LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries

Enzymes are potent catalysts with high specificity and selectivity. To leverage nature's synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.

Understanding structure-guided variant effect predictions using 3D convolutional neural networks

Predicting pathogenicity of missense variants in molecular diagnostics remains a challenge despite the available wealth of data, such as evolutionary information, and the wealth of tools to integrate that data. We describe DeepRank-Mut, a configurable framework designed to extract and learn from physicochemically relevant features of amino acids surrounding missense variants in 3D space. For each variant, various atomic and residue-level features are extracted from its structural environment, including sequence conservation scores of the surrounding amino acids, and stored in multi-channel 3D voxel grids which are then used to train a 3D convolutional neural network (3D-CNN). The resultant model gives a probabilistic estimate of whether a given input variant is disease-causing or benign. We find that the performance of our 3D-CNN model, on independent test datasets, is comparable to other widely used resources which also combine sequence and structural features. Based on the 10-fold cross-validation experiments, we achieve an average accuracy of 0.77 on the independent test datasets. We discuss the contribution of the variant neighborhood in the model's predictive power, in addition to the impact of individual features on the model's performance. Two key features: evolutionary information of residues in the variant neighborhood and their solvent accessibilities were observed to influence the predictions. We also highlight how predictions are impacted by the underlying disease mechanisms of missense mutations and offer insights into understanding these to improve pathogenicity predictions. Our study presents aspects to take into consideration when adopting deep learning approaches for protein structure-guided pathogenicity predictions.

Biological synthesis of ursodeoxycholic acid

Ursodeoxycholic acid (UDCA) is a fundamental treatment drug for numerous hepatobiliary diseases that also has adjuvant therapeutic effects on certain cancers and neurological diseases. Chemical UDCA synthesis is environmentally unfriendly with low yields. Biological UDCA synthesis by free-enzyme catalysis or whole-cell synthesis using inexpensive and readily available chenodeoxycholic acid (CDCA), cholic acid (CA), or lithocholic acid (LCA) as substrates is being developed. The free enzyme-catalyzed one-pot, one-step/two-step method uses hydroxysteroid dehydrogenase (HSDH); whole-cell synthesis, mainly uses engineered bacteria (mainly Escherichia coli) expressing the relevant HSDHs. To further develop these methods, HSDHs with specific coenzyme dependence, high enzyme activity, good stability, and high substrate loading concentration, P450 monooxygenase with C-7 hydroxylation activity and engineered strain harboring HSDHs must be exploited.

Redesigning Enzymes for Biocatalysis: Exploiting Structural Understanding for Improved Selectivity

The discovery of new enzymes, alongside the push to make chemical processes more sustainable, has resulted in increased industrial interest in the use of biocatalytic processes to produce high-value and chiral precursor chemicals. Huge strides in protein engineering methodology and in silico tools have facilitated significant progress in the discovery and production of enzymes for biocatalytic processes. However, there are significant gaps in our knowledge of the relationship between enzyme structure and function. This has demonstrated the need for improved computational methods to model mechanisms and understand structure dynamics. Here, we explore efforts to rationally modify enzymes toward changing aspects of their catalyzed chemistry. We highlight examples of enzymes where links between enzyme function and structure have been made, thus enabling rational changes to the enzyme structure to give predictable chemical outcomes. We look at future directions the field could take and the technologies that will enable it.

Trends in lipase engineering for enhanced biocatalysis

Lipases, also known as triacylglycerol hydrolases (E.C.No. 3.1.1.3), are considered as leading biocatalysts in the lipid modification business. With properties like ease of availability, capability to work in heterogeneous media, stability in organic solvents, property of catalyzing at the lipid-water interface and even in nonaqueous conditions, have made them a versatile choice for applications in the food, flavor, detergent, pharmaceutical, leather, textile, cosmetic, and paper industries [1]. The increasing alertness toward sustainable technologies, lesser waste generation and solvent usage and minimization of energy input has brought light toward the production and usage of recombinant/improved lipases. For example, Novozym 435, a broadly used recombinant lipase isolated from Candida antarctica, dominates the lipase industry and has even created a supplier bias in the market. This shows that there is a desperate need for novel, low-cost lipases with better properties. For this, mining of existing extremophilic genomes seems more rewarding. But considering the diversity of industrial requirements such as types of solvents used or carrier systems employed for enzyme immobilization, tailor-designed enzymes are an unrealized pressing priority. Therefore, protein engineering strategies in collaboration with the discovery of new lipases can serve as a vital tool to obtain tailor-made enzymes with specific characteristics.

