Engineering the protein dynamics of an ancestral luciferase

Noninvasive Optogenetics Realized by iPSC-Derived Tentacled Carrier in Alzheimer's Disease Treatment

Neural-activated optogenetics technique contributing to the "restart" of degenerative neurons offers hope for the treatment of several neurodegenerative diseases. However, the limitations of persistent invasiveness and inadequate repair of the pathological environment strongly hinder its further application. Here, a concept of differentiating stem cells is proposed to produce functional materials to enhance the therapeutic applicability of optogenetics. Induced pluripotent stem cells (iPSCs) are differentiated to generate the "tentacled" stem cells TenSCs. Their "tentacled" vesicles TenSCev, upon inheriting the biological functions of the parent cell, will possess both neural targeting capacity and pathological environment repair ability. Hence, TenSCev are utilized as functional carrier to deliver optogenetics elements for completely non-traumatic and controllable neuron activation, while also facilitating the comprehensive restoration of the pathological environment, thus effectively halted disease progression and significantly improved cognitive function in Alzheimer's disease or aged mice. Further, the concept of generating specialized biomaterials from differentiated stem cells as functional carriers holds the potential to broaden the applicability of various neuroregulatory techniques in the treatment of neurological disorders.

Quantitative Measurement of Molecular Permeability to a Synthetic Bacterial Microcompartment Shell System

Naturally evolved and synthetically designed forms of compartmentalization benefit encapsulated function by increasing local concentrations of substrates and protecting cargo from destabilizing environments and inhibitors. Crucial to understanding the fundamental principles of compartmentalization are experimental systems enabling the measurement of the permeability rates of small molecules. Here, we report the experimental measurement of the small-molecule permeability of a 40 nm icosahedral bacterial microcompartment shell. This was accomplished by heterologous loading of light-producing luciferase enzymes and kinetic measurement of luminescence using stopped-flow spectrophotometry. Compared to free enzyme, the luminescence signal kinetics was slower when the luciferase was encapsulated in bacterial microcompartment shells. The results indicate that substrates and products can still exchange across the shell, and modeling of the experimental data suggest that a 50× permeability rate increase occurs when shell vertices were vacant. Overall, our results suggest design considerations for the construction of heterologous bacterial microcompartment shell systems and compartmentalized function at the nanoscale.

Functional Characterization of Luciferase in a Brittle Star Indicates Parallel Evolution Influenced by Genomic Availability of Haloalkane Dehalogenase

Determining why convergent traits use distinct versus shared genetic components is crucial for understanding how evolutionary processes generate and sustain biodiversity. However, the factors dictating the genetic underpinnings of convergent traits remain incompletely understood. Here, we use heterologous protein expression, biochemical assays, and phylogenetic analyses to confirm the origin of a luciferase gene from haloalkane dehalogenases in the brittle star Amphiura filiformis. Through database searches and gene tree analyses, we also show a complex pattern of the presence and absence of haloalkane dehalogenases across organismal genomes. These results first confirm parallel evolution across a vast phylogenetic distance, because octocorals like Renilla also use luciferase derived from haloalkane dehalogenases. This parallel evolution is surprising, even though previously hypothesized, because many organisms that also use coelenterazine as the bioluminescence substrate evolved completely distinct luciferases. The inability to detect haloalkane dehalogenases in the genomes of several bioluminescent groups suggests that the distribution of this gene family influences its recruitment as a luciferase. Together, our findings highlight how biochemical function and genomic availability help determine whether distinct or shared genetic components are used during the convergent evolution of traits like bioluminescence.

Physics-based modeling in the new era of enzyme engineering

Enzyme engineering is entering a new era characterized by the integration of computational strategies. While bioinformatics and artificial intelligence methods have been extensively applied to accelerate the screening of function-enhancing mutants, physics-based modeling methods, such as molecular mechanics and quantum mechanics, are essential complements in many objectives. In this Perspective, we highlight how physics-based modeling will help the field of computational enzyme engineering reach its full potential by exploring current developments, unmet challenges and emerging opportunities for tool development.

Deep indel mutagenesis reveals the impact of amino acid insertions and deletions on protein stability and function

Amino acid insertions and deletions (indels) are an abundant class of genetic variants. However, compared to substitutions, the effects of indels on protein stability are not well understood. To better understand indels here we analyse new and existing large-scale deep indel mutagenesis (DIM) of structurally diverse proteins. The effects of indels on protein stability vary extensively among and within proteins and are not well predicted by existing computational methods. To address this shortcoming we present INDELi, a series of models that combine experimental or predicted substitution effects and secondary structure information to provide good prediction of the effects of indels on both protein stability and pathogenicity. Moreover, quantifying the effects of indels on protein-protein interactions suggests that insertions can be an important class of gain-of-function variants. Our results provide an overview of the impact of indels on proteins and a method to predict their effects genome-wide.

Automated Engineering Protein Dynamics via Loop Grafting: Improving Renilla Luciferase Catalysis

Engineering protein dynamics is a challenging and unsolved problem in protein design. Loop transplantation or loop grafting has been previously employed to transfer dynamic properties between proteins. We recently released a LoopGrafter Web server to execute the loop grafting task, employing eight computational tools and one database. The LoopGrafter method relies on the prediction of the local dynamic behavior of the elements to be transplanted and has successfully reconstructed previously engineered sequences. However, it was unclear whether catalytically competitive previously uncharacterized designs could be obtained by this method. Here, we address this question, showing how LoopGrafter generates viable loop-grafted chimeras of luciferases, how these chimeras encompass the activity of interest and unique kinetic properties, and how all this process is done fully automatically and agnostic of any previous knowledge. All constructed designs proved to be catalytically active, and the most active one improved the activity of the template enzyme by 4 orders of magnitude. The computational details and parameter optimization of the sequence pairing step of the LoopGrafter workflow are revealed. The optimized and experimentally validated loop grafting workflow available as a fully automated Web server represents a powerful approach for engineering catalytically efficient enzymes by modification of protein dynamics.

