Computational tools for designing and engineering biocatalysts

Tunnel engineering of gas-converting enzymes for inhibitor retardation and substrate acceleration

Carbon monoxide dehydrogenase (CODH), formate dehydrogenase (FDH), hydrogenase (H2ase), and nitrogenase (N2ase) are crucial enzymatic catalysts that facilitate the conversion of industrially significant gases such as CO, CO2, H2, and N2. The tunnels in the gas-converting enzymes serve as conduits for these low molecular weight gases to access deeply buried catalytic sites. The identification of the substrate tunnels is imperative for comprehending the substrate selectivity mechanism underlying these gas-converting enzymes. This knowledge also holds substantial value for industrial applications, particularly in addressing the challenges associated with separation and utilization of byproduct gases. In this comprehensive review, we delve into the emerging field of tunnel engineering, presenting a range of approaches and analyses. Additionally, we propose methodologies for the systematic design of enzymes, with the ultimate goal of advancing protein engineering strategies.

Chemical modification of enzymes to improve biocatalytic performance

Improvement in intrinsic enzymatic features is in many instances a prerequisite for the scalable applicability of many industrially important biocatalysts. To this end, various strategies of chemical modification of enzymes are maturing and now considered as a distinct way to improve biocatalytic properties. Traditional chemical modification methods utilize reactivities of amine, carboxylic, thiol and other side chains originating from canonical amino acids. On the other hand, noncanonical amino acid- mediated 'click' (bioorthogoal) chemistry and dehydroalanine (Dha)-mediated modifications have emerged as an alternate and promising ways to modify enzymes for functional enhancement. This review discusses the applications of various chemical modification tools that have been directed towards the improvement of functional properties and/or stability of diverse array of biocatalysts.

A Machine Learning Study on the Thermostability Prediction of (R)-ω-Selective Amine Transaminase from Aspergillus terreus

Artificial intelligence technologies such as machine learning have been applied to protein engineering, with unique advantages in protein structure, function prediction, catalytic activity, and other issues in recent years. Screening better mutants is still a bottleneck in protein engineering. In this paper, a new sequence-activity relationship method was analyzed for its application in improving the thermal stability of Aspergillus terreus (R)-ω-selective amine transaminase. The experimental data from 6 single-point mutated enzymes were used as a learning dataset to build models and predict the thermostability of 2⁶ mutants. Based on digital signal processing (DSP), this method digitized the amino acid sequence of proteins by fast Fourier transform (FFT) and then established the best model applying partial least squares regression (PLSR) to screen out all possible mutants, especially those with high performance. In protein engineering, the innovative sequence activity relationship (ISAR) method can make a reasonable prediction using limited experimental data and significantly reduce the experimental cost. The half-life (T_1/2) of (R)-ω-transaminase was fitted with the amino acid sequence by the ISAR algorithm, resulting in an R² of 0.8929 and a cvRMSE of 4.89. At the same time, the mutants with higher T_1/2 than the existing ones were predicted, laying the groundwork for better (R)-ω-transaminase in the later stage. The ISAR algorithm is expected to provide a new technique for protein evolution and screening.

Design of novel enzyme biocatalysts for industrial bioprocess: Harnessing the power of protein engineering, high throughput screening and synthetic biology

Biocatalysts have wider applications in various industries. Biocatalysts are generating bigger attention among researchers due to their unique catalytic properties like activity, specificity and stability. However the industrial use of many enzymes is hindered by low catalytic efficiency and stability during industrial processes. Properties of enzymes can be altered by protein engineering. Protein engineers are increasingly study the structure-function characteristics, engineering attributes, design of computational tools for enzyme engineering, and functional screening processes to improve the design and applications of enzymes. The potent and innovative techniques of enzyme engineering deliver outstanding opportunities for tailoring industrially important enzymes for the versatile production of biochemicals. An overview of the current trends in enzyme engineering is explored with important representative examples.