Computational Redesign of an ω-Transaminase from Pseudomonas jessenii for Asymmetric Synthesis of Enantiopure Bulky Amines

ω-Transaminases (ω-TA) are attractive biocatalysts for the production of chiral amines from prochiral ketones via asymmetric synthesis. However, the substrate scope of ω-TAs is usually limited due to steric hindrance at the active site pockets. We explored a protein engineering strategy using computational design to expand the substrate scope of an (S)-selective ω-TA from Pseudomonas jessenii (PjTA-R6) toward the production of bulky amines. PjTA-R6 is attractive for use in applied biocatalysis due to its thermostability, tolerance to organic solvents, and acceptance of high concentrations of isopropylamine as amino donor. PjTA-R6 showed no detectable activity for the synthesis of six bicyclic or bulky amines targeted in this study. Six small libraries composed of 7–18 variants each were separately designed via computational methods and tested in the laboratory for ketone to amine conversion. In each library, the vast majority of the variants displayed the desired activity, and of the 40 different designs, 38 produced the target amine in good yield with >99% enantiomeric excess. This shows that the substrate scope and enantioselectivity of PjTA mutants could be predicted in silico with high accuracy. The single mutant W58G showed the best performance in the synthesis of five structurally similar bulky amines containing the indan and tetralin moieties. The best variant for the other bulky amine, 1-phenylbutylamine, was the triple mutant W58M + F86L + R417L, indicating that Trp58 is a key residue in the large binding pocket for PjTA-R6 redesign. Crystal structures of the two best variants confirmed the computationally predicted structures. The results show that computational design can be an efficient approach to rapidly expand the substrate scope of ω-TAs to produce enantiopure bulky amines.

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.

PyPEF-An Integrated Framework for Data-Driven Protein Engineering

Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. However, the reliable prediction of beneficial amino acid substitutions, their combination, and the effect on functional properties remain the most significant challenges in protein engineering, which is applied to develop proteins and enzymes for biocatalysis, biomedicine, and life sciences. Here, we present a general-purpose framework (PyPEF: pythonic protein engineering framework) for performing data-driven protein engineering using machine learning methods combined with techniques from signal processing and statistical physics. PyPEF guides the identification and selection of beneficial proteins of a defined sequence space by systematically or randomly exploring the fitness of variants and by sampling random evolution pathways. The performance of PyPEF was evaluated concerning its predictive accuracy and throughput on four public protein and enzyme data sets using common regression models. It was proved that the program could efficiently predict the fitness of protein sequences for different target properties (predictive models with coefficient of determination values ranging from 0.58 to 0.92). By combining machine learning and protein evolution, PyPEF enabled the screening of proteins with various functions, reaching a screening capacity of more than 500,000 protein sequence variants in the timeframe of only a few minutes on a personal computer. PyPEF displayed significant accuracies on four public data sets (different proteins and properties) and underlined the potential of integrating data-driven technologies for covering different philosophies by either predicting the fitness of the variants to the highest accuracy accounting for epistatic effects or capturing the general trend of introduced mutations on the fitness in directed protein evolution campaigns. In essence, PyPEF can provide a powerful solution to current sequence exploration and combinatorial problems faced in protein engineering through exhaustive in silico screening of the sequence space.

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.

Insights into the molecular determinants of thermal stability in halohydrin dehalogenase HheD2

Halohydrin dehalogenases (HHDHs) are promising enzymes for application in biocatalysis due to their promiscuous epoxide ring-opening activity with various anionic nucleophiles. So far, seven different HHDH subtypes A to G have been reported with subtype D containing the by far largest number of enzymes. Moreover, several characterized members of subtype D have been reported to display outstanding characteristics such as high catalytic activity, broad substrate spectra or remarkable thermal stability. Yet, no structure of a D-type HHDH has been reported to date that could be used to investigate and understand those features on a molecular level. We therefore solved the crystal structure of HheD2 from gamma proteobacterium HTCC2207 at 1.6 Å resolution and used it as a starting point for targeted mutagenesis in combination with molecular dynamics (MD) simulation, in order to study the low thermal stability of HheD2 in comparison with other members of subtype D. This revealed a hydrogen bond between conserved residues Q160 and D198 to be connected with a high catalytic activity of this enzyme. Moreover, a flexible surface region containing two α-helices was identified to impact thermal stability of HheD2. Exchange of this surface region by residues of HheD3 yielded a variant with 10 °C higher melting temperature and reaction temperature optimum. Overall, our results provide important insights into the structure-function relationship of HheD2 and presumably for other D-type HHDHs. DATABASES: Structural data are available in PDB database under the accession number 7B73.

Engineering Regioselectivity of a P450 Monooxygenase Enables the Synthesis of Ursodeoxycholic Acid via 7β-Hydroxylation of Lithocholic Acid

We engineered the cytochrome P450 monooxygenase CYP107D1 (OleP) from Streptomyces antibioticus for the stereo- and regioselective 7β-hydroxylation of lithocholic acid (LCA) to yield ursodeoxycholic acid (UDCA). OleP was previously shown to hydroxylate testosterone at the 7β-position but LCA is exclusively hydroxylated at the 6β-position, forming murideoxycholic acid (MDCA). Structural and 3DM analysis, and molecular docking were used to identify amino acid residues F84, S240, and V291 as specificity-determining residues. Alanine scanning identified S240A as a UDCA-producing variant. A synthetic "small but smart" library based on these positions was screened using a colorimetric assay for UDCA. We identified a nearly perfectly regio- and stereoselective triple mutant (F84Q/S240A/V291G) that produces 10-fold higher levels of UDCA than the S240A variant. This biocatalyst opens up new possibilities for the environmentally friendly synthesis of UDCA from the biological waste product LCA.

