Laboratory evolution of protein conformational dynamics

Protein representations: Encoding biological information for machine learning in biocatalysis

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

Catalytic Redundancies and Conformational Plasticity Drives Selectivity and Promiscuity in Quorum Quenching Lactonases

Several enzymes from the metallo-β-lactamase-like family of lactonases (MLLs) degrade N-acyl L-homoserine lactones (AHLs). They play a role in a microbial communication system known as quorum sensing, which contributes to pathogenicity and biofilm formation. Designing quorum quenching (QQ) enzymes that can interfere with this communication allows them to be used in a range of industrial and biomedical applications. However, tailoring these enzymes for specific communication signals requires a thorough understanding of their mechanisms and the physicochemical properties that determine their substrate specificities. We present here a detailed biochemical, computational, and structural study of GcL, which is a highly proficient and thermostable MLL with broad substrate specificity. We show that GcL not only accepts a broad range of substrates but also hydrolyzes these substrates through at least two different mechanisms. Further, the preferred mechanism appears to depend on both the substrate structure and/or the nature of the residues lining the active site. We demonstrate that other lactonases, such as AiiA and AaL, show similar mechanistic promiscuity, suggesting that this is a shared feature among MLLs. Mechanistic promiscuity has been seen previously in the lactonase/paraoxonase PON1, as well as with protein tyrosine phosphatases that operate via a dual general acid mechanism. The apparent prevalence of this phenomenon is significant from both a biochemical and protein engineering perspective: in addition to optimizing for specific substrates, it may be possible to optimize for specific mechanisms, opening new doors not just for the design of novel quorum quenching enzymes but also of other mechanistically promiscuous enzymes.

Friends and relatives: insight into conformational regulation from orthologues and evolutionary lineages using KIF and KIN

Noncovalent interaction networks provide a powerful means to represent and analyze protein structure. Such networks can represent both static structures and dynamic conformational ensembles. We have recently developed two tools for analyzing such interaction networks and generating hypotheses for protein engineering. Here, we apply these tools to the conformational regulation of substrate specificity in class A β-lactamases, particularly the evolutionary development from generalist to specialist catalytic function and how that can be recapitulated or reversed by protein engineering. These tools, KIF and KIN, generate a set of prioritized residues and interactions as targets for experimental protein engineering.

Harnessing conformational dynamics in enzyme catalysis to achieve nature-like catalytic efficiencies: the shortest path map tool for computational enzyme redesign

Enzymes exhibit diverse conformations, as represented in the free energy landscape (FEL). Such conformational diversity provides enzymes with the ability to evolve towards novel functions. The challenge lies in identifying mutations that enhance specific conformational changes, especially if located in distal sites from the active site cavity. The shortest path map (SPM) method, which we developed to address this challenge, constructs a graph based on the distances and correlated motions of residues observed in nanosecond timescale molecular dynamics (MD) simulations. We recently introduced a template based AlphaFold2 (tAF2) approach coupled with 10 nanosecond MD simulations to quickly estimate the conformational landscape of enzymes and assess how the FEL is shifted after mutation. In this study, we evaluate the potential of SPM when coupled with tAF2-MD in estimating conformational heterogeneity and identifying key conformationally-relevant positions. The selected model system is the beta subunit of tryptophan synthase (TrpB). We compare how the SPM pathways differ when integrating tAF2 with different MD simulation lengths from as short as 10 ns until 50 ns and considering two distinct Amber forcefield and water models (ff14SB/TIP3P versus ff19SB/OPC). The new methodology can more effectively capture the distal mutations found in laboratory evolution, thus showcasing the efficacy of tAF2-MD-SPM in rapidly estimating enzyme dynamics and identifying the key conformationally relevant hotspots for computational enzyme engineering.