Structural insights into xylanase mutant 254RL1 for improved activity and lower pH optimum

Xylanases degrade xylan to valuable end products. In our previous study, the alkaline xylanase S7-xyl from Bacillus halodurans S7 was engineered by rational design and the best mutant xylanase 254RL1 exhibited 3.4-fold improvements in specific activity at pH 9.0. Further research found that the enzyme activity at pH 6.0 was almost 2-fold than that at pH 9.0. To elucidate the reason of enhanced performance of 254RL1 at decreased pH optimum, we determined the X-ray crystal structure of 254RL1 at 2.21 Å resolution. The structural analysis revealed that the mutations enlarged the opening of the access tunnel and shortened the tunnel. Moreover, the mutations changed the hydrogen bond network around the catalytic residue and decreased the pK_a value of acid-base catalyst E159 which reduced the pH optimum of the xylanase. The result provided the basis for the acid-alkaline engineering of the glycoside hydrolases.

CaverDock: a molecular docking-based tool to analyse ligand transport through protein tunnels and channels

MOTIVATION

Protein tunnels and channels are key transport pathways that allow ligands to pass between proteins' external and internal environments. These functionally important structural features warrant detailed attention. It is difficult to study the ligand binding and unbinding processes experimentally, while molecular dynamics simulations can be time-consuming and computationally demanding.

RESULTS

CaverDock is a new software tool for analysing the ligand passage through the biomolecules. The method uses the optimized docking algorithm of AutoDock Vina for ligand placement docking and implements a parallel heuristic algorithm to search the space of possible trajectories. The duration of the simulations takes from minutes to a few hours. Here we describe the implementation of the method and demonstrate CaverDock's usability by: (i) comparison of the results with other available tools, (ii) determination of the robustness with large ensembles of ligands and (iii) the analysis and comparison of the ligand trajectories in engineered tunnels. Thorough testing confirms that CaverDock is applicable for the fast analysis of ligand binding and unbinding in fundamental enzymology and protein engineering.

AVAILABILITY AND IMPLEMENTATION

User guide and binaries for Ubuntu are freely available for non-commercial use at https://loschmidt.chemi.muni.cz/caverdock/. The web implementation is available at https://loschmidt.chemi.muni.cz/caverweb/. The source code is available upon request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structure-function investigation of 3-methylaspartate ammonia lyase reveals substrate molecular determinants for the deamination reaction

The enzymatic reactions leading to the deamination of β-lysine, lysine, or 2-aminoadipic acid are of great interest for the metabolic conversion of lysine to adipic acid. Enzymes able to carry out these reactions are not known, however ammonia lyases (EC 4.3.1.-) perform deamination on a wide range of substrates. We have studied 3-methylaspartate ammonia lyase (MAL, EC 4.3.1.2) as a potential candidate for protein engineering to enable deamination towards β-lysine, that we have shown to be a competitive inhibitor of MAL. We have characterized MAL activity, binding and inhibition properties on six different compounds that would allow to define the molecular determinants necessary for MAL to deaminate our substrate of interest. Docking calculations showed that β-lysine as well as the other compounds investigated could fit spatially into MAL catalytic pocket, although they probably are weak or very transient binders and we identified molecular determinants involved in the binding of the substrate. The hydrophobic interactions formed by the methyl group of 3-methylaspartic acid, together with the presence of the amino group on carbon 2, play an essential role in the appropriate binding of the substrate. The results showed that β-lysine is able to fit and bind in MAL catalytic pocket and can be potentially converted from inhibitor to substrate of MAL upon enzyme engineering. The characterization of the binding and inhibition properties of the substrates tested here provide the foundation for future and more extensive studies on engineering MAL that could lead to a MAL variant able to catalyse this challenging deamination reaction.

Minimalist de novo Design of Protein Catalysts

The field of protein design has grown enormously in the past few decades. In this review we discuss the minimalist approach to design of artificial enzymes, in which protein sequences are created with the minimum number of elements for folding and function. This method relies on identifying starting points in catalytically inert scaffolds for active site installation. The progress of the field from the original helical assemblies of the 1980s to the more complex structures of the present day is discussed, highlighting the variety of catalytic reactions which have been achieved using these methods. We outline the strengths and weaknesses of the minimalist approaches, describe representative design cases and put it in the general context of the de novo design of proteins.

