Exploration of Enzyme Diversity by Integrating Bioinformatics with Expression Analysis and Biochemical Characterization

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

Semi-rational design for enhancing thermostability of Culex pipiens acetylcholinesterase and sensitivity analysis of acephate

Acetylcholinesterase (AChE) has emerged as a significant biological recognition element in the biosensor field, particularly for the detection of insecticides. Nevertheless, the weak thermostability of AChE restricts its utilization due to the complexities associated with production, storage, and application environments. By evaluating the binding affinity between representative AChE and insecticides, an AChE from Culex pipiens was screened out, which displayed a broad-spectrum and high sensitivity to insecticides. The C. pipiens AChE (CpA) was subsequently expressed in Escherichia coli (E. coli) as a soluble active protein. Furthermore, a three-point mutant, M4 (A340P/D390E/S581P), was obtained using a semi-rational design strategy that combined molecular dynamics (MD) simulation and computer-aided design, which exhibited a four-fold increase in half-life at 40 °C compared to the wild-type (WT) enzyme. The mutant M4 also demonstrated an optimal temperature of 50 °C and a melting temperature (Tm) of 51.2 °C. Additionally, the sensitivity of WT and M4 to acephate was examined, revealing a 50-fold decrease in the IC50 value of M4. The mechanism underlying the improvement in thermal performance was elucidated through secondary structure analysis and MD simulations, indicating an increase in the proportion of protein helices and local structural rigidity. MD analysis of the protein-ligand complexes suggested that the enhanced sensitivity of M4 could be attributed to frequent specific contacts between the organophosphorus (OP) group of acephate and the key active site residue Ser327. These findings have expanded the possibilities for the development of more reliable and effective industrial enzyme preparations and biosensors.

Descriptor-augmented machine learning for enzyme-chemical interaction predictions

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.

Strategies for mitigation of pesticides from the environment through alternative approaches: A review of recent developments and future prospects

Chemical-based peticides are having negative impacts on both the healths of human beings and plants as well. The World Health Organisation (WHO), reported that each year, >25 million individuals in poor nations are having acute pesticide poisoning cases along with 20,000 fatal injuries at global level. Normally, only ∼0.1% of the pesticide reaches to the intended targets, and rest amount is expected to come into the food chain/environment for a longer period of time. Therefore, it is crucial to reduce the amounts of pesticides present in the soil. Physical or chemical treatments are either expensive or incapable to do so. Hence, pesticide detoxification can be achieved through bioremediation/biotechnologies, including nano-based methodologies, integrated approaches etc. These are relatively affordable, efficient and environmentally sound methods. Therefore, alternate strategies like as advanced biotechnological tools like as CRISPR Cas system, RNAi and genetic engineering for development of insects and pest resistant plants which are directly involved in the development of disease- and pest-resistant plants and indirectly reduce the use of pesticides. Omics tools and multi omics approaches like metagenomics, genomics, transcriptomics, proteomics, and metabolomics for the efficient functional gene mining and their validation for bioremediation of pesticides also discussed from the literatures. Overall, the review focuses on the most recent advancements in bioremediation methods to lessen the effects of pesticides along with the role of microorganisms in pesticides elimination. Further, pesticide detection is also a big challenge which can be done by using HPLC, GC, SERS, and LSPR ELISA etc. which have also been described in this review.

A method for the systematic selection of enzyme panel candidates by solving the maximum diversity problem

Enzymes are being increasingly exploited for their potential as industrial biocatalysts. Establishing a portfolio of useful biocatalysts from large and diverse protein family is challenging and a systematic method for candidate selection promises to aid in this task. Moreover, accurate enzyme functional annotation can only be confidently guaranteed through experimental characterisation in the laboratory. The selection of catalytically diverse enzyme panels for experimental characterisation is also an important step for shedding light on the currently unannotated proteins in enzyme families. Current selection methods often lack efficiency and scalability, and are usually non-systematic. We present a novel algorithm for the automatic selection of subsets from enzyme families. A tabu search algorithm solving the maximum diversity problem for sequence identity was designed and implemented, and applied to three diverse enzyme families. We show that this approach automatically selects panels of enzymes that contain high richness and relative abundance of the known catalytic functions, and outperforms other methods such as k-medoids.

