mutations through an analysis of protein dynamics. We suggest that functional compensation for conserving protein dynamics is a likely evolutionary mechanism that maintains high-frequency mutations that are individually nonneutral but epistatically compensating within proteins. This analysis is the first of its type to look at human proteins with specific high population frequency mutations and examine the relationship between mutations that make up that observed high-frequency protein haplotype. Importantly, protein dynamics revealed a separation between high and low frequency haplotypes within a target protein cytochrome P450 2A7, with the high-frequency haplotypes showing behavior closer to the wild-type protein. Common protein haplotypes containing two mutations display dynamic compensation in which one mutation can correct for the dynamic effects of the other. We also utilize a dynamics-based metric, EpiScore, that evaluates the epistatic interactions and allows us to see dynamic compensation within many other proteins.

Engineering Rieske oxygenase activity one piece at a time

Enzyme engineering plays a central role in the development of biocatalysts for biotechnology, chemical and pharmaceutical manufacturing, and environmental remediation. Rational design of proteins has historically relied on targeting active site residues to confer a protein with desirable catalytic properties. However, additional "hotspots" are also known to exist beyond the active site. Structural elements such as subunit-subunit interactions, entrance tunnels, and flexible loops influence enzyme catalysis and serve as potential "hotspots" for engineering. For the Rieske oxygenases, which use a Rieske cluster and mononuclear iron center to catalyze a challenging set of reactions, these outside of the active site regions are increasingly being shown to drive catalytic outcomes. Therefore, here, we highlight recent work on structurally characterized Rieske oxygenases that implicates architectural pieces inside and outside of the active site as key dictators of catalysis, and we suggest that these features may warrant attention in efforts aimed at Rieske oxygenase engineering.