One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators

Enzymes are biological catalysts with many industrial applications, but natural enzymes are usually unsuitable for industrial processes because they are not optimized for the process conditions. The properties of enzymes can be improved by directed evolution, which involves multiple rounds of mutagenesis and screening. By using mathematical models to predict the structure-activity relationship of an enzyme, and by defining the optimal combination of mutations in silico, we can significantly reduce the number of bench experiments needed, and hence the time and investment required to develop an optimized product. Here, we applied our innovative sequence-activity relationship methodology (innov'SAR) to improve glucose oxidase activity in the presence of different mediators across a range of pH values. Using this machine learning approach, a predictive model was developed and the optimal combination of mutations was determined, leading to a glucose oxidase mutant (P1) with greater specificity for the mediators ferrocene-methanol (12-fold) and nitrosoaniline (8-fold), compared to the wild-type enzyme, and better performance in three pH-adjusted buffers. The k_cat /K_M ratio of P1 increased by up to 121 folds compared to the wild type enzyme at pH 5.5 in the presence of ferrocene methanol.

Enhancement of the thermostability of Aspergillus niger α-l-rhamnosidase based on PoPMuSiC algorithm

α-l-Rhamnosidase is a biotechnologically important enzyme in food industry and in the preparation of drugs and drug precursors. To expand the functionality of our previously cloned α-l-rhamnosidase from Aspergillus niger JMU-TS528, 14 mutants were constructed based on the changes of the folding free energy (ΔΔG), predicted by the PoPMuSiC algorithm. Among them, six single-site mutants displayed higher thermal stability than wild type (WT). The combinational mutant K573V-E631F displayed even higher thermostability than six single-site mutants. The spectra analyses displayed that the WT and K573V-E631F had almost similar secondary and tertiary structure profiles. The simulated protein structure-based interaction analysis and molecular dynamics calculation were further implemented to assess the conformational preferences of the K573V-E631F. The improved thermostability of mutant K573V-E631F may be attributed to the introduction of new cation-π and hydrophobic interactions, and the overall improvement of the enzyme conformation. PRACTICAL APPLICATIONS: The stability of enzymes, particularly with regards to thermal stability remains a critical issue in industrial biotechnology and industrial processing generally tends to higher ambient temperature to inhibit microbial growth. Most of the α-l-rhamnosidases are usually active at temperature from 30 to 60°C, which are apt to denature at temperatures over 60°C. To expand the functionality of our previously cloned α-l-rhamnosidase from Aspergillus niger JMU-TS528, we used protein engineering methods to increase the thermal stability of the α-l-rhamnosidase. Practically, conducting reactions at high temperatures enhances the solubility of substrates and products, increases the reaction rate thus reducing the reaction time, and inhibits the growth of contaminating microorganisms. Thus, the improvement on the thermostability of α-l-rhamnosidase on the one hand can increase enzyme efficacy; on the other hand, the high ambient temperature would enhance the solubility of natural substrates of α-l-rhamnosidase, such as naringin, rutin, and hesperidin, which are poorly dissolved in water at room temperature. Protein thermal resistance is an important issue beyond its obvious industrial importance. The current study also helps in the structure-function relationship study of α-l-rhamnosidase.

Holistic engineering of Cal-A lipase chain-length selectivity identifies triglyceride binding hot-spot

Through the application of a region-focused saturation mutagenesis and randomization approach, protein engineering of the Cal-A enzyme was undertaken with the goal of conferring new triglyceride selectivity. Little is known about the mode of triglyceride binding to Cal-A. Engineering Cal-A thus requires a systemic approach. Targeted and randomized Cal-A libraries were created, recombined using the Golden Gate approach and screened to detect variants able to discriminate between long-chain (olive oil) and short-chain (tributyrin) triglyceride substrates using a high-throughput in vivo method to visualize hydrolytic activity. Discriminative variants were analyzed using an in-house script to identify predominant substitutions. This approach allowed identification of variants that exhibit strong discrimination for the hydrolysis of short-chain triglycerides and others that discriminate towards hydrolysis of long-chain triglycerides. A clear pattern emerged from the discriminative variants, identifying the 217–245 helix-loop-helix motif as being a hot-spot for triglyceride recognition. This was the consequence of introducing the entire mutational load in selected regions, without putting a strain on distal parts of the protein. Our results improve our understanding of the Cal-A lipase mode of action and selectivity. This holistic perspective to protein engineering, where parts of the gene are individually mutated and the impact evaluated in the context of the whole protein, can be applied to any protein scaffold.

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes

Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov'SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.

Application of fourier transform and proteochemometrics principles to protein engineering

Background

Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.