Unlocking the potential of enzyme engineering via rational computational design strategies

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

Catalytic Redundancies and Conformational Plasticity Drives Selectivity and Promiscuity in Quorum Quenching Lactonases

Several enzymes from the metallo-β-lactamase-like family of lactonases (MLLs) degrade N- acyl-L-homoserine lactones (AHLs). In doing so, they play a role in a microbial communication system, quorum sensing, which contributes to pathogenicity and biofilm formation. There is currently great interest in designing quorum quenching ( QQ ) enzymes that can interfere with this communication and be used in a range of industrial and biomedical applications. However, tailoring these enzymes for specific targets requires a thorough understanding of their mechanisms and the physicochemical properties that determine their substrate specificities. We present here a detailed biochemical, computational, and structural study of the MLL GcL, which is highly proficient, thermostable, and has broad substrate specificity. Strikingly, we show that GcL does not only accept a broad range of substrates but is also capable of utilizing different reaction mechanisms that are differentially used in function of the substrate structure or the remodeling of the active site via mutations. Comparison of GcL to other lactonases such as AiiA and AaL demonstrates similar mechanistic promiscuity, suggesting this is a shared feature across lactonases in this enzyme family. Mechanistic promiscuity has previously been observed in the lactonase/paraoxonase PON1, as well as with protein tyrosine phosphatases that operate via a dual general-acid mechanism. The apparent prevalence of this phenomenon is significant from both a biochemical and an engineering perspective: in addition to optimizing for specific substrates, it is possible to optimize for specific mechanisms, opening new doors not just for the design of novel quorum quenching enzymes, but also of other mechanistically promiscuous enzymes.

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.

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.

From random to rational: improving enzyme design through electric fields, second coordination sphere interactions, and conformational dynamics

Enzymes are versatile and efficient biological catalysts that drive numerous cellular processes, motivating the development of enzyme design approaches to tailor catalysts for diverse applications. In this perspective, we investigate the unique properties of natural, evolved, and designed enzymes, recognizing their strengths and shortcomings. We highlight the challenges and limitations of current enzyme design protocols, with a particular focus on their limited consideration of long-range electrostatic and dynamic effects. We then delve deeper into the impact of the protein environment on enzyme catalysis and explore the roles of preorganized electric fields, second coordination sphere interactions, and protein dynamics for enzyme function. Furthermore, we present several case studies illustrating successful enzyme-design efforts incorporating enzyme strategies mentioned above to achieve improved catalytic properties. Finally, we envision the future of enzyme design research, spotlighting the challenges yet to be overcome and the synergy of intrinsic electric fields, second coordination sphere interactions, and conformational dynamics to push the state-of-the-art boundaries.

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.

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.

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.

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.

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.

Ancestral Sequence Reconstruction Identifies Structural Changes Underlying the Evolution of Ideonella sakaiensis PETase and Variants with Improved Stability and Activity

The improved production, recycling, and removal of plastic waste, such as polyethylene terephthalate (PET), are pressing environmental and economic issues for society. Biocatalytic (enzymatic) PET depolymerization is potentially a sustainable, low-energy solution to PET recycling, especially when compared with current disposal methods such as landfills, incineration, or gasification. IsPETase has been extensively studied for its use in PET depolymerization; however, its evolution from cutinases is not fully understood, and most engineering studies have neglected the majority of the available sequence space remote from the active site. In this study, ancestral protein reconstruction (ASR) has been used to trace the evolutionary trajectory from ancient serine hydrolases to IsPETase, while ASR and the related design approach, protein repair one-stop shop, were used to identify enzyme variants with improved activity and stability. Kinetic and structural characterization of these variants reveals new insights into the evolution of PETase activity and the role of second-shell mutations around the active site. Among the designed and reconstructed variants, we identified several with melting points 20 °C higher than that of IsPETase and two variants with significantly higher catalytic activity.

The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database

The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.