Protein Sequence Selection Method That Enables Full Consensus Design of Artificial l-Threonine 3-Dehydrogenases with Unique Enzymatic Properties

Exponentially increasing protein sequence data enables artificial enzyme design using sequence-based protein design methods, including full-consensus protein design (FCD). The success of artificial enzyme design is strongly dependent on the nature of the sequences used. Hence, sequences must be selected from databases and curated libraries prepared to enable a successful design by FCD. In this study, we proposed a selection approach regarding several key residues as sequence motifs. We used l-threonine 3-dehydrogenase (TDH) as a model to test the validity of this approach. In the classification, four residues (143, 174, 188, and 214) were used as key residues. We classified thousands of TDH homologous sequences into five groups containing hundreds of sequences. Utilizing sequences in the libraries, we designed five artificial TDHs by FCD. Among the five, we successfully expressed four in soluble form. Biochemical analysis of artificial TDHs indicated that their enzymatic properties vary; half of the maximum measured enzyme activity (t_1/2) and activation energies were distributed from 53 to 65 °C and from 38 to 125 kJ/mol, respectively. The artificial TDHs had unique kinetic parameters, distinct from one another. Structural analysis indicates that consensus mutations are mainly introduced in the secondary or outer shell. The functional diversity of the artificial TDHs is due to the accumulation of mutations that affect their physicochemical properties. Taken together, our findings indicate that our proposed approach can help generate artificial enzymes with unique enzymatic properties.

Determinants of the Nucleotide Specificity in the Carbohydrate Epimerase Family 1

In recent years, carbohydrate epimerases have attracted increasing attention as promising biocatalysts for the production of specialty sugars and derivatives. The vast majority of these enzymes are active on nucleotide-activated sugars, rather than on their free counterparts. Although such epimerases are known to have a clear preference for a particular nucleotide (UDP, GDP, CDP, or ADP), very little is known about the determinants of the respective specificities. In this work, sequence motifs are identified that correlate with the different nucleotide specificities in one of the main epimerase superfamilies, carbohydrate epimerase 1 (CEP1). To confirm their relevance, GDP- and CDP-specific residues are introduced into the UDP-glucose 4-epimerase from Thermus thermophilus, resulting in a 3-fold and 13-fold reduction in K_M for GDP-Glc and CDP-Glc, respectively. Moreover, several variants are severely crippled in UDP-Glc activity, which further underlines the crucial role of the identified positions. Hence, the analysis should prove to be valuable for the further exploration and application of epimerases involved in carbohydrate synthesis.

Penicillin Acylase from Streptomyces lavendulae and Aculeacin A Acylase from Actinoplanes utahensis: Two Versatile Enzymes as Useful Tools for Quorum Quenching Processes

Many Gram-negative bacteria produce N-acyl-homoserine lactones (AHLs), quorum sensing (QS) molecules that can be enzymatically inactivated by quorum quenching (QQ) processes; this approach is considered an emerging antimicrobial alternative. In this study, kinetic parameters of several AHLs hydrolyzed by penicillin acylase from Streptomyces lavendulae (SlPA) and aculeacin A acylase from Actinoplanes utahensis (AuAAC) have been determined. Both enzymes catalyze efficiently the amide bond hydrolysis in AHLs with different acyl chain moieties (with or without 3-oxo modification) and exhibit a clear preference for AHLs with long acyl chains (C12-HSL > C14-HSL > C10-HSL > C8-HSL for SlPA, whereas C14-HSL > C12-HSL > C10-HSL > C8-HSL for AuAAC). Involvement of SlPA and AuAAC in QQ processes was demonstrated by Chromobacterium violaceum CV026-based bioassays and inhibition of biofilm formation by Pseudomonas aeruginosa, a process controlled by QS molecules, suggesting the application of these multifunctional enzymes as quorum quenching agents, this being the first time that quorum quenching activity was shown by an aculeacin A acylase. In addition, a phylogenetic study suggests that SlPA and AuAAC could be part of a new family of actinomycete acylases, with a preference for substrates with long aliphatic acyl chains, and likely involved in QQ processes.

