Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants

Thermal Sensitivity of the Enzymatic Activity of β-Glucosidase: Simulations Lend Mechanistic Insights into Experimental Observations

A crucial prerequisite for industrial applications of enzymes is the maintenance of specific activity across wide thermal ranges. β-Glucosidase (EC 3.2.1.21) is an essential enzyme for converting cellulose in biomass to glucose. While the reaction mechanisms of β-glucosidases from various thermal ranges (hyperthermophilic, thermophilic, and mesophilic) are similar, the factors underlying their thermal sensitivity remain obscure. The work presented here aims to unravel the molecular mechanisms underlying the thermal sensitivity of the enzymatic activity of the β-glucosidase BglB from the bacterium Paenibacillus polymyxa. Experiments reveal a maximum enzymatic activity at 315 K, with a marked decrease in the activity below and above this temperature. Employing in silico simulations, we identified the crucial role of the active site tunnel residues in the thermal sensitivity. Specific tunnel residues were identified via energetic decomposition and protein-substrate hydrogen bond analyses. The experimentally observed trends in specific activity with temperature coincide with variations in overall binding free energy changes, showcasing a predominantly electrostatic effect that is consistent with enhanced catalytic pocket-substrate hydrogen bonding (HB) at Topt. The entropic advantage owing to the HB substate reorganization was found to facilitate better substrate binding at 315 K. This study elicits molecular-level insights into the associative mechanisms between thermally enabled fluctuations and enzymatic activity. Crucial differences emerge between molecular mechanisms involving the actual substrate (cellobiose) and a commonly employed chemical analogue. We posit that leveraging the role of fluctuations may reveal unexpected insights into enzyme behavior and offer novel paradigms for enzyme engineering.

Design to Data for mutants of B-glucosidase B from Paenibacillus polymyxa : V311D, F248N, Y166H, Y166K, M221K

Engaging computational tools for protein design is gaining traction in the enzyme engineering community. However, current design and modeling algorithms have limited functionality predictive capacities for enzymes due to limitations of the dataset in terms of size and data quality. This study aims to expand training datasets for improved algorithm development with the addition of five rationally designed single-point enzyme variants. β-glucosidase B variants were modeled in Foldit Standalone and then produced and assayed for thermal stability and kinetic parameters. Functional parameters: thermal stability (T _M ) and Michaelis-Menten constants ( k _cat , K _M , and k _cat /K _M ) of five variants, V311D, Y166H, M221K, F248N, and Y166K, were added into the Design2Data database. As a case study, evaluation of this small mutant set finds mutational effect trends that both corroborate and contradict findings from larger studies examining the entire dataset.

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.

Data-driven enzyme engineering to identify function-enhancing enzymes

Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on 18 Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field (EF) on the breaking C-H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the EF was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for 18 Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSD_{active_site} and SASA_ratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EF_C-H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

Cloning, Expression, and Characterization of a GHF 11 Xylanase from Alteromonas macleodii HY35 in Escherichia coli

A xylanase gene xynZT-1 from Alteromonas macleodii HY35 was cloned and expressed in Escherichia coli (E. coli). The sequencing results showed that the ORF of xynZT-1 was 831 bp. The xylanase DNA sequence encoded a 29 amino acids (aa) signal peptide and a 247-aa mature peptide. The XynZT-1 has been a calculated molecular weight (MW) of 27.93 kDa, isoelectric point (pI) of 5.11 and the formula C₁₂₆₆H₁₈₂₉N₃₂₇O₃₈₄S₅. The amino acid sequence of the xynZT-1 had a high similarity with that of glycosyl hydrolase family 11 (GHF11) reported from other microorganisms. The DNA sequence encoding mature peptide was subcloned into pET-28a(+) expression vector. The resulted plasmid pET-28a-xynZT-1 was transformed into E. coli BL21(DE3), and the recombinant strain BL21(DE3)/xynZT-1 was obtained. The optimum temperature and pH of the recombinant XynZT-1 were 45 ℃ and 5.0, respectively.