Estimating conformational heterogeneity of tryptophan synthase with a template-based Alphafold2 approach

The three-dimensional structure of the enzymes provides very relevant information on the arrangement of the catalytic machinery and structural elements gating the active site pocket. The recent success of the neural network Alphafold2 in predicting the folded structure of proteins from the primary sequence with high levels of accuracy has revolutionized the protein design field. However, the application of Alphafold2 for understanding and engineering function directly from the obtained single static picture is not straightforward. Indeed, understanding enzymatic function requires the exploration of the ensemble of thermally accessible conformations that enzymes adopt in solution. In the present study, we evaluate the potential of Alphafold2 in assessing the effect of the mutations on the conformational landscape of the beta subunit of tryptophan synthase (TrpB). Specifically, we develop a template-based Alphafold2 approach for estimating the conformational heterogeneity of several TrpB enzymes, which is needed for enhanced stand-alone activity. Our results show the potential of Alphafold2, especially if combined with molecular dynamics simulations, for elucidating the changes induced by mutation in the conformational landscapes at a rather reduced computational cost, thus revealing its plausible application in computational enzyme design.

Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes

Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.

A metal ion-dependent conformational switch modulates activity of the Plasmodium M17 aminopeptidase

The metal-dependent M17 aminopeptidases are conserved throughout all kingdoms of life. This large enzyme family is characterized by a conserved binuclear metal center and a distinctive homohexameric arrangement. Recently, we showed that hexamer formation in Plasmodium M17 aminopeptidases was controlled by the metal ion environment, although the functional necessity for hexamer formation is still unclear. To further understand the mechanistic role of the hexameric assembly, here we undertook an investigation of the structure and dynamics of the M17 aminopeptidase from Plasmodium falciparum, PfA-M17. We describe a novel structure of PfA-M17, which shows that the active sites of each trimer are linked by a dynamic loop, and loop movement is coupled with a drastic rearrangement of the binuclear metal center and substrate-binding pocket, rendering the protein inactive. Molecular dynamics simulations and biochemical analyses of PfA-M17 variants demonstrated that this rearrangement is inherent to PfA-M17, and that the transition between the active and inactive states is metal dependent and part of a dynamic regulatory mechanism. Key to the mechanism is a remodeling of the binuclear metal center, which occurs in response to a signal from the neighboring active site and serves to moderate the rate of proteolysis under different environmental conditions. In conclusion, this work identifies a precise mechanism by which oligomerization contributes to PfA-M17 function. Furthermore, it describes a novel role for metal cofactors in the regulation of enzymes, with implications for the wide range of metalloenzymes that operate via a two-metal ion catalytic center, including DNA processing enzymes and metalloproteases.

Distal Mutations Shape Substrate-Binding Sites during Evolution of a Metallo-Oxidase into a Laccase

Laccases are in increasing demand as innovative solutions in the biorefinery fields. Here, we combine mutagenesis with structural, kinetic, and in silico analyses to characterize the molecular features that cause the evolution of a hyperthermostable metallo-oxidase from the multicopper oxidase family into a laccase (k _cat 273 s^-1 for a bulky aromatic substrate). We show that six mutations scattered across the enzyme collectively modulate dynamics to improve the binding and catalysis of a bulky aromatic substrate. The replacement of residues during the early stages of evolution is a stepping stone for altering the shape and size of substrate-binding sites. Binding sites are then fine-tuned through high-order epistasis interactions by inserting distal mutations during later stages of evolution. Allosterically coupled, long-range dynamic networks favor catalytically competent conformational states that are more suitable for recognizing and stabilizing the aromatic substrate. This work provides mechanistic insight into enzymatic and evolutionary molecular mechanisms and spots the importance of iterative experimental and computational analyses to understand local-to-global changes.