Structural and Evolutionary Analysis Indicate That the SARS-CoV-2 Mpro Is a Challenging Target for Small-Molecule Inhibitor Design

The novel coronavirus whose outbreak took place in December 2019 continues to spread at a rapid rate worldwide. In the absence of an effective vaccine, inhibitor repurposing or de novo drug design may offer a longer-term strategy to combat this and future infections due to similar viruses. Here, we report on detailed classical and mixed-solvent molecular dynamics simulations of the main protease (Mpro) enriched by evolutionary and stability analysis of the protein. The results were compared with those for a highly similar severe acute respiratory syndrome (SARS) Mpro protein. In spite of a high level of sequence similarity, the active sites in both proteins showed major differences in both shape and size, indicating that repurposing SARS drugs for COVID-19 may be futile. Furthermore, analysis of the binding site's conformational changes during the simulation time indicated its flexibility and plasticity, which dashes hopes for rapid and reliable drug design. Conversely, structural stability of the protein with respect to flexible loop mutations indicated that the virus' mutability will pose a further challenge to the rational design of small-molecule inhibitors. However, few residues contribute significantly to the protein stability and thus can be considered as key anchoring residues for Mpro inhibitor design.

Algorithm-based coevolution network identification reveals key functional residues of the α/β hydrolase subfamilies

Covariant residues identified by computational algorithms have provided new insights into enzyme evolutionary routes. However, the reliability and accuracy of routine statistical coupling analysis (SCA) are unable to satisfy the needs of protein engineering because SCA depends only on sequence information. Here, we set up a new SCA algorithm, SCA.SIM, by integrating structure information and MD simulation data. The more reliable covariant residues with high-quality scores are obtained from sequence alignment weighted by residual movement for eight related subfamilies, belonging to α/β hydrolase family, with Candida antarctica lipase B (CALB). The 38 predicted covariant residues are tested for function by high-throughput quantitative evaluation in combination with activity and thermostability assays of a mutant library and deep sequencing. Based on the landscapes of both activity and thermostability, most mutants play key roles in catalysis, and some mutants gain 2.4- to 6-fold increase in half-life at 50°C and 9- to 12-fold improvement in catalytic efficiency. The activity of double mutants for A225F/T103A is higher than those of A225F and T103A which means that SCA.SIM method might be useful for identifying the allosteric coupling. The SCA.SIM algorithm can be used for protein coevolution and enzyme engineering research.

Facilities that make the PDB data collection more powerful

We describe a series of databases and tools that directly or indirectly support biomedical research on macromolecules, with focus on their applicability in protein structure bioinformatics research. DSSP, that determines secondary structures of proteins, has been updated to work well with extremely large structures in multiple formats. The PDBREPORT database that lists anomalies in protein structures has been remade to remove many small problems. These reports are now available as PDF-formatted files with a computer-readable summary. The VASE software has been added to analyze and visualize HSSP multiple sequence alignments for protein structures. The Lists collection of databases has been extended with a series of databases, most noticeably with a database that gives each protein structure a grade for usefulness in protein structure bioinformatics projects. The PDB-REDO collection of reanalyzed and re-refined protein structures that were solved by X-ray crystallography has been improved by dealing better with sugar residues and with hydrogen bonds, and adding many missing surface loops. All academic software underlying these protein structure bioinformatics applications and databases are now publicly accessible, either directly from the authors or from the GitHub software repository.

Structural and functional role of disulphide bonds and substrate binding residues of the human beta-galactoside alpha-2,3-sialyltransferase 1 (hST3Gal1)

Overexpression of hST3Gal1 leads to hypersialylation of cell-surface glycoconjugates, a cancer-associated condition that promotes cell growth, migration and invasion. Upregulation of this enzyme in ovarian cancer is linked to cancer progression and metastasis, contributing also to chemotherapy resistance. Strategies for preventing metastasis include the inhibition of hST3Gal1, which demands structure-based studies on its strict regioselectivity and substrate/donor preference. Herein we describe the contribution of various residues constituting donor CMP-Neu5Ac and acceptor Galβ1-3GalNAc-R binding sites to catalysis. Removal of hydrogen bonds and/or stacking interactions among substrates and residues Y191, Y230, N147, S148 and N170 affected the enzyme’s activity to a different extent, revealing the fine control needed for an optimal catalytic performance. To gain further understanding of the correlation among structure, activity and stability, the in vitro role of hST3Gal1 disulphide bonds was analysed. As expected, disruption of the Glycosyltransferase family 29 (GT29) invariant bond C142-C281, as well as the ST3Gal1 subfamily conserved disulphide C61-C139 inactivates the enzyme. While disulphide C59-C64 is not essential for function, its absence reduces the activity (k_cat) for donor and acceptor substrates to about 67 and 72%, respectively, and diminishes the enzyme’s melting temperature (T_m) by 7 °C.