Novel thermo-alkali-stable cellulase-producing Serratia sp. AXJ-M cooperates with Arthrobacter sp. AXJ-M1 to improve degradation of cellulose in papermaking black liquor

There is an urgent requirement to treat cellulose present in papermaking black liquor since it induces severe economic wastes and causes environmental pollution. We characterized cellulase activity at different temperatures and pH to seek thermo-alkali-stable cellulase-producing bacteria, a natural consortium of Serratia sp. AXJ-M and Arthrobacter sp. AXJ-M1 was used to improve the degradation of cellulose. Notably, the enzyme activities and the degradation rate of cellulose were increased by 30%-70% and 30% after co-culture, respectively. In addition, the addition of cosubstrates increased the degradation rate of cellulose beyond 30%. The thermo-alkali-stable endoglucanase (bcsZ) gene was derived from the strain AXJ-M and was cloned and expressed. The purified bcsZ displayed the maximum activity at 70 °C and pH 9. Mn²⁺, Ca²⁺, Mg²⁺ and Tween-20 had beneficial effects on the enzyme activity. Structurally, bcsZ potentially catalyzed the degradation of cellulose. The co-culture with ligninolytic activities significantly decreased target the parameters (cellulose 45% and COD 95%) while using the immobilized fluidized bed reactors (FBRs). Finally, toxicological tests and antioxidant enzyme activities indicated that the co-culture had a detoxifying effect on black liquor. Our study showed that Serratia sp. AXJ-M acts synergistically with Arthrobacter sp. AXJ-M1 may be potentially useful for bioremediation for black liquor.

Computational Enzyme Design at Zymvol

Directed evolution is the most recognized methodology for enzyme engineering. The main drawback resides in its random nature and in the limited sequence exploration; both require screening of thousands (if not millions) of variants to achieve a target function. Computer-driven approaches can limit laboratorial screening to a few hundred candidates, enabling and accelerating the development of industrial enzymes. In this book chapter, the technology adopted at Zymvol is described. An overview of the current development and future directions in the company is also provided.

Improved methanol tolerance of Rhizomucor miehei lipase based on N‑glycosylation within the α-helix region and its application in biodiesel production

BACKGROUND

Liquid lipases are widely used to convert oil into biodiesel. Methanol-resistant lipases with high catalytic activity are the first choice for practical production. Rhizomucor miehei lipase (RML) is a single-chain α/β-type protein that is widely used in biodiesel preparation. Improving the catalytic activity and methanol tolerance of RML is necessary to realise the industrial production of biodiesel.

RESULTS

In this study, a semi-rational design method was used to optimise the catalytic activity and methanol tolerance of ProRML. After N-glycosylation modification of the α-helix of the mature peptide in ProRML, the resulting mutants N218, N93, N115, N260, and N183 increased enzyme activity by 66.81, 13.54, 10.33, 3.69, and 2.39 times than that of WT, respectively. The residual activities of N218 and N260 were 88.78% and 86.08% after incubation in 50% methanol for 2.5 h, respectively. In addition, the biodiesel yield of all mutants was improved when methanol was added once and reacted for 24 h with colza oil as the raw material. N260 and N218 increased the biodiesel yield from 9.49% to 88.75% and 90.46%, respectively.

CONCLUSIONS

These results indicate that optimising N-glycosylation modification in the α-helix structure is an effective strategy for improving the performance of ProRML. This study provides an effective approach to improve the design of the enzyme and the properties of lipase mutants, thereby rendering them suitable for industrial biomass conversion.

High throughput and quantitative enzymology in the genomic era

Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.

Rubisco Adaptation Is More Limited by Phylogenetic Constraint Than by Catalytic Trade-off

Rubisco assimilates CO2 to form the sugars that fuel life on earth. Correlations between rubisco kinetic traits across species have led to the proposition that rubisco adaptation is highly constrained by catalytic trade-offs. However, these analyses did not consider the phylogenetic context of the enzymes that were analyzed. Thus, it is possible that the correlations observed were an artefact of the presence of phylogenetic signal in rubisco kinetics and the phylogenetic relationship between the species that were sampled. Here, we conducted a phylogenetically resolved analysis of rubisco kinetics and show that there is a significant phylogenetic signal in rubisco kinetic traits. We re-evaluated the extent of catalytic trade-offs accounting for this phylogenetic signal and found that all were attenuated. Following phylogenetic correction, the largest catalytic trade-offs were observed between the Michaelis constant for CO2 and carboxylase turnover (∼21-37%), and between the Michaelis constants for CO2 and O2 (∼9-19%), respectively. All other catalytic trade-offs were substantially attenuated such that they were marginal (<9%) or non-significant. This phylogenetically resolved analysis of rubisco kinetic evolution also identified kinetic changes that occur concomitant with the evolution of C4 photosynthesis. Finally, we show that phylogenetic constraints have played a larger role than catalytic trade-offs in limiting the evolution of rubisco kinetics. Thus, although there is strong evidence for some catalytic trade-offs, rubisco adaptation has been more limited by phylogenetic constraint than by the combined action of all catalytic trade-offs.