Complex Loop Dynamics Underpin Activity, Specificity, and Evolvability in the (βα)8 Barrel Enzymes of Histidine and Tryptophan Biosynthesis

Enzymes are conformationally dynamic, and their dynamical properties play an important role in regulating their specificity and evolvability. In this context, substantial attention has been paid to the role of ligand-gated conformational changes in enzyme catalysis; however, such studies have focused on tremendously proficient enzymes such as triosephosphate isomerase and orotidine 5'-monophosphate decarboxylase, where the rapid (μs timescale) motion of a single loop dominates the transition between catalytically inactive and active conformations. In contrast, the (βα)8-barrels of tryptophan and histidine biosynthesis, such as the specialist isomerase enzymes HisA and TrpF, and the bifunctional isomerase PriA, are decorated by multiple long loops that undergo conformational transitions on the ms (or slower) timescale. Studying the interdependent motions of multiple slow loops, and their role in catalysis, poses a significant computational challenge. This work combines conventional and enhanced molecular dynamics simulations with empirical valence bond simulations to provide rich details of the conformational behavior of the catalytic loops in HisA, PriA, and TrpF, and the role of their plasticity in facilitating bifunctionality in PriA and evolved HisA variants. In addition, we demonstrate that, similar to other enzymes activated by ligand-gated conformational changes, loops 3 and 4 of HisA and PriA act as gripper loops, facilitating the isomerization of the large bulky substrate ProFAR, albeit now on much slower timescales. This hints at convergent evolution on these different (βα)8-barrel scaffolds. Finally, our work reemphasizes the potential of engineering loop dynamics as a tool to artificially manipulate the catalytic repertoire of TIM-barrel proteins.

Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field

The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit treatment of electronic polarization, such an extension must be accompanied by optimization of van der Waals (vdW) interactions, in the context of the Lennard-Jones (LJ) formalism in the present study. This is particularly challenging due to the extensive nature of chemical space combined with the correlated nature of LJ parameters. To address this challenge, a deep learning (DL)-based parametrization framework is developed, allowing for sampling of wide ranges of LJ parameters targeting experimental condensed phase thermodynamic properties. The present work utilizes this framework to develop the LJ parameters for atoms associated with four distinct groups covering 10 different atom types. Final parameter selection was facilitated by quantum mechanical data on rare-gas interactions with the training set molecules. The chosen parameters were then validated through experimental hydration free energies and condensed phase thermodynamic properties of validation set molecules to confirm transferability. The ultimate outcome of utilizing this framework is a set of LJ parameters in the context of the polarizable Drude FF, which demonstrated improvement in the reproduction of both experimental pure solvent and crystal properties and hydration free energies of the molecules compared to the additive CHARMM General FF (CGenFF) including the ability of the Drude FF to accurately reproduce both experimental pure solvent properties and hydration free energies. The study also shows how correlations between difference in the reproduction of condensed phase data between model compounds may be used to direct the selection of new atom types and training set molecules during FF development.

Integrating dynamics into enzyme engineering

Enzyme engineering has become a widely adopted practice in research labs and industry. In parallel, the past decades have seen tremendous strides in characterizing the dynamics of proteins, using a growing array of methodologies. Importantly, links have been established between the dynamics of proteins and their function. Characterizing the dynamics of an enzyme prior to, and following, its engineering is beginning to inform on the potential of 'dynamic engineering', i.e. the rational modification of protein dynamics to alter enzyme function. Here we examine the state of knowledge at the intersection of enzyme engineering and protein dynamics, describe current challenges and highlight pioneering work in the nascent area of dynamic engineering.

Combined active pocket and hinge region engineering to develop an NADPH-dependent phenylglycine dehydrogenase

NADPH-dependent amino acid dehydrogenases (AADHs) are favorable enzymes to construct artificial biosynthetic pathways in whole-cell for high-value noncanonical amino acids (NcAAs) production. Glutamate dehydrogenases (GluDHs) represent attractive candidates for the development of novel NADPH-dependent AADHs. Here, we report the development of a novel NADPH-dependent phenylglycine dehydrogenase by combining active pocket engineering and hinge region engineering of a GluDH from Pseudomonas putida (PpGluDH). The active pocket of PpGluDH was firstly tailored to optimize its binding mode with bulky substrate α-oxobenzeneacetic acid (α-OA), and then, the hinge region was further engineered to tune the protein conformational dynamics, which finally resulted in a mutant M3 (T196A/T121I/L123D) with a 103-fold increase of catalytic efficiency (k_cat/K_m) toward α-OA. The M3 mutant exhibited high catalytic performance in both in vitro biocatalysis preparation and in vivo biosynthesis of l-phenylglycine, indicating its promising practical applications. Our results demonstrated that co-engineering of the active pocket and hinge region is an effective strategy for developing novel NADPH-dependent AADHs from GluDHs for NcAAs production.