Dissecting the evolvability landscape of the CalB active site toward aromatic substrates

A key event in the directed evolution of enzymes is the systematic use of mutagenesis and selection, a process that can give rise to mutant libraries containing millions of protein variants. To this day, the functional analysis and identification of active variants among such high numbers of mutational possibilities is not a trivial task. Here, we describe a combinatorial semi-rational approach to partly overcome this challenge and help design smaller and smarter mutant libraries. By adapting a liquid medium transesterification assay in organic solvent conditions with a combination of virtual docking, iterative saturation mutagenesis, and residue interaction network (RIN) analysis, we engineered lipase B from P. antarctica (CalB) to improve enzyme recognition and activity against the bulky aromatic substrates and flavoring agents methyl cinnamate and methyl salicylate. Substrate-imprinted docking was used to target active-site positions involved in enzyme-substrate and enzyme-product complexes, in addition to identifying 'hot spots' most likely to yield active variants. This iterative semi-rational design strategy allowed selection of CalB variants exhibiting increased activity in just two rounds of site-saturation mutagenesis. Beneficial replacements were observed by screening only 0.308% of the theoretical library size, illustrating how semi-rational approaches with targeted diversity can quickly facilitate the discovery of improved activity variants relevant to a number of biotechnological applications.

Distant Non-Obvious Mutations Influence the Activity of a Hyperthermophilic Pyrococcus furiosus Phosphoglucose Isomerase

The cupin-type phosphoglucose isomerase (PfPGI) from the hyperthermophilic archaeon Pyrococcus furiosus catalyzes the reversible isomerization of glucose-6-phosphate to fructose-6-phosphate. We investigated PfPGI using protein-engineering bioinformatics tools to select functionally-important residues based on correlated mutation analyses. A pair of amino acids in the periphery of PfPGI was found to be the dominant co-evolving mutation. The position of these selected residues was found to be non-obvious to conventional protein engineering methods. We designed a small smart library of variants by substituting the co-evolved pair and screened their biochemical activity, which revealed their functional relevance. Four mutants were further selected from the library for purification, measurement of their specific activity, crystal structure determination, and metal cofactor coordination analysis. Though the mutant structures and metal cofactor coordination were strikingly similar, variations in their activity correlated with their fine-tuned dynamics and solvent access regulation. Alternative, small smart libraries for enzyme optimization are suggested by our approach, which is able to identify non-obvious yet beneficial mutations.

Pyruvate Carboxylase Variants Enabling Improved Lysine Production from Glucose Identified by Biosensor-Based High-Throughput Fluorescence-Activated Cell Sorting Screening

Pyruvate carboxylase is an anaplerotic carbon dioxide-fixing enzyme replenishing the tricarboxylic acid cycle with oxaloacetate during growth on sugars. In this study, we applied a lysine biosensor to identify pyruvate carboxylase variants in Corynebacterium glutamicum that enable improved l-lysine production from glucose. A suitable reporter strain was transformed with a pyc gene library created by error-prone PCR and screened by fluorescence-activated cell sorting (FACS) for cells with increased fluorescence triggered by an elevated cytoplasmic lysine concentration. Two pyruvate carboxylase variants, PCx^T343A,I1012S and PCx^T132A were identified allowing 9% and 19% increased lysine titers upon plasmid-based expression. Chromosomal expression of PCx^T132A and PCx^T343A variants led to 6% and 14% higher l-lysine levels. The new PCx variants can be useful also for other microbial strains producing TCA cycle-derived metabolites. Our approach indicates that a biosensor such as pSenLys enables directed evolution of many enzymes involved in converting a carbon source into the target metabolite.

nanoDSF as screening tool for enzyme libraries and biotechnology development

Enzymes are attractive tools for synthetic applications. To be viable for industrial use, enzymes need sufficient stability towards the desired reaction conditions such as high substrate and cosolvent concentration, non-neutral pH and elevated temperatures. Thermal stability is an attractive feature not only because it allows for protein purification by thermal treatment and higher process temperatures but also due to the associated higher stability against other destabilising factors. Therefore, high-throughput screening (HTS) methods are desirable for the identification of thermostable biocatalysts by discovery from nature or by protein engineering but current methods have low throughput and require time-demanding purification of protein samples. We found that nanoscale differential scanning fluorimetry (nanoDSF) is a valuable tool to rapidly and reliably determine melting points of native proteins. To avoid intrinsic problems posed by crude protein extracts, hypotonic extraction of overexpressed protein from bacterial host cells resulted in higher sample quality and accurate manual determination of several hundred melting temperatures per day. We have probed the use of nanoDSF for HTS of a phylogenetically diverse aldolase library to identify novel thermostable enzymes from metagenomic sources and for the rapid measurements of variants from saturation mutagenesis. The feasibility of nanoDSF for the screening of synthetic reaction conditions was proved by studies of cosolvent tolerance, which showed protein melting temperature to decrease linearly with increasing cosolvent concentration for all combinations of six enzymes and eight water-miscible cosolvents investigated, and of substrate affinity, which showed stabilisation of hexokinase by sugars in the absence of ATP cofactor. ENZYMES: Alcohol dehydrogenase (NADP⁺ ) (EC 1.1.1.2), transketolase (EC 2.2.1.1), hexokinase (EC 2.7.1.1), 2-deoxyribose-5-phosphate aldolase (EC 4.1.2.4), fructose-6-phosphate aldolase (EC 4.1.2.n).