Electrically Driven, Bioluminescent Compliant Devices for Soft Robotics

Soft robotics, a research field wherein robots are fabricated from compliant materials, has sparked widespread research interest because of its potential applications in a variety of scenarios. In soft robots, luminescence is an important functionality for communication and information transmission, and it is typically achieved through electroluminescence, which relies on synthetic substances activated by external electric sources, such as batteries. This paper focuses on the use of luciferase, a biologically derived luminescent enzyme, as a luminescent material. Bioluminescence, which is triggered by the luciferin-luciferase reaction, is highly energy-efficient, nontoxic, and eco-friendly. In this regard, a mammalian cell-derived secreted luciferase bioluminescent liquid was developed. This bioluminescent liquid is strongly bright, stable, freezable, and scalable for use as a soft robotic material. To investigate the applicability of this bioluminescent liquid to soft robotics, it was incorporated as an electrode in electrically driven soft actuators, sensors, and robots. Specifically, dielectric elastomer sensors (DESs) and dielectric elastomer actuators (DEAs) were fabricated and characterized using established fabrication processes. The resistivity of the bioluminescent liquid was found to be 448.1 Ω·cm. When the DES was subjected to uniaxial strain, it exhibited a linear response and large deformation of up to 200% strain, with a simultaneous luminance change of 27%. The DEA displayed an areal strain of 46.0% and a luminance change of 31% at an applied voltage of 3.4 kV. The waterproof bending DEA generated a tip angle of 21.8° at 10 kV and was applied to a jellyfish robot that could swim in water at a speed of 2.1 mm/s. The experimental results demonstrated the successful operation of these devices, validating the concept of energy-efficient, safe, and environmentally friendly bioluminescent soft robots.

Systematic characterization of indel variants using a yeast-based protein folding sensor

Gene variants resulting in insertions or deletions of amino acid residues (indels) have important consequences for evolution and are often linked to disease, yet, compared to missense variants, the effects of indels are poorly understood and predicted. We developed a sensitive protein folding sensor based on the complementation of uracil auxotrophy in yeast by circular permutated orotate phosphoribosyltransferase (CPOP). The sensor reports on the folding of disease-linked missense variants and de-novo-designed proteins. Applying the folding sensor to a saturated library of single-residue indels in human dihydrofolate reductase (DHFR) revealed that most regions that tolerate indels are confined to internal loops, the termini, and a central α helix. Several indels are temperature sensitive, and folding is rescued upon binding to methotrexate. Rosetta and AlphaFold2 predictions correlate with the observed effects, suggesting that most indels destabilize the native fold and that these computational tools are useful for the classification of indels observed in population sequencing.

Visual Support for the Loop Grafting Workflow on Proteins

In understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on exploring regions in proteins called loops. Analyzing various characteristics of these regions helps the experts design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. We designed a set of interactive visualizations that provide experts with visual support through all the loop grafting pipeline steps. The workflow is divided into several phases, reflecting the steps of the pipeline. Each phase is supported by a specific set of abstracted 2D visual representations of proteins and their loops that are interactively linked with the 3D View of proteins. By sequentially passing through the individual phases, the user shapes the list of loops that are potential candidates for loop grafting. Finally, the actual in-silico insertion of the loop candidates from one protein to the other is performed, and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. We showcase the contribution of our visual support design on a real case scenario changing the enantiomer selectivity of the engineered enzyme. Moreover, we provide the readers with the experts' feedback.

Ancestral Sequence Reconstruction for Designing Biocatalysts and Investigating their Functional Mechanisms

Biocatalysis has emerged as a green approach for efficient and sustainable production in various industries. In recent decades, numerous advancements in computational and predictive approaches, including ancestral sequence reconstruction (ASR) have sparked a new wave for protein engineers to improve and expand biocatalyst capabilities. ASR is an evolution-based strategy that uses phylogenetic relationships among homologous extant sequences to probabilistically infer the most likely ancestral sequences. It has proven to be a powerful tool with applications ranging from creating highly stable enzymes for direct applications to preparing moderately active robust protein scaffolds for further enzyme engineering. Intriguingly, it can also provide insights into fundamental aspects that are challenging to study with extant (current) enzymes. This Perspective discusses a practical strategy for guiding enzyme engineers on how to embrace ASR as a practical or associated protocol for protein engineering and highlights recent examples of using ASR in various applications, including increasing thermostability, expanding promiscuity, fine-tuning selectivity and function, and investigating mechanistic and evolution aspects. We believe that the use of the ASR approach will continue to contribute to the ongoing development of the biocatalysis field. We have been in a "golden era" for biocatalysis in which numerous useful enzymes have been developed through many waves of enzyme engineering via advancements in computational methodologies.