Multimeric structure of a subfamily III haloalkane dehalogenase-like enzyme solved by combination of cryo-EM and x-ray crystallography

Haloalkane dehalogenase (HLD) enzymes employ an SN 2 nucleophilic substitution mechanism to erase halogen substituents in diverse organohalogen compounds. Subfamily I and II HLDs are well-characterized enzymes, but the mode and purpose of multimerization of subfamily III HLDs are unknown. Here we probe the structural organization of DhmeA, a subfamily III HLD-like enzyme from the archaeon Haloferax mediterranei, by combining cryo-electron microscopy (cryo-EM) and x-ray crystallography. We show that full-length wild-type DhmeA forms diverse quaternary structures, ranging from small oligomers to large supramolecular ring-like assemblies of various sizes and symmetries. We optimized sample preparation steps, enabling three-dimensional reconstructions of an oligomeric species by single-particle cryo-EM. Moreover, we engineered a crystallizable mutant (DhmeAΔGG ) that provided diffraction-quality crystals. The 3.3 Å crystal structure reveals that DhmeAΔGG forms a ring-like 20-mer structure with outer and inner diameter of ~200 and ~80 Å, respectively. An enzyme homodimer represents a basic repeating building unit of the crystallographic ring. Three assembly interfaces (dimerization, tetramerization, and multimerization) were identified to form the supramolecular ring that displays a negatively charged exterior, while its interior part harboring catalytic sites is positively charged. Localization and exposure of catalytic machineries suggest a possible processing of large negatively charged macromolecular substrates.

Structure-Based Design of Small Imine Reductase Panels for Target Substrates

Biocatalysis is important in the discovery, development, and manufacture of pharmaceuticals. However, the identification of enzymes for target transformations of interest requires major screening efforts. Here, we report a structure-based computational workflow to prioritize protein sequences by a score based on predicted activities on substrates, thereby reducing a resource-intensive laboratory-based biocatalyst screening. We selected imine reductases (IREDs) as a class of biocatalysts to illustrate the application of the computational workflow termed IREDFisher. Validation by using published data showed that IREDFisher can retrieve the best enzymes and increase the hit rate by identifying the top 20 ranked sequences. The power of IREDFisher is confirmed by computationally screening 1400 sequences for chosen reductive amination reactions with different levels of complexity. Highly active IREDs were identified by only testing 20 samples in vitro. Our speed test shows that it only takes 90 min to rank 85 sequences from user input and 30 min for the established IREDFisher database containing 591 IRED sequences. IREDFisher is available as a user-friendly web interface (https://enzymeevolver.com/IREDFisher). IREDFisher enables the rapid discovery of IREDs for applications in synthesis and directed evolution studies, with minimal time and resource expenditure. Future use of the workflow with other enzyme families could be implemented following the modification of the workflow scoring function.

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

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

Thrombolysis for acute ischaemic stroke: current status and future perspectives

Alteplase is currently the only approved thrombolytic agent for treatment of acute ischaemic stroke, but interest is burgeoning in the development of new thrombolytic agents for systemic reperfusion with an improved safety profile, increased efficacy, and convenient delivery. Tenecteplase has emerged as a potential alternative thrombolytic agent that might be preferred over alteplase because of its ease of administration and reported efficacy in patients with large vessel occlusion. Ongoing research efforts are also looking at potential improvements in recanalisation with the use of adjunct therapies to intravenous thrombolysis. New treatment strategies are also emerging that aim to reduce the risk of vessel reocclusion after intravenous thrombolysis administration. Other research endeavors are looking at the use of intra-arterial thrombolysis after mechanical thrombectomy to induce tissue reperfusion. The growing implementation of mobile stroke units and advanced neuroimaging could boost the number of patients who can receive intravenous thrombolysis by shortening onset-to-treatment times and identifying patients with salvageable penumbra. Continued improvements in this area will be essential to facilitate the ongoing research endeavors and to improve delivery of new interventions.

The Sabatier principle as a tool for discovery and engineering of industrial enzymes

The recent breakthrough in all-atom, protein structure prediction opens new avenues for a range of computational approaches in enzyme design. These new approaches could become instrumental for the development of technical biocatalysts, and hence our transition toward more sustainable industries. Here, we discuss one approach, which is well-known within inorganic catalysis, but essentially unexploited in biotechnology. Specifically, we review examples of linear free-energy relationships (LFERs) for enzyme reactions and discuss how LFERs and the associated Sabatier Principle may be implemented in algorithms that estimate kinetic parameters and enzyme performance based on model structures.