Results

We show that the digitalized representation of the protein sequence in the form of a Fourier spectrum can be used as an efficient descriptor to model the sequence-activity relationship of proteins. The iSAR methodology that we have developed identifies high fitness mutants from mutant libraries relying on physicochemical properties of the amino acids, digital signal processing and regression techniques. iSAR correlates variations caused by mutations in spectra with biological activity/fitness. It takes into account the impact of mutations on the whole spectrum and does not focus on local fitness alone. The utility of the method is illustrated on 4 datasets: cytochrome P450 for thermostability, TNF-alpha for binding affinity, GLP-2 for potency and enterotoxins for thermostability. The choice of the datasets has been made such as to illustrate the ability of the method to perform when limited training data is available and also when novel mutations appear in the test set, that have not been featured in the training set.

Conclusion

The combination of Fast Fourier Transform and Partial Least Squares regression is efficient in capturing the effects of mutations on the function of the protein. iSAR is a fast algorithm which can be implemented with limited computational resources and can make effective predictions even if the training set is limited in size.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2407-8) contains supplementary material, which is available to authorized users.

Automated Design of Efficient and Functionally Diverse Enzyme Repertoires

Substantial improvements in enzyme activity demand multiple mutations at spatially proximal positions in the active site. Such mutations, however, often exhibit unpredictable epistatic (non-additive) effects on activity. Here we describe FuncLib, an automated method for designing multipoint mutations at enzyme active sites using phylogenetic analysis and Rosetta design calculations. We applied FuncLib to two unrelated enzymes, a phosphotriesterase and an acetyl-CoA synthetase. All designs were active, and most showed activity profiles that significantly differed from the wild-type and from one another. Several dozen designs with only 3-6 active-site mutations exhibited 10- to 4,000-fold higher efficiencies with a range of alternative substrates, including hydrolysis of the toxic organophosphate nerve agents soman and cyclosarin and synthesis of butyryl-CoA. FuncLib is implemented as a web server (http://FuncLib.weizmann.ac.il); it circumvents iterative, high-throughput experimental screens and opens the way to designing highly efficient and diverse catalytic repertoires.

Exploring the combinatorial space of complete pathways to chemicals

Computational pathway design tools often face the challenges of balancing the stoichiometry of co-metabolites and cofactors, and dealing with reaction rule utilization in a single workflow. To this end, we provide an overview of two complementary stoichiometry-based pathway design tools optStoic and novoStoic developed in our group to tackle these challenges. optStoic is designed to determine the stoichiometry of overall conversion first which optimizes a performance criterion (e.g. high carbon/energy efficiency) and ensures a comprehensive search of co-metabolites and cofactors. The procedure then identifies the minimum number of intervening reactions to connect the source and sink metabolites. We also further the pathway design procedure by expanding the search space to include both known and hypothetical reactions, represented by reaction rules, in a new tool termed novoStoic. Reaction rules are derived based on a mixed-integer linear programming (MILP) compatible reaction operator, which allow us to explore natural promiscuous enzymes, engineer candidate enzymes that are not already promiscuous as well as design de novo enzymes. The identified biochemical reaction rules then guide novoStoic to design routes that expand the currently known biotransformation space using a single MILP modeling procedure. We demonstrate the use of the two computational tools in pathway elucidation by designing novel synthetic routes for isobutanol.

Strategies for design of improved biocatalysts for industrial applications

Biocatalysts are creating increased interest among researchers due to their unique properties. Several enzymes are efficiently produced by microorganisms. However, the use of natural enzymes as biocatalysts is hindered by low catalytic efficiency and stability during various industrial processes. Many advanced enzyme technologies have been developed to reshape the existing natural enzymes to reduce these limitations and prospecting of novel enzymes. Frequently used enzyme technologies include protein engineering by directed evolution, immobilisation techniques, metagenomics etc. This review summarizes recent and emerging advancements in the area of enzyme technologies for the development of novel biocatalysts and further discusses the future directions in this field.

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

Selection platforms for directed evolution in synthetic biology

Life on Earth is incredibly diverse. Yet, underneath that diversity, there are a number of constants and highly conserved processes: all life is based on DNA and RNA; the genetic code is universal; biology is limited to a small subset of potential chemistries. A vast amount of knowledge has been accrued through describing and characterizing enzymes, biological processes and organisms. Nevertheless, much remains to be understood about the natural world. One of the goals in Synthetic Biology is to recapitulate biological complexity from simple systems made from biological molecules-gaining a deeper understanding of life in the process. Directed evolution is a powerful tool in Synthetic Biology, able to bypass gaps in knowledge and capable of engineering even the most highly conserved biological processes. It encompasses a range of methodologies to create variation in a population and to select individual variants with the desired function-be it a ligand, enzyme, pathway or even whole organisms. Here, we present some of the basic frameworks that underpin all evolution platforms and review some of the recent contributions from directed evolution to synthetic biology, in particular methods that have been used to engineer the Central Dogma and the genetic code.