Structural and energetic analysis of stabilizing indel mutations

Amino acid insertions and deletions (indels) are among the most common protein mutations and necessitate changes to a protein's backbone geometry. Examining how indels affect protein folding stability (and especially how indels can increase stability) can help reveal the role of backbone energetics on stability and introduce new protein engineering strategies. Tsuboyama et al. measured folding stability for 57,698 single amino acid insertion or deletion mutants in 405 small domains, and this analysis identified 103 stabilizing mutants (ΔΔGunfolding > 1 kcal/mol). Here, we use computational modeling to analyze structural and energetic changes for these stabilizing indel mutants. We find that stabilizing indel mutations tend to have local structural effects and that stabilizing deletions (but less so insertions) are often found in regions of high backbone strain. We also find that stabilizing indels are typically correctly classified as stabilizing by the Rosetta energy function (which explicitly models backbone energetics), but not by an inverse folding (ESM-IF)-based analysis (Cagiada et al. 2024) which predicts absolute stability (ΔGunfolding).

Engineering the native ensemble to tune protein function: Diverse mutational strategies and interlinked molecular mechanisms

Natural proteins are fragile entities, intrinsically sensitive to perturbations both at the level of sequence and their immediate environment. Here, we highlight the diverse strategies available for engineering function through mutations influencing backbone conformational entropy, charge-charge interactions, and in the loops and hinge regions, many of which are located far from the active site. It thus appears that there are potentially numerous ways to microscopically vary the identity of residues and the constituent interactions to tune function. Functional modulation could occur via changes in native-state stability, altered thermodynamic coupling extents within the folded structure, redistributed dynamics, or through modulation of the population of conformational substates. As these mechanisms are intrinsically linked and given the pervasive long-range effects of mutations, it is crucial to consider the interaction network as a whole and fully map the native conformational landscape to place mutational effects in the context of allostery and protein evolution.

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.

Functional characterization of luciferase in a brittle star indicates parallel evolution influenced by genomic availability of haloalkane dehalogenase

Determining why convergent traits use distinct versus shared genetic components is crucial for understanding how evolutionary processes generate and sustain biodiversity. However, the factors dictating the genetic underpinnings of convergent traits remain incompletely understood. Here, we use heterologous protein expression, biochemical assays, and phylogenetic analyses to confirm the origin of a luciferase gene from haloalkane dehalogenases in the brittle star Amphiura filiformis . Through database searches and gene tree analyses, we also show a complex pattern of presence and absence of haloalkane dehalogenases across organismal genomes. These results first confirm parallel evolution across a vast phylogenetic distance, because octocorals like Renilla also use luciferase derived from haloalkane dehalogenases. This parallel evolution is surprising, even though previously hypothesized, because many organisms that also use coelenterazine as the bioluminescence substrate evolved completely distinct luciferases. The inability to detect haloalkane dehalogenases in the genomes of several bioluminescent groups suggests that the distribution of this gene family influences its recruitment as a luciferase. Together, our findings highlight how biochemical function and genomic availability help determine whether distinct or shared genetic components are used during the convergent evolution of traits like bioluminescence.

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

Conformational Modulation of a Mobile Loop Controls Catalysis in the (βα)8-Barrel Enzyme of Histidine Biosynthesis HisF

The overall significance of loop motions for enzymatic activity is generally accepted. However, it has largely remained unclear whether and how such motions can control different steps of catalysis. We have studied this problem on the example of the mobile active site β1α1-loop (loop1) of the (βα)8-barrel enzyme HisF, which is the cyclase subunit of imidazole glycerol phosphate synthase. Loop1 variants containing single mutations of conserved amino acids showed drastically reduced rates for the turnover of the substrates N'-[(5'-phosphoribulosyl) formimino]-5-aminoimidazole-4-carboxamide ribonucleotide (PrFAR) and ammonia to the products imidazole glycerol phosphate (ImGP) and 5-aminoimidazole-4-carboxamide-ribotide (AICAR). A comprehensive mechanistic analysis including stopped-flow kinetics, X-ray crystallography, NMR spectroscopy, and molecular dynamics simulations detected three conformations of loop1 (open, detached, closed) whose populations differed between wild-type HisF and functionally affected loop1 variants. Transient stopped-flow kinetic experiments demonstrated that wt-HisF binds PrFAR by an induced-fit mechanism whereas catalytically impaired loop1 variants bind PrFAR by a simple two-state mechanism. Our findings suggest that PrFAR-induced formation of the closed conformation of loop1 brings active site residues in a productive orientation for chemical turnover, which we show to be the rate-limiting step of HisF catalysis. After the cyclase reaction, the closed loop conformation is destabilized, which favors the formation of detached and open conformations and hence facilitates the release of the products ImGP and AICAR. Our data demonstrate how different conformations of active site loops contribute to different catalytic steps, a finding that is presumably of broad relevance for the reaction mechanisms of (βα)8-barrel enzymes and beyond.