In Silico Identification and Experimental Validation of Distal Activity-Enhancing Mutations in Tryptophan Synthase

Allostery is a central mechanism for the regulation of multi-enzyme complexes. The mechanistic basis that drives allosteric regulation is poorly understood but harbors key information for enzyme engineering. In the present study, we focus on the tryptophan synthase complex that is composed of TrpA and TrpB subunits, which allosterically activate each other. Specifically, we develop a rational approach for identifying key amino acid residues of TrpB distal from the active site. Those residues are predicted to be crucial for shifting the inefficient conformational ensemble of the isolated TrpB to a productive ensemble through intra-subunit allosteric effects. The experimental validation of the conformationally driven TrpB design demonstrates its superior stand-alone activity in the absence of TrpA, comparable to those enhancements obtained after multiple rounds of experimental laboratory evolution. Our work evidences that the current challenge of distal active site prediction for enhanced function in computational enzyme design has become within reach.

Unlocking the Stereoselectivity and Substrate Acceptance of Enzymes: Proline-Induced Loop Engineering Test

Protein stability and evolvability influence each other. Although protein dynamics play essential roles in various catalytically important properties, their high flexibility and diversity makes it difficult to incorporate such properties into rational engineering. Therefore, how to unlock the potential evolvability in a user-friendly rational design process remains a challenge. In this endeavor, we describe a method for engineering an enantioselective alcohol dehydrogenase. It enables synthetically important substrate acceptance for 4-chlorophenyl pyridine-2-yl ketone, and perfect stereocontrol of both (S)- and (R)-configured products. Thermodynamic analysis unveiled the subtle interaction between enzyme stability and evolvability, while computational studies provided insights into the origin of selectivity and substrate recognition. Preparative-scale synthesis of the (S)-product (73 % yield; >99 % ee) was performed on a gram-scale. This proof-of-principle study demonstrates that interfaced proline residues can be rationally engineered to unlock evolvability and thus provide access to new biocatalysts with highly improved catalytic performance.

Evolution of dynamical networks enhances catalysis in a designer enzyme

Activation heat capacity is emerging as a crucial factor in enzyme thermoadaptation, as shown by the non-Arrhenius behaviour of many natural enzymes. However, its physical origin and relationship to the evolution of catalytic activity remain uncertain. Here we show that directed evolution of a computationally designed Kemp eliminase reshapes protein dynamics, which gives rise to an activation heat capacity absent in the original design. These changes buttress transition-state stabilization. Extensive molecular dynamics simulations show that evolution results in the closure of solvent-exposed loops and a better packing of the active site. Remarkably, this gives rise to a correlated dynamical network that involves the transition state and large parts of the protein. This network tightens the transition-state ensemble, which induces a negative activation heat capacity and non-linearity in the activity-temperature dependence. Our results have implications for understanding enzyme evolution and suggest that selectively targeting the conformational dynamics of the transition-state ensemble by design and evolution will expedite the creation of novel enzymes.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

A Minireview on Temperature Dependent Protein Conformational Sampling

In this minireview we discuss the role of the more subtle conformational change-protein conformational sampling and connect it to the classic relationship of protein structure and function. The theory of pre-existing functional states of protein are discussed in context of alternate protein conformational sampling. Last, we discuss how temperature, ligand binding and mutations affect the protein conformational sampling mode which is linked to the protein function regulation. The review includes several protein systems that showed temperature dependent protein conformational sampling. We also specifically included two enzyme systems, thermophilic alcohol dehydrogenase (ht-ADH) and thermolysin which we previously studied when discussing temperature dependent protein conformational sampling.