Amino acid interactions that facilitate enzyme catalysis

Interactions in enzymes between catalytic and neighboring amino acids and how these interactions facilitate catalysis are examined. In examples from both natural and designed enzymes, it is shown that increases in catalytic rates may be achieved through elongation of the buffer range of the catalytic residues; such perturbations in the protonation equilibria are, in turn, achieved through enhanced coupling of the protonation equilibria of the active ionizable residues with those of other ionizable residues. The strongest coupling between protonation states for a pair of residues that deprotonate to form an anion (or a pair that accept a proton to form a cation) is achieved when the difference in the intrinsic pK_as of the two residues is approximately within 1 pH unit. Thus, catalytic aspartates and glutamates are often coupled to nearby acidic residues. For an anion-forming residue coupled to a cation-forming residue, the elongated buffer range is achieved when the intrinsic pK_a of the anion-forming residue is higher than the intrinsic pK_a of the (conjugate acid of the) cation-forming residue. Therefore, the high pK_a, anion-forming residues tyrosine and cysteine make good coupling partners for catalytic lysine residues. For the anion-cation pairs, the optimum difference in intrinsic pK_as is a function of the energy of interaction between the residues. For the energy of interaction ε expressed in units of (ln 10)RT, the optimum difference in intrinsic pK_as is within ∼1 pH unit of ε.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Resource uptake and the evolution of moderately efficient enzymes

Enzymes speed up reactions that would otherwise be too slow to sustain the metabolism of selfreplicators. Yet, most enzymes seem only moderately efficient, exhibiting kinetic parameters orders of magnitude lower than their expected physically achievable maxima and spanning over surprisingly large ranges of values. Here, we question how these parameters evolve using a mechanistic model where enzyme efficiency is a key component of individual competition for resources. We show that kinetic parameters are under strong directional selection only up to a point, above which enzymes appear to evolve under near-neutrality, thereby confirming the qualitative observation of other modeling approaches. While the existence of a large fitness plateau could potentially explain the extensive variation in enzyme features reported, we show using a population genetics model that such a widespread distribution is an unlikely outcome of evolution on a common landscape, as mutation–selection–drift balance occupy a narrow area even when very moderate biases towards lower efficiency are considered. Instead, differences in the evolutionary context encountered by each enzyme should be involved, such that each evolves on an individual, unique landscape. Our results point to drift and effective population size playing an important role, along with the kinetics of nutrient transporters, the tolerance to high concentrations of intermediate metabolites, and the reversibility of reactions. Enzyme concentration also shapes selection on kinetic parameters, but we show that the joint evolution of concentration and efficiency does not yield extensive variance in evolutionary outcomes when documented costs to protein expression are applied.

Accelerated knowledge discovery from omics data by optimal experimental design

How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli’s populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.

How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. Here, the authors present OPEX, an optimal experimental design method to identify informative omics experiments for both experimental space exploration and model training.

Improving the thermostability of a thermostable endoglucanase from Chaetomium thermophilum by engineering the conserved noncatalytic residue and N-glycosylation site

Endoglucanases provide an attractive avenue for the bioconversion of lignocellulosic materials into fermentable sugars to supply cellulosic feedstock for biofuels and other value-added chemicals. Thermostable endoglucanases with high catalytic activity are preferred in practical processes. To improve the thermostability and activity of the thermostable β-1,4-endoglucanase CTendo45 isolated from the thermophilic fungus Chaetomium thermophilum, structure-based rational design was performed by using site-directed mutagenesis. When inactivated mutation of the unique N-glycosylation sequon (N88-E89-T90) was implemented and the conserved Y173 residue was substituted with phenylalanine, a double mutant T90A/Y173F demonstrated enzymatic activity that dramatically increased 2.12- and 1.82-fold towards CMC-Na and β-D-glucan, respectively. Additionally, T90A/Y173F exhibited extraordinary heat endurance after 300 min of incubation at elevated temperatures. This study provides a valid approach to the improvement of enzyme redesign protocols and the properties of this endoglucanase mutant distinguish it as an excellent candidate enzyme for industrial biomass conversion.