Molecular Dynamics Simulation Combined with Neural Relationship Inference and Markov Model to Reveal the Relationship between Conformational Regulation and Bioluminescence Properties of Gaussia Luciferase

Gaussia luciferase (Gluc) is currently known as the smallest naturally secreted luciferase. Due to its small molecular size, high sensitivity, short half-life, and high secretion efficiency, it has become an ideal reporter gene and is widely used in monitoring promoter activity, studying protein-protein interactions, protein localization, high-throughput drug screening, and real-time monitoring of tumor occurrence and development. Although studies have shown that different Gluc mutations exhibit different bioluminescent properties, their mechanisms have not been further investigated. The purpose of this study is to reveal the relationship between the conformational changes of Gluc mutants and their bioluminescent properties through molecular dynamics simulation combined with neural relationship inference (NRI) and Markov models. Our results indicate that, after binding to the luciferin coelenterazine (CTZ), the α-helices of the 109-119 residues of the Gluc Mutant2 (GlucM2, the flash-type mutant) are partially unraveled, while the α-helices of the same part of the Gluc Mutant1 (GlucM1, the glow-type mutant) are clearly formed. The results of Markov flux analysis indicate that the conformational differences between glow-type and flash-type mutants when combined with luciferin substrate CTZ mainly involve the helicity change of α7. The most representative conformation and active pocket distance analysis indicate that compared to the flash-type mutant GlucM2, the glow-type mutant GlucM1 has a higher degree of active site closure and tighter binding. In summary, we provide a theoretical basis for exploring the relationship between the conformational changes of Gluc mutants and their bioluminescent properties, which can serve as a reference for the modification and evolution of luciferases.

Exploring the Potential of Arginine to Increase Coelenterazine-Renilla Luciferase Affinity and Enzyme Stability: Kinetic and Molecular Dynamics Studies

Renilla luciferase catalyzes the oxidation of coelenterazine to coelenteramide and results in the emission of a photon of light. Although Renilla luciferase has various applications in biotechnology, its low thermal stability limits the development of its applications. Arginine is a well-known stabilizing amino acid that plays a key role in protein stabilization against inactivation. However, its impact on enzyme properties is unpredictable. This study investigates the impact of arginine on the kinetics and thermal stability of Renilla luciferase. The enzyme's performance was significantly enhanced in the presence of arginine, with catalytic efficiency increasing by 3.31-fold and 3.08-fold when exposed to 0.2 M and 0.3 M arginine, respectively. Additionally, arginine improved the thermal stability of Renilla luciferase. Molecular dynamics simulation showed that the addition of 0.2 M arginine reduced the binding of coelenteramide, the reaction product and an enzyme inhibitor, to the active site of the Renilla luciferase. Therefore, the release of the product was accelerated, and the affinity of Renilla luciferase for coelenterazine increased. Furthermore, Molecular dynamics studies indicated an increased network of water molecules surrounding Renilla luciferase in the presence of 0.2 M arginine. This network potentially enhances the hydrophobic effect on the protein structure, ultimately improving enzyme stability. The findings of this study hold promise for the development of commercial kits incorporating Renilla luciferase.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

Exploring the Light-Emitting Agents in Renilla Luciferases by an Effective QM/MM Approach

Bioluminescence is a fascinating natural phenomenon, wherein organisms produce light through specific biochemical reactions. Among these organisms, Renilla luciferase (RLuc) derived from the sea pansy Renilla reniformis is notable for its blue light emission and has potential applications in bioluminescent tagging. Our study focuses on RLuc8, a variant of RLuc with eight amino acid substitutions. Recent studies have shown that the luminescent emitter coelenteramide can adopt multiple protonation states, which may be influenced by nearby residues at the enzyme's active site, demonstrating a complex interplay between protein structure and bioluminescence. Herein, using the quantum mechanical consistent force field method and the semimacroscopic protein dipole-Langevin dipole method with linear response approximation, we show that the phenolate state of coelenteramide in RLuc8 is the primary light-emitting species in agreement with experimental results. Our calculations also suggest that the proton transfer (PT) from neutral coelenteramide to Asp162 plays a crucial role in the bioluminescence process. Additionally, we reproduced the observed emission maximum for the amide anion in RLuc8-D120A and the pyrazine anion in the presence of a Na+ counterion in RLuc8-D162A, suggesting that these are the primary emitters. Furthermore, our calculations on the neutral emitter in the engineered AncFT-D160A enzyme, structurally akin to RLuc8-D162A but with a considerably blue-shifted emission peak, aligned with the observed data, possibly explaining the variance in emission peaks. Overall, this study demonstrates an effective approach to investigate chromophores' bimolecular states while incorporating the PT process in emission spectra calculations, contributing valuable insights for future studies of PT in photoproteins.

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.

Temporal Resolution of Activity-Related Solvation Dynamics in the TIM Barrel Enzyme Murine Adenosine Deaminase

Murine adenosine deaminase (mADA) is a prototypic system for studying the thermal activation of active site chemistry within the TIM barrel family of enzyme reactions. Previous temperature-dependent hydrogen deuterium exchange studies under various conditions have identified interconnected thermal networks for heat transfer from opposing protein-solvent interfaces to active site residues in mADA. One of these interfaces contains a solvent exposed helix-loop-helix moiety that presents the hydrophobic face of its long α-helix to the backside of bound substrate. Herein we pursue the time and temperature dependence of solvation dynamics at the surface of mADA, for comparison to established kinetic parameters that represent active site chemistry. We first created a modified protein devoid of native tryptophans with close to native kinetic behavior. Single site-specific tryptophan mutants were back inserted into each of the four positions where native tryptophans reside. Measurements of nanosecond fluorescence relaxation lifetimes and Stokes shift decays, that reflect time dependent environmental reoroganization around the photo-excited state of Trp*, display minimal temperature dependences. These regions serve as controls for the behavior of a new single tryptophan inserted into a solvent exposed region near the helix-loop-helix moiety located behind the bound substrate, Lys54Trp. This installed Trp displays a significantly elevated value for Ea ( k Stokes shift ) ; further, when Phe61 within the long helix positioned behind bound substrate is replaced by a series of aliphatic hydrophobic side chains, the trends in Ea ( k Stokes shift ) mirror the earlier reported impact of the same series of function-altering hydrophobic side chains on the activation energy of catalysis, Ea ( k cat ) .The reported experimental findings implicate a solvent initiated and rapid (>ns) protein restructuring that contributes to the enthalpic activation barrier to catalysis in mADA.