A robust cosolvent-compatible halohydrin dehalogenase by computational library design

To improve the applicability of halohydrin dehalogenase as a catalyst for reactions in the presence of organic cosolvents, we explored a computational library design strategy (Framework for Rapid Enzyme Stabilization by Computational libraries) that involves discovery and in silico evaluation of stabilizing mutations. Energy calculations, disulfide bond predictions and molecular dynamics simulations identified 218 point mutations and 35 disulfide bonds with predicted stabilizing effects. Experiments confirmed 29 stabilizing point mutations, most of which were located in two distinct regions, whereas introduction of disulfide bonds was not effective. Combining the best mutations resulted in a 12-fold mutant (HheC-H12) with a 28°C higher apparent melting temperature and a remarkable increase in resistance to cosolvents. This variant also showed a higher optimum temperature for catalysis while activity at low temperature was preserved. Mutant H12 was used as a template for the introduction of mutations that enhance enantioselectivity or activity. Crystal structures showed that the structural changes in the H12 mutant mostly agreed with the computational predictions and that the enhanced stability was mainly due to mutations that redistributed surface charges and improved interactions between subunits, the latter including better interactions of water molecules at the subunit interfaces.

Characterization of a Novel α-l-Arabinofuranosidase from Ruminococcus albus 7 and Rational Design for Its Thermostability

An α-l-arabinofuranosidase (Abf) encoding gene was obtained via genomic mining from a Ruminococcus albus strain. The specific activity of this GH 51 Abf was 73.3 U/mg at pH 6.0 and 50 °C. The modification of Abf, aimed at improving thermostability, was performed through different strategies. Structure-based rational design using the PoPMuSiC and the Enzyme Thermal Stability System (ETSS) predicted thermal stability of Abf and enhanced the half-life of thermal inactivation (t_1/2) at 50 °C for K208W more than 11.1 times versus the wild-type (WT). Sequence-based rational design was also conducted by substituting histidine with lysine at various sites. Among eight mutants, the t_1/2 at 50 °C of H337K was prolonged by 5.0-fold, and the specific activity of this mutant was increased to 121.8 U/mg. In addition, the mutant H337K was utilized with some enzymes to extract pectin from apple pomace. The enzymatically produced pectin got less moisture and ash, milder pH, and higher viscosity than its acid-extracted counterpart, indicating that Abf has an application prospect in pectin production.

A new concept for molecular engineering of artificial enzymes: a multiscale simulation

We propose a new concept for the design of artificial enzymes from synthetic protein-like copolymers and non-natural functional monomers which in terms of their affinity for water can be divided into two categories: hydrophobic and hydrophilic. Hydrophilic monomers comprise catalytically active groups similar to those in the corresponding amino acid residues. A key ingredient of our approach is that the target globular conformation of protein-like, core-shell morphology with multiple catalytic groups appears spontaneously in the course of controlled radical polymerization in a selective solvent. As a proof of concept, we construct a fully synthetic analog of serine hydrolase, e.g.α-chymotrypsin, using the conformation-dependent sequence design approach and multiscale simulation that combines the methods of "mesoscale chemistry" and atomistic molecular dynamics (MD). A 100 ns GPU-accelerated MD simulation of the designed polymer-supported catalyst in the aqueous environment provides valuable information on the structural organization of this system that has been synthesized in our Lab.

Proteus and the Design of Ligand Binding Sites

This chapter describes the organization and use of Proteus, a multitool computational suite for the optimization of protein and ligand conformations and sequences, and the calculation of pK α shifts and relative binding affinities. The software offers the use of several molecular mechanics force fields and solvent models, including two generalized Born variants, and a large range of scoring functions, which can combine protein stability, ligand affinity, and ligand specificity terms, for positive and negative design. We present in detail the steps for structure preparation, system setup, construction of the interaction energy matrix, protein sequence and structure optimizations, pK α calculations, and ligand titration calculations. We discuss illustrative examples, including the chemical/structural optimization of a complex between the MHC class II protein HLA-DQ8 and the vinculin epitope, and the chemical optimization of the compstatin analog Ac-Val4Trp/His9Ala, which regulates the function of protein C3 of the complement system.