Identification and Characterization of a Novel Hyperthermostable Bifunctional Cellobiohydrolase- Xylanase Enzyme for Synergistic Effect With Commercial Cellulase on Pretreated Wheat Straw Degradation

The novel cellobiohydrolase gene ctcel7 was identified from Chaetomium thermophilum, and its recombinant protein CtCel7, a member of glycoside hydrolase family 7, was heterologously expressed in Pichia pastoris and biochemically characterized. Compared with commercial hydrolases, purified CtCel7 exhibited superior bifunctional cellobiohydrolase and xylanase activities against microcrystalline cellulose and xylan, respectively, under optimal conditions of 60°C and pH 4.0. Moreover, CtCel7 displayed remarkable thermostability with over 90% residual activity after heat (60°C) treatment for 180 min. CtCel7 was insensitive to most detected cations and reagents and preferentially cleaved the β-1,4-glycosidic bond to generate oligosaccharides through the continuous saccharification of lignocellulosic substrates, which are crucial for various practical applications. Notably, the hydrolysis effect of a commercial cellulase cocktail on pretreated wheat straw was substantively improved by its combination with CtCel7. Taken together, these excellent properties distinguish CtCel7 as a robust candidate for the biotechnological production of biofuels and biobased chemicals.

Improvement of the catalytic activity and thermostability of a hyperthermostable endoglucanase by optimizing N-glycosylation sites

BACKGROUND

Endoglucanase has been extensively employed in industrial processes as a key biocatalyst for lignocellulosic biomass degradation. Thermostable endoglucanases with high catalytic activity at elevated temperatures are preferred in industrial use. To improve the activity and thermostability, site-directed mutagenesis was conducted to modify the N-glycosylation sites of the thermostable β-1,4-endoglucanase CTendo45 from Chaetomium thermophilum.

RESULTS

In this study, structure-based rational design was performed based on the modification of N-glycosylation sites in CTendo45. Eight single mutants and one double mutant were constructed and successfully expressed in Pichia pastoris. When the unique N-glycosylation site of N88 was eliminated, a T90A variant was active, and its specific activity towards CMC-Na and β-d-glucan was increased 1.85- and 1.64-fold, respectively. The mutant R67S with an additional N-glycosylation site of N65 showed a distinct enhancement in catalytic efficiency. Moreover, T90A and R67S were endowed with extraordinary heat endurance after 200 min of incubation at different temperatures ranging from 30 to 90 °C. Likewise, the half-lives (t _1/2) indicated that T90A and R67S exhibited improved enzyme thermostability at 80 °C and 90 °C. Notably, the double-mutant T90A/R67S possessed better hydrolysis activity and thermal stability than its single-mutant counterparts and the wild type.

CONCLUSIONS

This study provides initial insight into the biochemical function of N-glycosylation in thermostable endoglucanases. Moreover, the design approach to the optimization of N-glycosylation sites presents an effective and feasible strategy to improve enzymatic activity and thermostability.

Machine learning-assisted directed protein evolution with combinatorial libraries

To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si-H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee (enantiomeric excess). By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.

Data-driven engineering of protein therapeutics

Protein therapeutics requires a series of properties beyond biochemical activity, including serum stability, low immunogenicity, and manufacturability. Mutations that improve one property often decrease one or more of the other essential requirements for therapeutic efficacy, making the protein engineering challenge difficult. The past decade has seen an explosion of new techniques centered around cheaply reading and writing DNA. This review highlights the recent use of such high throughput technologies for engineering protein therapeutics. Examples include the use of human antibody repertoire sequence data to pair antibody heavy and light chains, comprehensive mutational analysis for engineering antibody specificity, and the use of ancestral and inter-species sequence data to engineer simultaneous improvements in enzyme catalytic efficiency and stability. We conclude with a perspective on further ways to integrate mature protein engineering pipelines with the exponential increases in the volume of sequencing data expected in the forthcoming decade.