BALCONY: an R package for MSA and functional compartments of protein variability analysis

BACKGROUND

Here, we present an R package for entropy/variability analysis that facilitates prompt and convenient data extraction, manipulation and visualization of protein features from multiple sequence alignments. BALCONY can work with residues dispersed across a protein sequence and map them on the corresponding alignment of homologous protein sequences. Additionally, it provides several entropy and variability scores that indicate the conservation of each residue.

RESULTS

Our package allows the user to visualize evolutionary variability by locating the positions most likely to vary and to assess mutation candidates in protein engineering.

CONCLUSION

In comparison to other R packages BALCONY allows conservation/variability analysis in context of protein structure with linkage of the appropriate metrics with physicochemical features of user choice.

AVAILABILITY

CRAN project page: https://cran.r-project.org/package=BALCONY and our website: http://www.tunnelinggroup.pl/software/ for major platforms: Linux/Unix, Windows and Mac OS X.

Complete Switch of Reaction Specificity of an Aldolase by Directed Evolution In Vitro: Synthesis of Generic Aliphatic Aldol Products

A structure-guided engineering of fructose-6-phosphate aldolase was performed to expand its substrate promiscuity toward aliphatic nucleophiles, that is, unsubstituted alkanones and alkanals. A "smart" combinatorial library was created targeting residues D6, T26, and N28, which form a binding pocket around the nucleophilic carbon atom. Double-selectivity screening was executed by high-performance TLC that allowed simultaneous determination of total activity as well as a preference for acetone versus propanal as competing nucleophiles. D6 turned out to be the key residue that enabled activity with non-hydroxylated nucleophiles. Altogether 25 single- and double-site variants (D6X and D6X/T26X) were discovered that show useful synthetic activity and a varying preference for ketone or aldehyde as the aldol nucleophiles. Remarkably, all of the novel variants had completely lost their native activity for cleavage of fructose 6-phosphate.

Engineering thermal stability and solvent tolerance of the soluble quinoprotein PedE from Pseudomonas putida KT2440 with a heterologous whole-cell screening approach

Due to their ability for direct electron transfer to electrodes, the utilization of rare earth metals as cofactor, and their periplasmic localization, pyrroloquinoline quinone-dependent alcohol dehydrogenases (PQQ-ADHs) represent an interesting class of biocatalysts for various biotechnological applications. For most biocatalysts protein stability is crucial, either to increase the performance of the protein under a given process condition or to maximize robustness of the protein towards mutational manipulations, which are often needed to enhance or introduce a functionality of interest. In this study, we describe a whole-cell screening assay, suitable for probing PQQ-ADH activities in Escherichia coli BL21(DE3) cells, and use this assay to screen smart mutant libraries for increased thermal stability of the PQQ-ADH PedE (PP_2674) from Pseudomonas putida KT2440. Upon three consecutive rounds of screening, we identified three different amino acid positions, which significantly improve enzyme stability. The subsequent combination of the beneficial mutations finally results in the triple mutant R91D/E408P/N410K, which not only exhibits a 7°C increase in thermal stability but also a twofold increase in residual activity upon incubation with up to 50% dimethyl sulfoxide (DMSO), while showing no significant difference in enzymatic efficiency (kcat /KM ).

Outer membrane phospholipase A's roles in Helicobacter pylori acid adaptation

Background

The pH of the human gastric mucosa varies around 2.5 so that only bacteria with strong acidic stress tolerance can colonize it. The ulcer causing Helicobacter pylori thrives in the gastric mucosa. We analyse the roles of the key outer membrane protein OMPLA in its roles in acid tolerance.

Results

The homology model of Helicobacter pylori outer membrane phospholipase A (OMPLA) reveals a twelve stranded β-barrel with a pore that allows molecules to pass with a diameter up to 4 Å. Structure based multiple sequence alignments revealed the functional roles of many amino acids, and led to the suggestion that OMPLA has multiple functions. Besides its role as phospholipase it lets urea enter and ammonium exit the periplasm. Combined with an extensive literature study, our work leads to a comprehensive model for H. pylori’s acid tolerance. This model is based on the conversion of urea into ammonium, and it includes multiple roles for OMPLA and involves two hitherto little studied membrane channels in the OMPLA operon.

Conclusion

The three-dimensional model of OMPLA predicts a transmembrane pore that can aid H. pylori’s acid tolerance through urea influx and ammonium efflux. After urea passes through OMPLA into the periplasm, it passes through the pH-gated inner membrane channel UreI into the cytoplasm where urease hydrolyses it into NH₃ and CO₂. Most of the NH₃ becomes NH₄⁺ that is likely to need an inner membrane channel to reach the periplasm. Two genes that are co-regulated with OMPLA in gastric Helicobacter operons could aid this transport. The NH₄⁺ that might leave the cell through the OMPLA pore has been implicated in H. pylor’s pathogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13099-017-0184-y) contains supplementary material, which is available to authorized users.