Identification of the Thermal Activation Network in Human 15-Lipoxygenase-2: Divergence from Plant Orthologs and Its Relationship to Hydrogen Tunneling Activation Barriers

The oxidation of polyunsaturated fatty acids by lipoxygenases (LOXs) is initiated by a C-H cleavage step in which the hydrogen atom is transferred quantum mechanically (i.e., via tunneling). In these reactions, protein thermal motions facilitate the conversion of ground-state enzyme-substrate complexes to tunneling-ready configurations and are thus important for transferring energy from the solvent to the active site for the activation of catalysis. In this report, we employed temperature-dependent hydrogen-deuterium exchange mass spectrometry (TDHDX-MS) to identify catalytically linked, thermally activated peptides in a representative animal LOX, human epithelial 15-LOX-2. TDHDX-MS of wild-type 15-LOX-2 was compared to two active site mutations that retain structural stability but have increased activation energies (Ea) of catalysis. The Ea value of one variant, V427L, is implicated to arise from suboptimal substrate positioning by increased active-site side chain rotamer dynamics, as determined by X-ray crystallography and ensemble refinement. The resolved thermal network from the comparative Eas of TDHDX-MS between wild-type and V426A is localized along the front face of the 15-LOX-2 catalytic domain. The network contains a clustering of isoleucine, leucine, and valine side chains within the helical peptides. This thermal network of 15-LOX-2 is different in location, area, and backbone structure compared to a model plant lipoxygenase from soybean that exhibits a low Ea value of catalysis compared to the human ortholog. The presented data provide insights into the divergence of thermally activated protein motions in plant and animal LOXs and their relationships to the enthalpic barriers for facilitating hydrogen tunneling.

Bioluminescence as a functional tool for visualizing and controlling neuronal activity in vivo

The use of bioluminescence as a reporter for physiology in neuroscience is as old as the discovery of the calcium-dependent photon emission of aequorin. Over the years, luciferases have been largely replaced by fluorescent reporters, but recently, the field has seen a renaissance of bioluminescent probes, catalyzed by unique developments in imaging technology, bioengineering, and biochemistry to produce luciferases with previously unseen colors and intensity. This is not surprising as the advantages of bioluminescence make luciferases very attractive for noninvasive, longitudinal in vivo observations without the need of an excitation light source. Here, we review how the development of dedicated and specific sensor-luciferases afforded, among others, transcranial imaging of calcium and neurotransmitters, or cellular metabolites and physical quantities such as forces and membrane voltage. Further, the increased versatility and light output of luciferases have paved the way for a new field of functional bioluminescence optogenetics, in which the photon emission of the luciferase is coupled to the gating of a photosensor, e.g., a channelrhodopsin and we review how they have been successfully used to engineer synthetic neuronal connections. Finally, we provide a primer to consider important factors in setting up functional bioluminescence experiments, with a particular focus on the genetic model Caenorhabditis elegans, and discuss the leading challenges that the field needs to overcome to regain a competitive advantage over fluorescence modalities. Together, our paper caters to experienced users of bioluminescence as well as novices who would like to experience the advantages of luciferases in their own hand.

A billion years of evolution manifest in nanosecond protein dynamics

Protein dynamics form a critical bridge between protein structure and function, yet the impact of evolution on ultrafast processes inside proteins remains enigmatic. This study delves deep into nanosecond-scale protein dynamics of a structurally and functionally conserved protein across species separated by almost a billion years, investigating ten homologs in complex with their ligand. By inducing a photo-triggered destabilization of the ligand inside the binding pocket, we resolved distinct kinetic footprints for each homolog via transient infrared spectroscopy. Strikingly, we found a cascade of rearrangements within the protein complex which manifest in time points of increased dynamic activity conserved over hundreds of millions of years within a narrow window. Among these processes, one displays a subtle temporal shift correlating with evolutionary divergence, suggesting reduced selective pressure in the past. Our study not only uncovers the impact of evolution on molecular processes in a specific case, but has also the potential to initiate a field of scientific inquiry within molecular paleontology, where species are compared and classified based on the rapid pace of protein dynamic processes; a field which connects the shortest conceivable time scale in living matter (10[Formula: see text] s) with the largest ones (10[Formula: see text] s).

Uncovering the role of sorbitol in Renilla luciferase kinetics: Insights from spectroscopic and molecular dynamics studies

Renilla luciferase catalyzes the oxidation of coelenterazine to coelenteramide, resulting in the emission of a photon of light. This study investigated the impact of sorbitol on the structural and kinetic properties of Renilla luciferase using circular dichroism, fluorescence spectroscopy, and molecular dynamics simulations. Our investigation, carried out using circular dichroism and fluorescence analyses, as well as a thermal stability assay, has revealed that sorbitol induces conformational changes in the enzyme but does not improve its thermal stability. Moreover, through kinetic studies, it has been demonstrated that at a concentration of 0.4 M, sorbitol enhances the catalytic efficiency of Renilla luciferase. However, at higher concentrations, sorbitol results in a decrease in catalytic efficiency. Additionally, molecular dynamics simulations have shown that sorbitol increases the presence of hydrophobic pockets on the enzyme's surface. These simulations have also provided evidence that at a concentration of 0.4 M, sorbitol facilitates substrate access to the active site of the enzyme. Nevertheless, at higher concentrations, sorbitol obstructs substrate trafficking, most likely due to its impact on the gateway to the active site. This study may provide insights into the kinetic changes observed in enzymes with buried active sites, such as those with α/β hydrolase fold.