β-Glucosidase Discovery and Design for the Degradation of Oleuropein

Current lye processing for debittering California black table olives produces large amounts of caustic wastewater and destroys many of the beneficial phenolic compounds in the fruit. Herein, we propose using enzyme treatment in place of lye, potentially reducing the amount and causticity of wastewater produced. By specifically targeting the bitterness-causing compound, oleuropein, retention of other beneficial phenolics may be possible. A β-glucosidase from Streptomyces sp. was identified from a screen of 22 glycosyl hydrolases to completely degrade oleuropein in 24 h. Computational modeling was performed on this enzyme, and mutation C181A was found to improve the rate of catalysis by 3.2-fold. This mutant was tested in the context of the olive fruit and leaf extract. Degradation was observed in the olive leaf extract but not in the fruit matrix, suggesting that enzyme fruit penetration is a limiting factor. This work discovers and begins the refinement process for an enzyme that has the catalytic properties for debittering olives and provides direction for future engineering efforts required to make a product with commercial value.

Predicting the evolution of Escherichia coli by a data-driven approach

A tantalizing question in evolutionary biology is whether evolution can be predicted from past experiences. To address this question, we created a coherent compendium of more than 15,000 mutation events for the bacterium Escherichia coli under 178 distinct environmental settings. Compendium analysis provides a comprehensive view of the explored environments, mutation hotspots and mutation co-occurrence. While the mutations shared across all replicates decrease with the number of replicates, our results argue that the pairwise overlapping ratio remains the same, regardless of the number of replicates. An ensemble of predictors trained on the mutation compendium and tested in forward validation over 35 evolution replicates achieves a 49.2 ± 5.8% (mean ± std) precision and 34.5 ± 5.7% recall in predicting mutation targets. This work demonstrates how integrated datasets can be harnessed to create predictive models of evolution at a gene level and elucidate the effect of evolutionary processes in well-defined environments.

How reproducible evolutionary processes are remains an important question in evolutionary biology. Here, the authors compile a compendium of more than 15,000 mutation events for Escherichia coli under 178 distinct environmental settings, and develop an ensemble of predictors to predict evolution at a gene level.

Repurposing Citizen Science Games as Software Tools for Professional Scientists

Scientific software is often developed with professional scientists in mind, resulting in complex tools with a steep learning curve. Citizen science games, however, are designed for citizen scientists- members of the general public. These games maintain scientific accuracy while placing design goals such as usability and enjoyment at the forefront. In this paper, we identify an emerging use of game-based technology, in the repurposing of citizen science games to be software tools for professional scientists in their work. We discuss our experience in two such repurposings: Foldit, a protein folding and design game, and Eyewire, a web-based 3D neuron reconstruction game. Based on this experience, we provide evidence that the software artifacts produced for citizen science can be useful for professional scientists, and provide an overview of key design principles we found to be useful in the process of repurposing.

Automated Planning Enables Complex Protocols on Liquid-Handling Robots

Robotic automation in synthetic biology is especially relevant for liquid handling to facilitate complex experiments. However, research tasks that are not highly standardized are still rarely automated in practice. Two main reasons for this are the substantial investments required to translate molecular biological protocols into robot programs, and the fact that the resulting programs are often too specific to be easily reused and shared. Recent developments of standardized protocols and dedicated programming languages for liquid-handling operations addressed some aspects of ease-of-use and portability of protocols. However, either they focus on simplicity, at the expense of enabling complex protocols, or they entail detailed programming, with corresponding skills and efforts required from the users. To reconcile these trade-offs, we developed Roboliq, a software system that uses artificial intelligence (AI) methods to integrate (i) generic formal, yet intuitive, protocol descriptions, (ii) complete, but usually hidden, programming capabilities, and (iii) user-system interactions to automatically generate executable, optimized robot programs. Roboliq also enables high-level specifications of complex tasks with conditional execution. To demonstrate the system's benefits for experiments that are difficult to perform manually because of their complexity, duration, or time-critical nature, we present three proof-of-principle applications for the reproducible, quantitative characterization of GFP variants.