Enhancement of solubility of recombinant alcohol dehydrogenase from Rhodococcus ruber using predictive tool

Solubility is one of key factors influencing the heterologous production of recombinant proteins in biotechnology. Among many aggregation-prone proteins, alcohol dehydrogenase (ADH-A) from Rhodococcus ruber (in this work abbreviated RrADH) shows a great potential in processes involved in the biotransformation of natural compounds. As ADH-A is a potentially high value asset in industrial biotransformation processes, improvement of its solubility would be of major commercial benefit. Predictive tools and in silico analysis provide a fast means for improving protein properties, for selecting appropriate changes, and ultimately for saving costs. We have therefore focused on enhancement of the solubility of RrADH using an online accesible predictive tool Aggrescan 3D 2.0. Selected mutations were introduced into the protein amino acid sequence by using site-directed PCR. This led to a 17% increase in the protein solubility of RrADHmut1 and a 98% increase for RrADHmut2. Moreover, the basic kinetics of the enzyme reaction were positively affected, further optimizing the overall performance of the production process.

Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling

HaloTag labeling technology has introduced unrivaled potential in protein chemistry and molecular and cellular biology. A wide variety of ligands have been developed to meet the specific needs of diverse applications, but only a single protein tag, DhaAHT, is routinely used for their incorporation. Following a systematic kinetic and computational analysis of different reporters, a tetramethylrhodamine- and three 4-stilbazolium-based fluorescent ligands, we showed that the mechanism of incorporating different ligands depends both on the binding step and the efficiency of the chemical reaction. By studying the different haloalkane dehalogenases DhaA, LinB, and DmmA, we found that the architecture of the access tunnels is critical for the kinetics of both steps and the ligand specificity. We showed that highly efficient labeling with specific ligands is achievable with natural dehalogenases. We propose a simple protocol for selecting the optimal protein tag for a specific ligand from the wide pool of available enzymes with diverse access tunnel architectures. The application of this protocol eliminates the need for expensive and laborious protein engineering.

In silico screening and heterologous expression of soluble dimethyl sulfide monooxygenases of microbial origin in Escherichia coli

Abstract

Sequence-based screening has been widely applied in the discovery of novel microbial enzymes. However, majority of the sequences in the genomic databases were annotated using computational approaches and lacks experimental characterization. Hence, the success in obtaining the functional biocatalysts with improved characteristics requires an efficient screening method that considers a wide array of factors. Recombinant expression of microbial enzymes is often hampered by the undesirable formation of inclusion body. Here, we present a systematic in silico screening method to identify the proteins expressible in soluble form and with the desired biological properties. The screening approach was adopted in the recombinant expression of dimethyl sulfide (DMS) monooxygenase in Escherichia coli. DMS monooxygenase, a two-component enzyme consisting of DmoA and DmoB subunits, was used as a model protein. The success rate of producing soluble and active DmoA is 71% (5 out of 7 genes). Interestingly, the soluble recombinant DmoA enzymes exhibited the NADH:FMN oxidoreductase activity in the absence of DmoB (second subunit), and the cofactor FMN, suggesting that DmoA is also an oxidoreductase. DmoA originated from Janthinobacterium sp. AD80 showed the maximum NADH oxidation activity (maximum reaction rate: 6.6 µM/min; specific activity: 133 µM/min/mg). This novel finding may allow DmoA to be used as an oxidoreductase biocatalyst for various industrial applications. The in silico gene screening methodology established from this study can increase the success rate of producing soluble and functional enzymes while avoiding the laborious trial and error involved in the screening of a large pool of genes available.

Key points

• A systematic gene screening method was demonstrated.

• DmoA is also an oxidoreductase capable of oxidizing NADH and reducing FMN.

• DmoA oxidizes NADH in the absence of external FMN.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-022-12008-8.