Engineering Substrate Promiscuity of Nucleoside Phosphorylase Via an Insertions-Deletions Strategy

Nucleoside phosphorylases (NPs) are the key enzymes in the nucleoside metabolism pathway and are widely employed for the synthesis of nucleoside analogs, which are difficult to access via conventional synthetic methods. NPs are generally classified as purine nucleoside phosphorylase (PNP) and pyrimidine or uridine nucleoside phosphorylase (PyNP/UP), based on their substrate preference. Here, based on the evolutionary information on the NP-I family, we adopted an insertions-deletions (InDels) strategy to engineer the substrate promiscuity of nucleoside phosphorylase AmPNPΔS2V102 K, which exhibits both PNP and UP activities from a trimeric PNP (AmPNP) of Aneurinibacillus migulanus. Furthermore, the AmPNPΔS2V102 K exerted phosphorylation activities toward arabinose nucleoside, fluorosyl nucleoside, and dideoxyribose, thereby broadening the unnatural-ribose nucleoside substrate spectrum of AmPNP. Finally, six purine nucleoside analogues were successfully synthesized, using the engineered AmPNPΔS2V102 K instead of the traditional "two-enzymes PNP/UP" approach. These results provide deep insights into the catalytic mechanisms of the PNP and demonstrate the benefits of using the InDels strategy to achieve substrate promiscuity in an enzyme, as well as broadening the substrate spectrum of the enzyme.

Uncovering the role of leucine 59 in Renilla luciferase stability and activity with error-prone PCR: Implications for protein engineering

A new variant of Renilla luciferase, named Met C-SRLuc 8, was obtained from a random mutagenesis library and expressed in Escherichia coli BL21 (DE3) plys and purified. The results of the enzyme's binding affinity, kinetic stability, and bioinformatic studies demonstrated that leucine 59, located within the hot-spot foldon in the N-terminal domain of the protein, plays a significant role in the stability and activity of Renilla luciferase. These findings may facilitate the engineering of different variants of this enzyme to achieve thermally stable versions for various biotechnological applications.

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.

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

BACKGROUND

Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families, including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax-the wavelength of maximum absorbance-which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype.

RESULTS

Here, we report a newly compiled database of all heterologously expressed opsin genes with λmax phenotypes that we call the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains 864 unique opsin genotypes and corresponding λmax phenotypes collected across all animals from 73 separate publications. We use VPOD data and deepBreaks to show regression-based machine learning (ML) models often reliably predict λmax, account for nonadditive effects of mutations on function, and identify functionally critical amino acid sites.

CONCLUSION

The ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism's ecological niche, and may be used more broadly for de novo protein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes.

Glow-type conversion and characterization of a minimal luciferase via mutational analyses

Luciferases are widely used as reporter proteins in various fields. Recently, we developed a minimal bright luciferase, picALuc, via partial deletion of the artificial luciferase (ALuc) derived from copepods luciferases. However, the structures of copepod luciferases in the substrate-bound state remain unknown. Moreover, as suggested by structural modeling, picALuc has a larger active site cavity, unlike that in other copepod luciferases. Here, to explore the bioluminescence mechanism of picALuc and its luminescence properties, we conducted multiple mutational analyses, and identified residues and regions important for catalysis and bioluminescence. Mutations of residues likely involved in catalysis (S33, H34, and D55) markedly reduced bioluminescence, whereas that of residue (E50) (near the substrate in the structural model) enhanced luminescence intensity. Furthermore, deletion mutants (Δ70-Δ78) in the loop region (around I73) exhibited longer luminescence lifetimes (~ 30 min) and were reactivated multiple times upon re-addition of the substrate. Due to the high thermostability of picALuc, one of its representative mutant (Δ74), was able to be reused, that is, luminescence recycling, for day-scale time at room temperature. These findings provide important insights into picALuc bioluminescence mechanism and copepod luciferases and may help with sustained observations in a variety of applications.

Illuminating the mechanism and allosteric behavior of NanoLuc luciferase

NanoLuc, a superior β-barrel fold luciferase, was engineered 10 years ago but the nature of its catalysis remains puzzling. Here experimental and computational techniques are combined, revealing that imidazopyrazinone luciferins bind to an intra-barrel catalytic site but also to an allosteric site shaped on the enzyme surface. Structurally, binding to the allosteric site prevents simultaneous binding to the catalytic site, and vice versa, through concerted conformational changes. We demonstrate that restructuration of the allosteric site can boost the luminescent reaction in the remote active site. Mechanistically, an intra-barrel arginine coordinates the imidazopyrazinone component of luciferin, which reacts with O2 via a radical charge-transfer mechanism, and then it also protonates the resulting excited amide product to form a light-emitting neutral species. Concomitantly, an aspartate, supported by two tyrosines, fine-tunes the blue color emitter to secure a high emission intensity. This information is critical to engineering the next-generation of ultrasensitive bioluminescent reporters.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

SBILib: a handle for protein modeling and engineering

SUMMARY

The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library.

AVAILABILITY AND IMPLEMENTATION

SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.