Engineering the conserved and noncatalytic residues of a thermostable β-1,4-endoglucanase to improve specific activity and thermostability

Endoglucanases are increasingly applied in agricultural and industrial applications as a key biocatalyst for cellulose biodegradation. However, the low performance in extreme conditions seriously challenges the enzyme’s commercial utilization. To obtain endoglucanases with substantially improved activity and thermostability, structure-based rational design was carried out based on the Chaetomium thermophilum β-1,4-endoglucanase CTendo45. In this study, five mutant enzymes were constructed by substitution of conserved and noncatalytic residues using site-directed mutagenesis. Mutants were constitutively expressed in Pichia pastoris, purified, and ultimately tested for enzymatic characteristics. Two single mutants, Y30F and Y173F, increased the enzyme’s specific activity 1.35- and 1.87-fold using carboxymethylcellulose sodium (CMC-Na) as a substrate, respectively. Furthermore, CTendo45 and mutants exhibited higher activity towards β-D-glucan than that of CMC-Na, and activities of Y173F and Y30F were also increased obviously against β-D-glucan. In addition, Y173F significantly improved the enzyme’s heat resistance at 80 °C and 90 °C. More interestingly, the double mutant Y30F/Y173F obtained considerably higher stability at elevated temperatures but failed to inherit the increased catalytic efficiency of its single mutant counterparts. This work gives an initial insight into the biological function of conserved and noncatalytic residues of thermostable endoglucanases and proposes a feasible path for the improvement of enzyme redesign proposals.

Systematic Functional Analysis of Active-Site Residues in l-Threonine Dehydrogenase from Thermoplasma volcanium

Enzymes have been through millions of years of evolution during which their active-site microenvironments are fine-tuned. Active-site residues are commonly conserved within protein families, indicating their importance for substrate recognition and catalysis. In this work, we systematically mutated active-site residues of l-threonine dehydrogenase from Thermoplasma volcanium and characterized the mutants against a panel of substrate analogs. Our results demonstrate that only a subset of these residues plays an essential role in substrate recognition and catalysis and that the native enzyme activity can be further enhanced roughly 4.6-fold by a single point mutation. Kinetic characterization of mutants on substrate analogs shows that l-threonine dehydrogenase possesses promiscuous activities toward other chemically similar compounds not previously observed. Quantum chemical calculations on the hydride-donating ability of these substrates also reveal that this enzyme did not evolve to harness the intrinsic substrate reactivity for enzyme catalysis. Our analysis provides insights into connections between the details of enzyme active-site structure and specific function. These results are directly applicable to rational enzyme design and engineering.

Bacterial cell-free expression technology to in vitro systems engineering and optimization

Cell-free expression system is a technology for the synthesis of proteins in vitro. The system is a platform for several bioengineering projects, e.g. cell-free metabolic engineering, evolutionary design of experiments, and synthetic minimal cell construction. Bacterial cell-free protein synthesis system (CFPS) is a robust tool for synthetic biology. The bacteria lysate, the DNA, and the energy module, which are the three optimized sub-systems for in vitro protein synthesis, compose the integrated system. Currently, an optimized E. coli cell-free expression system can produce up to ∼2.3 mg/mL of a fluorescent reporter protein. Herein, I will describe the features of ATP-regeneration systems for in vitro protein synthesis, and I will present a machine-learning experiment for optimizing the protein yield of E. coli cell-free protein synthesis systems. Moreover, I will introduce experiments on the synthesis of a minimal cell using liposomes as dynamic containers, and E. coli cell-free expression system as biochemical platform for metabolism and gene expression. CFPS can be further integrated with other technologies for novel applications in environmental, medical and material science.

Thermal stability and kinetic constants for 129 variants of a family 1 glycoside hydrolase reveal that enzyme activity and stability can be separately designed

Accurate modeling of enzyme activity and stability is an important goal of the protein engineering community. However, studies seeking to evaluate current progress are limited by small data sets of quantitative kinetic constants and thermal stability measurements. Here, we report quantitative measurements of soluble protein expression in E. coli, thermal stability, and Michaelis-Menten constants (k_cat, K_M, and k_cat/K_M) for 129 designed mutants of a glycoside hydrolase. Statistical analyses reveal that functional T_m is independent of k_cat, K_M, and k_cat/K_M in this system, illustrating that an individual mutation can modulate these functional parameters independently. In addition, this data set is used to evaluate computational predictions of protein stability using the established Rosetta and FoldX algorithms. Predictions for both are found to correlate only weakly with experimental measurements, suggesting improvements are needed in the underlying algorithms.