A Nonconventional Archaeal Fluorinase Identified by In Silico Mining for Enhanced Fluorine Biocatalysis

Fluorinases, the only enzymes known to catalyze the transfer of fluorine to an organic molecule, are essential catalysts for the biological synthesis of valuable organofluorines. However, the few fluorinases identified so far have low turnover rates that hamper biotechnological applications. Here, we isolated and characterized putative fluorinases retrieved from systematic in silico mining and identified a nonconventional archaeal enzyme from Methanosaeta sp. that mediates the fastest S_N2 fluorination rate reported to date. Furthermore, we demonstrate enhanced production of fluoronucleotides in vivo in a bacterial host engineered with this archaeal fluorinase, paving the way toward synthetic metabolism for efficient biohalogenation.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

Mechanism-Based Design of Efficient PET Hydrolases

Polyethylene terephthalate (PET) is the most widespread synthetic polyester, having been utilized in textile fibers and packaging materials for beverages and food, contributing considerably to the global solid waste stream and environmental plastic pollution. While enzymatic PET recycling and upcycling have recently emerged as viable disposal methods for a circular plastic economy, only a handful of benchmark enzymes have been thoroughly described and subjected to protein engineering for improved properties over the last 16 years. By analyzing the specific material properties of PET and the reaction mechanisms in the context of interfacial biocatalysis, this Perspective identifies several limitations in current enzymatic PET degradation approaches. Unbalanced enzyme-substrate interactions, limited thermostability, and low catalytic efficiency at elevated reaction temperatures, and inhibition caused by oligomeric degradation intermediates still hamper industrial applications that require high catalytic efficiency. To overcome these limitations, successful protein engineering studies using innovative experimental and computational approaches have been published extensively in recent years in this thriving research field and are summarized and discussed in detail here. The acquired knowledge and experience will be applied in the near future to address plastic waste contributed by other mass-produced polymer types (e.g., polyamides and polyurethanes) that should also be properly disposed by biotechnological approaches.

Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class

Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation.

Correct annotation of genomes is crucial for our understanding and utilization of functional gene diversity, yet the reliability of current protein annotations in public databases is largely unknown. In our work we validated annotations to an S-2-hydroxyacid oxidase enzyme class (EC 1.1.3.15) by assessing activity of 122 representative sequences in a high-throughput screening experiment. From this dataset we inferred that at least 78% of the sequences in the enzyme class are misannotated, and confirmed four alternative activities among the misannotated sequences. We showed that the misannotation is widespread throughout enzyme classes, affecting even well-studied classes of industrial relevance. Overall, our study highlights the value of experimental and computational validation of predicted functions within individual enzyme classes.

New tyrosinases with putative action against contaminants of emerging concern

Tyrosinases (EC 1.14.18.1) are type-3 copper metalloenzymes with strong oxidative capacities and low allosteric selectivity to phenolic and non-phenolic aromatic compounds, which have been used as biosensors and biocatalysts to mitigate the impacts of environmental contaminants over aquatic ecosystems. However, the widespread use of these polyphenol oxidases is limited by elevated production costs and restricted knowledge on their spectrum of action. Here, six tyrosinase homologs were identified and characterized from the genomes of four widespread freshwater ciliates using bioinformatics. Next, we performed a virtual screening to calculate binding energies between 3D models of these homologs and ~ 1000 contaminants of emerging concern (CECs), as an indirect approach to identify likely and unlikely targets for tyrosinases. Many fine chemicals, pharmaceuticals, personal care products, illicit drugs, natural toxins, and pesticides exhibited strong binding energies to these new tyrosinases, suggesting the spectrum of targets of these enzymes might be considerably broader than previously thought. Many ciliates, including those carrying tyrosinase genes, are fast-growing unicellular microeukaryotes that can be efficiently cultured, at large scales, under in vitro conditions, suggesting these organisms should be regarded as potential low-cost sources of new environmental biotechnological molecules.

Web-based tools for computational enzyme design

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

The tetrameric structure of the novel haloalkane dehalogenase DpaA from Paraglaciecola agarilytica NO2

Haloalkane dehalogenases (EC 3.8.1.5) are microbial enzymes that catalyse the hydrolytic conversion of halogenated compounds, resulting in a halide ion, a proton and an alcohol. These enzymes are used in industrial biocatalysis, bioremediation and biosensing of environmental pollutants or for molecular tagging in cell biology. The novel haloalkane dehalogenase DpaA described here was isolated from the psychrophilic and halophilic bacterium Paraglaciecola agarilytica NO2, which was found in marine sediment collected from the East Sea near Korea. Gel-filtration experiments and size-exclusion chromatography provided information about the dimeric composition of the enzyme in solution. The DpaA enzyme was crystallized using the sitting-drop vapour-diffusion method, yielding rod-like crystals that diffracted X-rays to 2.0 Å resolution. Diffraction data analysis revealed a case of merohedral twinning, and subsequent structure modelling and refinement resulted in a tetrameric model of DpaA, highlighting an uncommon multimeric nature for a protein belonging to haloalkane dehalogenase subfamily I.