Designing Efficient Enzymes: Eight Predicted Mutations Convert a Hydroxynitrile Lyase into an Efficient Esterase

Hydroxynitrile lyase from rubber tree (HbHNL) shares 45% identical amino acid residues with the homologous esterase from tobacco, SABP2, but the two enzymes catalyze different reactions. The x-ray structures reveal a serine-histidine-aspartate catalytic triad in both enzymes along with several differing amino acid residues within the active site. Previous exchange of three amino acid residues in the active site of HbHNL with the corresponding amino acid residue in SABP2 (T11G-E79H-K236M) created variant HNL3, which showed low esterase activity toward p-nitrophenyl acetate. Further structure comparison reveals additional differences surrounding the active site. HbHNL contains an improperly positioned oxyanion hole residue and differing solvation of the catalytic aspartate. We hypothesized that correcting these structural differences would impart good esterase activity on the corresponding HbHNL variant. To predict the amino acid substitutions needed to correct the structure, we calculated shortest path maps for both HbHNL and SABP2, which reveal correlated movements of amino acids in the two enzymes. Replacing four amino acid residues (C81L-N104T-V106F-G176S) whose movements are connected to the movements of the catalytic residues yielded variant HNL7TV (stabilizing substitution H103V was also added), which showed an esterase catalytic efficiency comparable to that of SABP2. The x-ray structure of an intermediate variant, HNL6V, showed an altered solvation of the catalytic aspartate and a partially corrected oxyanion hole. This dramatic increase in catalytic efficiency demonstrates the ability of shortest path maps to predict which residues outside the active site contribute to catalytic activity.

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.

Epigenetic CpG duplex marks probed by an evolved DNA reader via a well-tempered conformational plasticity

5-methylcytosine (mC) and its TET-oxidized derivatives exist in CpG dyads of mammalian DNA and regulate cell fate, but how their individual combinations in the two strands of a CpG act as distinct regulatory signals is poorly understood. Readers that selectively recognize such novel 'CpG duplex marks' could be versatile tools for studying their biological functions, but their design represents an unprecedented selectivity challenge. By mutational studies, NMR relaxation, and MD simulations, we here show that the selectivity of the first designer reader for an oxidized CpG duplex mark hinges on precisely tempered conformational plasticity of the scaffold adopted during directed evolution. Our observations reveal the critical aspect of defined motional features in this novel reader for affinity and specificity in the DNA/protein interaction, providing unexpected prospects for further design progress in this novel area of DNA recognition.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

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.

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.

Big data mining, rational modification, and ancestral sequence reconstruction inferred multiple xylose isomerases for biorefinery

The isomerization of xylose to xylulose is considered the most promising approach to initiate xylose bioconversion. Here, phylogeny-guided big data mining, rational modification, and ancestral sequence reconstruction strategies were implemented to explore new active xylose isomerases (XIs) for Saccharomyces cerevisiae. Significantly, 13 new active XIs for S. cerevisiae were mined or artificially created. Moreover, the importance of the amino-terminal fragment for maintaining basic XI activity was demonstrated. With the mined XIs, four efficient xylose-utilizing S. cerevisiae were constructed and evolved, among which the strain S. cerevisiae CRD5HS contributed to ethanol titers as high as 85.95 and 94.76 g/liter from pretreated corn stover and corn cob, respectively, without detoxifying or washing pretreated biomass. Potential genetic targets obtained from adaptive laboratory evolution were further analyzed by sequencing the high-performance strains. The combined XI mining methods described here provide practical references for mining other scarce and valuable enzymes.

Tuning Selectivity in CalA Lipase: Beyond Tunnel Engineering

Engineering studies of Candida (Pseudozyma) antarctica lipase A (CalA) have demonstrated the potential of this enzyme in the selective hydrolysis of fatty acid esters of different chain lengths. CalA has been shown to bind substrates preferentially through an acyl-chain binding tunnel accessed via the hydrolytic active site; it has also been shown that selectivity for substrates of longer or shorter chain length can be tuned, for instance by modulating steric hindrance within the tunnel. Here we demonstrate that, whereas the tunnel region is certainly of paramount importance for substrate recognition, residues in distal regions of the enzyme can also modulate substrate selectivity. To this end, we investigate variants that carry one or more substitutions within the substrate tunnel as well as in distal regions. Combining experimental determination of the substrate selectivity using natural and synthetic substrates with computational characterization of protein dynamics and of tunnels, we deconvolute the effect of key substitutions and demonstrate that epistatic interactions contribute to procuring selectivity toward either long-chain or short/medium-chain fatty acid esters. We demonstrate that various mechanisms contribute to the diverse selectivity profiles, ranging from reshaping tunnel morphology and tunnel stabilization to obstructing the main substrate-binding tunnel, highlighting the dynamic nature of the substrate-binding region. This work provides important insights into the versatility of this robust lipase toward diverse applications.

Insertions and Deletions (Indels): A Missing Piece of the Protein Engineering Jigsaw

Over the years, protein engineers have studied nature and borrowed its tricks to accelerate protein evolution in the test tube. While there have been considerable advances, our ability to generate new proteins in the laboratory is seemingly limited. One explanation for these shortcomings may be that insertions and deletions (indels), which frequently arise in nature, are largely overlooked during protein engineering campaigns. The profound effect of indels on protein structures, by way of drastic backbone alterations, could be perceived as "saltation" events that bring about significant phenotypic changes in a single mutational step. Should we leverage these effects to accelerate protein engineering and gain access to unexplored regions of adaptive landscapes? In this Perspective, we describe the role played by indels in the functional diversification of proteins in nature and discuss their untapped potential for protein engineering, despite their often-destabilizing nature. We hope to spark a renewed interest in indels, emphasizing that their wider study and use may prove insightful and shape the future of protein engineering by unlocking unique functional changes that substitutions alone could never achieve.