Stability of Ligand-induced Protein Conformation Influences Affinity in Maltose-binding Protein

Our understanding of what determines ligand affinity of proteins is poor, even with high-resolution structures available. Both the non-covalent ligand-protein interactions and the relative free energies of available conformations contribute to the affinity of a protein for a ligand. Distant, non-binding site residues can influence the ligand affinity by altering the free energy difference between a ligand-free and ligand-bound conformation. Our hypothesis is that when different ligands induce distinct ligand-bound conformations, it should be possible to tweak their affinities by changing the free energies of the available conformations. We tested this idea for the maltose-binding protein (MBP) from Escherichia coli. We used single-molecule Förster resonance energy transfer (smFRET) to distinguish several unique ligand-bound conformations of MBP. We engineered mutations, distant from the binding site, to affect the stabilities of different ligand-bound conformations. We show that ligand affinity can indeed be altered in a conformation-dependent manner. Our studies provide a framework for the tuning of ligand affinity, apart from modifying binding site residues.

Parameters and determinants of responses to selection in antibody libraries

The sequences of antibodies from a given repertoire are highly diverse at few sites located on the surface of a genome-encoded larger scaffold. The scaffold is often considered to play a lesser role than highly diverse, non-genome-encoded sites in controlling binding affinity and specificity. To gauge the impact of the scaffold, we carried out quantitative phage display experiments where we compare the response to selection for binding to four different targets of three different antibody libraries based on distinct scaffolds but harboring the same diversity at randomized sites. We first show that the response to selection of an antibody library may be captured by two measurable parameters. Second, we provide evidence that one of these parameters is determined by the degree of affinity maturation of the scaffold, affinity maturation being the process by which antibodies accumulate somatic mutations to evolve towards higher affinities during the natural immune response. In all cases, we find that libraries of antibodies built around maturated scaffolds have a lower response to selection to other arbitrary targets than libraries built around germline-based scaffolds. We thus propose that germline-encoded scaffolds have a higher selective potential than maturated ones as a consequence of a selection for this potential over the long-term evolution of germline antibody genes. Our results are a first step towards quantifying the evolutionary potential of biomolecules.

Antibodies in the immune system consist of a genetically encoded scaffold that exposes a few highly diverse, non-genetically encoded sites. This focused diversity is sufficient to produce antibodies that bind to any target molecule. To understand the role of the scaffold, which acquires hypermutations during the immune response, over the selective response, we analyze quantitative in vitro experiments where large antibody populations based on different scaffolds are selected against different targets. We show that selective responses are described statistically by two parameters, one of which depends on prior evolution of the scaffold as part of a previous response. Our work provides methods to assay whether naïve antibody scaffolds are endowed with a distinctively high selective potential.

Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics

Multidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. To date, such efforts usually focus on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report a multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We develop a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative signs and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and β-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers insights for engineering P450s.

Connecting conformational dynamics and epistasis has so far been limited to a few proteins and a single fitness trait. Here, the authors provide evidence of positive epistasis on multiple catalytic traits in the evolution and dynamics of engineered cytochrome P450 monooxygenase, offering insights for in silico protein design.

The evolution and engineering of enzyme activity through tuning conformational landscapes

Proteins are dynamic molecules whose structures consist of an ensemble of conformational states. Dynamics contribute to protein function and a link to protein evolution has begun to emerge. This increased appreciation for the evolutionary impact of conformational sampling has grown from our developing structural biology capabilities and the exploration of directed evolution approaches, which have allowed evolutionary trajectories to be mapped. Recent studies have provided empirical examples of how proteins can evolve via conformational landscape alterations. Moreover, minor conformational substates have been shown to be involved in the emergence of new enzyme functions as they can become enriched through evolution. The role of remote mutations in stabilizing new active site geometries has also granted insight into the molecular basis underpinning poorly understood epistatic effects that guide protein evolution. Finally, we discuss how the growth of our understanding of remote mutations is beginning to refine our approach to engineering enzymes.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