Directing positional specificity in enzymatic synthesis of bioactive 1-phosphatidylinositol by protein engineering of a phospholipase D

Phosphatidylinositol (PI) holds a potential of becoming an important dietary supplement due to its effects on lipid metabolism in animals and humans manifested as a decrease of the blood cholesterol and lipids, and relief of the metabolic syndrome. To establish an efficient, enzymatic system for PI production from phosphatidylcholine and myo-inositol as an alcohol acceptor, our previous study started with the wild-type Streptomyces antibioticus phospholipase D (SaPLD) as a template for generation of PI-synthesizing variants by saturation mutagenesis targeting positions involved in acceptor accommodation, W187, Y191, and Y385. The isolated variants generated PI as a mixture of positional isomers, among which only 1-PI exists in nature. Thus, the current study has focused to improve positional specificity of W187N/Y191Y/Y385R SaPLD (NYR) which generates PI as a mixture of 1-PI and 3-PI in the ratio of 76/24, by subjecting four residues of its acceptor-binding site to saturation mutagenesis. Subsequent screening pointed at NYR-186T and NYR-186L as the most improved variants producing PI with a ratio of 1-/3-PI = 93/7 and 87/13, respectively, at 37°C. Lowering the reaction temperature further improved the specificity of both variants to 1-/3-PI > 97/3 at 20°C with no change in total PI yield. Structure model analyses imply that G186T and G186L mutations increased rigidity of the acceptor-binding site, thus limiting the possible orientations of myo-inositol. The two newly isolated PLDs are promising for future application in large-scale 1-PI production.

Machine learning assisted design of highly active peptides for drug discovery

The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides. Source code freely available at http://graal.ift.ulaval.ca/peptide-design/.

Part of the complexity of drug discovery is the sheer chemical diversity to explore combined to all requirements a compound must meet to become a commercial drug. Hence, it makes sense to automate this chemical exploration endeavor in a wise, informed, and efficient fashion. Here, we focused on peptides as they have properties that make them excellent drug starting points. Machine learning techniques may replace expensive in-vitro laboratory experiments by learning an accurate model of it. However, computational models also suffer from the combinatorial explosion due to the enormous chemical diversity. Indeed, applying the model to every peptides would take an astronomical amount of computer time. Therefore, given a model, is it possible to determine, using reasonable computational time, the peptide that has the best properties and chance for success? This exact question is what motivated our work. We focused on recent advances in kernel methods and machine learning to learn a model that already had excellent results. We demonstrate that this class of model has mathematical properties that makes it possible to rapidly identify and sort the best peptides. Finally, in-vitro and in-silico results are provided to support and validate this theoretical discovery.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Engineering the acyltransferase substrate specificity of assembly line polyketide synthases

Polyketide natural products act as a broad range of therapeutics, including antibiotics, immunosuppressants and anti-cancer agents. This therapeutic diversity stems from the structural diversity of these small molecules, many of which are produced in an assembly line manner by modular polyketide synthases. The acyltransferase (AT) domains of these megasynthases are responsible for selection and incorporation of simple monomeric building blocks, and are thus responsible for a large amount of the resulting polyketide structural diversity. The substrate specificity of these domains is often targeted for engineering in the generation of novel, therapeutically active natural products. This review outlines recent developments that can be used in the successful engineering of these domains, including AT sequence and structural data, mechanistic insights and the production of a diverse pool of extender units. It also provides an overview of previous AT domain engineering attempts, and concludes with proposed engineering approaches that take advantage of current knowledge. These approaches may lead to successful production of biologically active 'unnatural' natural products.