A quantitative indicator diagram for lytic polysaccharide monooxygenases reveals the role of aromatic surface residues in HjLPMO9A regioselectivity

Lytic polysaccharide monooxygenases (LPMOs) have changed our understanding of lignocellulosic degradation dramatically over the last years. These metalloproteins catalyze oxidative cleavage of recalcitrant polysaccharides and can act on the C1 and/or C4 position of glycosidic bonds. Structural data have led to several hypotheses, but we are still a long way from reaching complete understanding of the factors that determine their divergent regioselectivity. Site-directed mutagenesis enables the investigation of structure-function relationship in enzymes and will be of major importance in unraveling this intriguing matter. In this context, it is crucial to have an enzyme assay or screening approach with a direct correlation with the desired functionality. LPMOs render this search extra challenging due to their insoluble substrates, complex pattern of reaction products and lack of synthetic standards of most oxidized products. Here, we describe a regioselectivity indicator diagram based on the time-course of only 2 HPAEC-PAD signals. The diagram was successfully used to confirm the hypothesis that aromatic surface residues influence the C1/C4 oxidation ratio in Hypocrea jecorina LPMO9A. Consequently, the diagram should become a valuable tool in the search towards better understanding and engineering of regioselectivity in LPMOs.

CorNet: Assigning function to networks of co-evolving residues by automated literature mining

CorNet is a web-based tool for the analysis of co-evolving residue positions in protein super-family sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.

Enzyme discovery beyond homology: a unique hydroxynitrile lyase in the Bet v1 superfamily

Homology and similarity based approaches are most widely used for the identification of new enzymes for biocatalysis. However, they are not suitable to find truly novel scaffolds with a desired function and this averts options and diversity. Hydroxynitrile lyases (HNLs) are an example of non-homologous isofunctional enzymes for the synthesis of chiral cyanohydrins. Due to their convergent evolution, finding new representatives is challenging. Here we show the discovery of unique HNL enzymes from the fern Davallia tyermannii by coalescence of transcriptomics, proteomics and enzymatic screening. It is the first protein with a Bet v1-like protein fold exhibiting HNL activity, and has a new catalytic center, as shown by protein crystallography. Biochemical properties of D. tyermannii HNLs open perspectives for the development of a complementary class of biocatalysts for the stereoselective synthesis of cyanohydrins. This work shows that systematic integration of -omics data facilitates discovery of enzymes with unpredictable sequences and helps to extend our knowledge about enzyme diversity.

Computational tools for the evaluation of laboratory-engineered biocatalysts

Biocatalysis is based on the application of natural catalysts for new purposes, for which enzymes were not designed. Although the first examples of biocatalysis were reported more than a century ago, biocatalysis was revolutionized after the discovery of an in vitro version of Darwinian evolution called Directed Evolution (DE). Despite the recent advances in the field, major challenges remain to be addressed. Currently, the best experimental approach consists of creating multiple mutations simultaneously while limiting the choices using statistical methods. Still, tens of thousands of variants need to be tested experimentally, and little information is available on how these mutations lead to enhanced enzyme proficiency. This review aims to provide a brief description of the available computational techniques to unveil the molecular basis of improved catalysis achieved by DE. An overview of the strengths and weaknesses of current computational strategies is explored with some recent representative examples. The understanding of how this powerful technique is able to obtain highly active variants is important for the future development of more robust computational methods to predict amino-acid changes needed for activity.

In Silico Structure and Sequence Analysis of Bacterial Porins and Specific Diffusion Channels for Hydrophilic Molecules: Conservation, Multimericity and Multifunctionality

Diffusion channels are involved in the selective uptake of nutrients and form the largest outer membrane protein (OMP) family in Gram-negative bacteria. Differences in pore size and amino acid composition contribute to the specificity. Structure-based multiple sequence alignments shed light on the structure-function relations for all eight subclasses. Entropy-variability analysis results are correlated to known structural and functional aspects, such as structural integrity, multimericity, specificity and biological niche adaptation. The high mutation rate in their surface-exposed loops is likely an important mechanism for host immune system evasion. Multiple sequence alignments for each subclass revealed conserved residue positions that are involved in substrate recognition and specificity. An analysis of monomeric protein channels revealed particular sequence patterns of amino acids that were observed in other classes at multimeric interfaces. This adds to the emerging evidence that all members of the family exist in a multimeric state. Our findings are important for understanding the role of members of this family in a wide range of bacterial processes, including bacterial food uptake, survival and adaptation mechanisms.

Addressing biological uncertainties in engineering gene circuits

Synthetic biology has grown tremendously over the past fifteen years. It represents a new strategy to develop biological understanding and holds great promise for diverse practical applications. Engineering of a gene circuit typically involves computational design of the circuit, selection of circuit components, and test and optimization of circuit functions. A fundamental challenge in this process is the predictable control of circuit function due to multiple layers of biological uncertainties. These uncertainties can arise from different sources. We categorize these uncertainties into incomplete quantification of parts, interactions between heterologous components and the host, or stochastic dynamics of chemical reactions and outline potential design strategies to minimize or exploit them.