Study of Protein Conformational Dynamics Using Hydrogen/Deuterium Exchange Mass Spectrometry

Intrinsic protein dynamics contribute to their biological functions. Rational engineering of protein dynamics is extremely challenging with only a handful of successful examples. Hydrogen/deuterium exchange coupled to mass spectrometry (HDX-MS) represents a powerful technique for quantitative analysis of protein dynamics. Here we provide a detailed description of the preparation of protein samples, collection of high-quality data, and their in-depth analysis using various computational tools. We illustrate the application of HDX-MS for the study of protein dynamics in the rational engineering of flexible loops in the reconstructed ancestor of haloalkane dehalogenase and Renilla luciferase. These experiments provided unique and valuable data rigorously describing the modification of protein dynamics upon grafting of the loop-helix element. Tips and tricks are provided to stimulate the wider use of HDX-MS to study and engineer protein dynamics.

Enzymatic Conjugation of Modified RNA Fragments by Ancestral RNA Ligase AncT4_2

Oligonucleotide therapeutics have great potential as a next-generation approach to treating intractable diseases. Large quantities of modified DNA/RNA containing xenobiotic nucleic acids (XNAs) must be synthesized before clinical application. In this study, the ancestral RNA ligase AncT4_2 was designed by ancestral sequence reconstruction (ASR) to perform the conjugation reaction of modified RNA fragments. AncT4_2 had superior properties to native RNA ligase 2 from T4 phage (T4Rnl2), including high productivity, a >2.5-fold-higher turnover number, and >10°C higher thermostability. One remarkable point is the broad substrate selectivity of AncT4_2; the activity of AncT4_2 toward 17 of the modified RNA fragments was higher than that of T4Rnl2. The activity was estimated by measuring the conjugation reaction of two RNA strands, 3'-OH (12 bp) and 5'-PO4 (12 bp), in which the terminal and penultimate positions of the 3'-OH fragment and the first and second positions of the 5'-PO4 fragment were substituted by 2'-fluoro, 2'-O-methyl, 2'-O-methoxyethyl, and 2'-H, respectively. The enzymatic properties of AncT4_2 allowed the enzyme to conjugate large quantities of double-stranded RNA coding for patisiran (>400 μM level), which was formed by four RNA fragments containing 2'-OMe-substituted nucleic acids. Structural analysis of modeled AncT4_2 suggested that protein dynamics were changed by mutation to Gly or indel during ASR and that this may positively impact the conjugation of modified RNA fragments with the enzyme. AncT4_2 is expected to be a key biocatalyst in synthesizing RNA therapeutics by an enzymatic reaction. IMPORTANCE RNA therapeutics is one of the next-generation medicines for treating various diseases. Our designed ancestral RNA ligase AncT4_2 exhibited excellent enzymatic properties, such as high thermal stability, productivity, specific activity, and broad substrate selectivity compared to native enzymes. These advantages create the potential for AncT4_2 to be applied in conjugating the modified RNA fragments containing various xenobiotic nucleic acids. In addition, patisiran, a known polyneuropathy therapeutic, could be synthesized from four fragmented oligonucleotides at a preparative scale. Taken together, these findings indicate AncT4_2 could open the door to synthesizing RNA therapeutics by enzymatic reaction at large-scale production.

Insights into the importance of WPD-loop sequence for activity and structure in protein tyrosine phosphatases

Protein tyrosine phosphatases (PTPs) possess a conserved mobile catalytic loop, the WPD-loop, which brings an aspartic acid into the active site where it acts as an acid/base catalyst. Prior experimental and computational studies, focused on the human enzyme PTP1B and the PTP from Yersinia pestis, YopH, suggested that loop conformational dynamics are important in regulating both catalysis and evolvability. We have generated a chimeric protein in which the WPD-loop of YopH is transposed into PTP1B, and eight chimeras that systematically restored the loop sequence back to native PTP1B. Of these, four chimeras were soluble and were subjected to detailed biochemical and structural characterization, and a computational analysis of their WPD-loop dynamics. The chimeras maintain backbone structural integrity, with somewhat slower rates than either wild-type parent, and show differences in the pH dependency of catalysis, and changes in the effect of Mg2+. The chimeric proteins' WPD-loops differ significantly in their relative stability and rigidity. The time required for interconversion, coupled with electrostatic effects revealed by simulations, likely accounts for the activity differences between chimeras, and relative to the native enzymes. Our results further the understanding of connections between enzyme activity and the dynamics of catalytically important groups, particularly the effects of non-catalytic residues on key conformational equilibria.

Elucidating the Structural Impacts of Protein InDels

The effects of amino acid insertions and deletions (InDels) remain a rather under-explored area of structural biology. These variations oftentimes are the cause of numerous disease phenotypes. In spite of this, research to study InDels and their structural significance remains limited, primarily due to a lack of experimental information and computational methods. In this work, we fill this gap by modeling InDels computationally; we investigate the rigidity differences between the wildtype and a mutant variant with one or more InDels. Further, we compare how structural effects due to InDels differ from the effects of amino acid substitutions, which are another type of amino acid mutation. We finish by performing a correlation analysis between our rigidity-based metrics and wet lab data for their ability to infer the effects of InDels on protein fitness.