Protein design for pathway engineering

Design and construction of biochemical pathways has increased the complexity of biosynthetically-produced compounds when compared to single enzyme biocatalysis. However, the coordination of multiple enzymes can introduce a complicated set of obstacles to overcome in order to achieve a high titer and yield of the desired compound. Metabolic engineering has made great strides in developing tools to optimize the flux through a target pathway, but the inherent characteristics of a particular enzyme within the pathway can still limit the productivity. Thus, judicious protein design is critical for metabolic and pathway engineering. This review will describe various strategies and examples of applying protein design to pathway engineering to optimize the flux through the pathway. The proteins can be engineered for altered substrate specificity/selectivity, increased catalytic activity, reduced mass transfer limitations through specific protein localization, and reduced substrate/product inhibition. Protein engineering can also be expanded to design biosensors to enable high through-put screening and to customize cell signaling networks. These strategies have successfully engineered pathways for significantly increased productivity of the desired product or in the production of novel compounds.

Strategies for the discovery and engineering of enzymes for biocatalysis

Protein engineering is the most important method to overcome the limitations of natural enzymes as biocatalysts. The past few years have seen a tremendous increase in novel concepts to facilitate the design of mutant libraries for focused directed evolution mostly guided by advanced bioinformatic tools. In addition, advanced high-throughput methods were developed using, for example, FACS analysis or microfluidic systems. These achievements significantly facilitate the tailor-made design of enzymes to make them suitable for industrial applications.

Reducing codon redundancy and screening effort of combinatorial protein libraries created by saturation mutagenesis

Saturation mutagenesis probes define sections of the vast protein sequence space. However, even if randomization is limited this way, the combinatorial numbers problem is severe. Because diversity is created at the codon level, codon redundancy is a crucial factor determining the necessary effort for library screening. Additionally, due to the probabilistic nature of the sampling process, oversampling is required to ensure library completeness as well as a high probability to encounter all unique variants. Our trick employs a special mixture of three primers, creating a degeneracy of 22 unique codons coding for the 20 canonical amino acids. Therefore, codon redundancy and subsequent screening effort is significantly reduced, and a balanced distribution of codon per amino acid is achieved, as demonstrated exemplarily for a library of cyclohexanone monooxygenase. We show that this strategy is suitable for any saturation mutagenesis methodology to generate less-redundant libraries.

Computer-Aided Protein Directed Evolution: a Review of Web Servers, Databases and other Computational Tools for Protein Engineering

The combination of computational and directed evolution methods has proven a winning strategy for protein engineering. We refer to this approach as computer-aided protein directed evolution (CAPDE) and the review summarizes the recent developments in this rapidly growing field. We will restrict ourselves to overview the availability, usability and limitations of web servers, databases and other computational tools proposed in the last five years. The goal of this review is to provide concise information about currently available computational resources to assist the design of directed evolution based protein engineering experiment.

Recent advances in rational approaches for enzyme engineering

Enzymes are an attractive alternative in the asymmetric syntheses of chiral building blocks. To meet the requirements of industrial biotechnology and to introduce new functionalities, the enzymes need to be optimized by protein engineering. This article specifically reviews rational approaches for enzyme engineering and de novo enzyme design involving structure-based approaches developed in recent years for improvement of the enzymes’ performance, broadened substrate range, and creation of novel functionalities to obtain products with high added value for industrial applications.

Biocatalyst development by directed evolution

Biocatalysis has emerged as a great addition to traditional chemical processes for production of bulk chemicals and pharmaceuticals. To overcome the limitations of naturally occurring enzymes, directed evolution has become the most important tool for improving critical traits of biocatalysts such as thermostability, activity, selectivity, and tolerance towards organic solvents for industrial applications. Recent advances in mutant library creation and high-throughput screening have greatly facilitated the engineering of novel and improved biocatalysts. This review provides an update of the recent developments in the use of directed evolution to engineer biocatalysts for practical applications.

Structure-based prediction of protein activity changes: assessing the impact of single residue replacements

A computational mutagenesis methodology founded upon a structure-dependent and knowledge-based four-body statistical potential is utilized in generating feature vectors that characterize over 8500 individual amino acid substitutions occurring in seven proteins, each mutant having been experimentally ascertained for its relative effect on native protein activity. The proteins are diverse with respect to host organism (viral, bacterial, human) and function (enzymatic, nucleic acid binding, signaling), the structures span all four major SCOP classifications, and the mutations occur at positions well distributed throughout the seven structures. Implementation of the random forest algorithm, for classifying mutant activity as either unaffected or affected relative to the native protein, yields 84% accuracy based on tenfold cross-validation. A freely available online server for obtaining predictions with the trained model, which also displays 84% accuracy on an independent test set of mutants, is available at http://proteins.gmu.edu/automute/AUTO-MUTE_Activity.html.