GPCRdb: an information system for G protein-coupled receptors

Recent developments in G protein-coupled receptor (GPCR) structural biology and pharmacology have greatly enhanced our knowledge of receptor structure-function relations, and have helped improve the scientific foundation for drug design studies. The GPCR database, GPCRdb, serves a dual role in disseminating and enabling new scientific developments by providing reference data, analysis tools and interactive diagrams. This paper highlights new features in the fifth major GPCRdb release: (i) GPCR crystal structure browsing, superposition and display of ligand interactions; (ii) direct deposition by users of point mutations and their effects on ligand binding; (iii) refined snake and helix box residue diagram looks; and (iii) phylogenetic trees with receptor classification colour schemes. Under the hood, the entire GPCRdb front- and back-ends have been re-coded within one infrastructure, ensuring a smooth browsing experience and development. GPCRdb is available at http://www.gpcrdb.org/ and it's open source code at https://bitbucket.org/gpcr/protwis.

Alteration of the Donor/Acceptor Spectrum of the (S)-Amine Transaminase from Vibrio fluvialis

To alter the amine donor/acceptor spectrum of an (S)-selective amine transaminase (ATA), a library based on the Vibrio fluvialis ATA targeting four residues close to the active site (L56, W57, R415 and L417) was created. A 3DM-derived alignment comprising fold class I pyridoxal-5′-phosphate (PLP)-dependent enzymes allowed identification of positions, which were assumed to determine substrate specificity. These positions were targeted for mutagenesis with a focused alphabet of hydrophobic amino acids to convert an amine:α-keto acid transferase into an amine:aldehyde transferase. Screening of 1200 variants revealed three hits, which showed a shifted amine donor/acceptor spectrum towards aliphatic aldehydes (mainly pentanal), as well as an altered pH profile. Interestingly, all three hits, although found independently, contained the same mutation R415L and additional W57F and L417V substitutions.

Evaluating the accuracy and efficiency of multiple sequence alignment methods

A comparison of 10 most popular Multiple Sequence Alignment (MSA) tools, namely, MUSCLE, MAFFT(L-INS-i), MAFFT (FFT-NS-2), T-Coffee, ProbCons, SATe, Clustal Omega, Kalign, Multalin, and Dialign-TX is presented. We also focused on the significance of some implementations embedded in algorithm of each tool. Based on 10 simulated trees of different number of taxa generated by R, 400 known alignments and sequence files were constructed using indel-Seq-Gen. A total of 4000 test alignments were generated to study the effect of sequence length, indel size, deletion rate, and insertion rate. Results showed that alignment quality was highly dependent on the number of deletions and insertions in the sequences and that the sequence length and indel size had a weaker effect. Overall, ProbCons was consistently on the top of list of the evaluated MSA tools. SATe, being little less accurate, was 529.10% faster than ProbCons and 236.72% faster than MAFFT(L-INS-i). Among other tools, Kalign and MUSCLE achieved the highest sum of pairs. We also considered BALiBASE benchmark datasets and the results relative to BAliBASE- and indel-Seq-Gen-generated alignments were consistent in the most cases.

Modeling catalytic promiscuity in the alkaline phosphatase superfamily

In recent years, it has become increasingly clear that promiscuity plays a key role in the evolution of new enzyme function. This finding has helped to elucidate fundamental aspects of molecular evolution. While there has been extensive experimental work on enzyme promiscuity, computational modeling of the chemical details of such promiscuity has traditionally fallen behind the advances in experimental studies, not least due to the nearly prohibitive computational cost involved in examining multiple substrates with multiple potential mechanisms and binding modes in atomic detail with a reasonable degree of accuracy. However, recent advances in both computational methodologies and power have allowed us to reach a stage in the field where we can start to overcome this problem, and molecular simulations can now provide accurate and efficient descriptions of complex biological systems with substantially less computational cost. This has led to significant advances in our understanding of enzyme function and evolution in a broader sense. Here, we will discuss currently available computational approaches that can allow us to probe the underlying molecular basis for enzyme specificity and selectivity, discussing the inherent strengths and weaknesses of each approach. As a case study, we will discuss recent computational work on different members of the alkaline phosphatase superfamily (AP) using a range of different approaches, showing the complementary insights they have provided. We have selected this particular superfamily, as it poses a number of significant challenges for theory, ranging from the complexity of the actual reaction mechanisms involved to the reliable modeling of the catalytic metal centers, as well as the very large system sizes. We will demonstrate that, through current advances in methodologies, computational tools can provide significant insight into the molecular basis for catalytic promiscuity, and, therefore, in turn, the mechanisms of protein functional evolution.

Computational tools for rational protein engineering of aldolases

In this mini-review we describe the different strategies for rational protein engineering and summarize the computational tools available. Computational tools can either be used to design focused libraries, to predict sequence-function relationships or for structure-based molecular modelling. This also includes de novo design of enzymes. Examples for protein engineering of aldolases and transaldolases are given in the second part of the mini-review.

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.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.