How directed evolution reshapes the energy landscape in an enzyme to boost catalysis

The advent of biocatalysts designed computationally and optimized by laboratory evolution provides an opportunity to explore molecular strategies for augmenting catalytic function. Applying a suite of nuclear magnetic resonance, crystallography, and stopped-flow techniques to an enzyme designed for an elementary proton transfer reaction, we show how directed evolution gradually altered the conformational ensemble of the protein scaffold to populate a narrow, highly active conformational ensemble and accelerate this transformation by nearly nine orders of magnitude. Mutations acquired during optimization enabled global conformational changes, including high-energy backbone rearrangements, that cooperatively organized the catalytic base and oxyanion stabilizer, thus perfecting transition-state stabilization. The development of protein catalysts for many chemical transformations could be facilitated by explicitly sampling conformational substates during design and specifically stabilizing productive substates over all unproductive conformations.

Conformational Landscapes of Halohydrin Dehalogenases and Their Accessible Active Site Tunnels

Halohydrin dehalogenases (HHDH) are industrially relevant biocatalysts exhibiting a promiscuous epoxide-ring opening reactivity in the presence of small nucleophiles, thus giving access to novel carbon–carbon, carbon–oxygen, carbon–nitrogen, and carbon–sulfur bonds. Recently, the repertoire of HHDH has been expanded, providing access to some novel HHDH subclasses exhibiting a broader epoxide substrate scope. In this work, we develop a computational approach based on the application of linear and non-linear dimensionality reduction techniques to long time-scale Molecular Dynamics (MD) simulations to study the HHDH conformational landscapes. We couple the analysis of the conformational landscapes to CAVER calculations to assess their impact on the active site tunnels and potential ability towards bulky epoxide ring opening reaction. Our study indicates that the analyzed HHDHs subclasses share a common breathing motion of the halide binding pocket, but present large deviations in the loops adjacent to the active site pocket and N-terminal regions. Such conformational differences affect the available tunnels for epoxide binding to the active site. The superior activity of the HHDH G subclass towards bulkier substrates is explained by the additional structural elements delimiting the active site region, its rich conformational heterogeneity, and the substantially wider and frequently observed active site tunnels. This study therefore provides key information for HHDH promiscuity and engineering.

Exposing the distinctive modular behavior of β-strands and α-helices in folded proteins

Although folded proteins are commonly depicted as simplistic combinations of β-strands and α-helices, the actual properties and functions of these secondary-structure elements in their native contexts are just partly understood. The principal reason is that the behavior of individual β- and α-elements is obscured by the global folding cooperativity. In this study, we have circumvented this problem by designing frustrated variants of the mixed α/β-protein S6, which allow the structural behavior of individual β-strands and α-helices to be targeted selectively by stopped-flow kinetics, X-ray crystallography, and solution-state NMR. Essentially, our approach is based on provoking intramolecular "domain swap." The results show that the α- and β-elements have quite different characteristics: The swaps of β-strands proceed via global unfolding, whereas the α-helices are free to swap locally in the native basin. Moreover, the α-helices tend to hybridize and to promote protein association by gliding over to neighboring molecules. This difference in structural behavior follows directly from hydrogen-bonding restrictions and suggests that the protein secondary structure defines not only tertiary geometry, but also maintains control in function and structural evolution. Finally, our alternative approach to protein folding and native-state dynamics presents a generally applicable strategy for in silico design of protein models that are computationally testable in the microsecond-millisecond regime.

Asymmetric dynamic coupling promotes alternative evolutionary pathways in an enzyme dimer

The importance of dynamic factors in enzyme evolution is gaining recognition. Here we study how the evolution of a new enzymatic activity exploits conformational tinkering and demonstrate that conversion of a dimeric phosphotriesterase to an arylesterase in Pseudomonas diminuta is accompanied by structural divergence between the two subunits. Deviations in loop conformations increase with promiscuity, leading to functionally distinct states, while they decrease during specialisation for the new function. We show that opposite loop movements in the two subunits are due to a dynamic coupling with the dimer interface, the importance of which is also corroborated by the co-evolution of the loop and interface residues. These results illuminate how protein dynamics promotes conformational heterogeneity in a dimeric enzyme, leading to alternative evolutionary pathways for the emergence of a new function.

Adaptability and specificity: how do proteins balance opposing needs to achieve function?

Many proteins select from a small repertoire of 3-dimensional folds retained over evolutional timescales and recruited for different functions, with changes in local structure and sequence to enable specificity. Recent studies have revealed the evolutionary constraints on protein dynamics to achieve function. The significance of protein dynamics in simultaneously satisfying conformational flexibility/malleability and stability/precision requirements becomes clear upon dissecting the spectrum of equilibrium motions accessible to fold families. Accessibility to highly conserved global modes of motions shared by family members, to low-to-intermediate-frequency modes that distinguish subfamilies and confer specificity, and to conserved high-frequency modes ensuring chemical precision and core stability underlies functional specialization while exploiting highly versatile folds. These design principles are illustrated for the family of PDZ domains.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.

Harnessing Conformational Plasticity to Generate Designer Enzymes

Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.

Comprehensive exploration of the translocation, stability and substrate recognition requirements in VIM-2 lactamase

Metallo-β-lactamases (MBLs) degrade a broad spectrum of β-lactam antibiotics, and are a major disseminating source for multidrug resistant bacteria. Despite many biochemical studies in diverse MBLs, molecular understanding of the roles of residues in the enzyme's stability and function, and especially substrate specificity, is lacking. Here, we employ deep mutational scanning (DMS) to generate comprehensive single amino acid variant data on a major clinical MBL, VIM-2, by measuring the effect of thousands of VIM-2 mutants on the degradation of three representative classes of β-lactams (ampicillin, cefotaxime, and meropenem) and at two different temperatures (25°C and 37°C). We revealed residues responsible for expression and translocation, and mutations that increase resistance and/or alter substrate specificity. The distribution of specificity-altering mutations unveiled distinct molecular recognition of the three substrates. Moreover, these function-altering mutations are frequently observed among naturally occurring variants, suggesting that the enzymes have continuously evolved to become more potent resistance genes.

Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method

To facilitate investigations of protein-protein interactions (PPIs), we developed a novel platform for quantitative mapping of protein binding specificity landscapes, which combines the multi-target screening of a mutagenesis library into high- and low-affinity populations with sophisticated next-generation sequencing analysis. Importantly, this method generates accurate models to predict affinity and specificity values for any mutation within a protein complex, and requires only a few experimental binding affinity measurements using purified proteins for calibration. We demonstrated the utility of the approach by mapping quantitative landscapes for interactions between the N-terminal domain of the tissue inhibitor of metalloproteinase 2 (N-TIMP2) and three matrix metalloproteinases (MMPs) having homologous structures but different affinities (MMP-1, MMP-3, and MMP-14). The binding landscapes for N-TIMP2/MMP-1 and N-TIMP2/MMP-3 showed the PPIs to be almost fully optimized, with most single mutations giving a loss of affinity. In contrast, the non-optimized PPI for N-TIMP2/MMP-14 was reflected in a wide range of binding affinities, where single mutations exhibited a far more attenuated effect on the PPI. Our new platform reliably and comprehensively identified not only hot- and cold-spot residues, but also specificity-switch mutations that shape target affinity and specificity. Thus, our approach provides a methodology giving an unprecedentedly rich quantitative analysis of the binding specificity landscape, which will broaden the understanding of the mechanisms and evolutionary origins of specific PPIs and facilitate the rational design of specific inhibitors for structurally similar target proteins.