Screening and characterization of a diverse panel of metagenomic imine reductases for biocatalytic reductive amination

Finding faster and simpler ways to screen protein sequence space to enable the identification of new biocatalysts for asymmetric synthesis remains both a challenge and a rate-limiting step in enzyme discovery. Biocatalytic strategies for the synthesis of chiral amines are increasingly attractive and include enzymatic asymmetric reductive amination, which offers an efficient route to many of these high-value compounds. Here we report the discovery of over 300 new imine reductases and the production of a large (384 enzymes) and sequence-diverse panel of imine reductases available for screening. We also report the development of a facile high-throughput screen to interrogate their activity. Through this approach we identified imine reductase biocatalysts capable of accepting structurally demanding ketones and amines, which include the preparative synthesis of N-substituted β-amino ester derivatives via a dynamic kinetic resolution process, with excellent yields and stereochemical purities.

Development and Testing of Thrombolytics in Stroke

Despite recent advances in recanalization therapy, mechanical thrombectomy will never be a treatment for every ischemic stroke because access to mechanical thrombectomy is still limited in many countries. Moreover, many ischemic strokes are caused by occlusion of cerebral arteries that cannot be reached by intra-arterial catheters. Reperfusion using thrombolytic agents will therefore remain an important therapy for hyperacute ischemic stroke. However, thrombolytic drugs have shown limited efficacy and notable hemorrhagic complication rates, leaving room for improvement. A comprehensive understanding of basic and clinical research pipelines as well as the current status of thrombolytic therapy will help facilitate the development of new thrombolytics. Compared with alteplase, an ideal thrombolytic agent is expected to provide faster reperfusion in more patients; prevent re-occlusions; have higher fibrin specificity for selective activation of clot-bound plasminogen to decrease bleeding complications; be retained in the blood for a longer time to minimize dosage and allow administration as a single bolus; be more resistant to inhibitors; and be less antigenic for repetitive usage. Here, we review the currently available thrombolytics, strategies for the development of new clot-dissolving substances, and the assessment of thrombolytic efficacies in vitro and in vivo.

SoluProt: Prediction of Soluble Protein Expression in Escherichia coli

Motivation

Poor protein solubility hinders the production of many therapeutic and industrially useful proteins. Experimental efforts to increase solubility are plagued by low success rates and often reduce biological activity. Computational prediction of protein expressibility and solubility in Escherichia coli using only sequence information could reduce the cost of experimental studies by enabling prioritization of highly soluble proteins.

Results

A new tool for sequence-based prediction of soluble protein expression in E.coli, SoluProt, was created using the gradient boosting machine technique with the TargetTrack database as a training set. When evaluated against a balanced independent test set derived from the NESG database, SoluProt’s accuracy of 58.5% and AUC of 0.62 exceeded those of a suite of alternative solubility prediction tools. There is also evidence that it could significantly increase the success rate of experimental protein studies. SoluProt is freely available as a standalone program and a user-friendly webserver at https://loschmidt.chemi.muni.cz/soluprot/.

Availability and implementation

https://loschmidt.chemi.muni.cz/soluprot/.

Supplementary information

Supplementary data are available at Bioinformatics online.

EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities

Millions of protein sequences are being discovered at an incredible pace, representing an inexhaustible source of biocatalysts. Despite genomic databases growing exponentially, classical biochemical characterization techniques are time-demanding, cost-ineffective and low-throughput. Therefore, computational methods are being developed to explore the unmapped sequence space efficiently. Selection of putative enzymes for biochemical characterization based on rational and robust analysis of all available sequences remains an unsolved problem. To address this challenge, we have developed EnzymeMiner—a web server for automated screening and annotation of diverse family members that enables selection of hits for wet-lab experiments. EnzymeMiner prioritizes sequences that are more likely to preserve the catalytic activity and are heterologously expressible in a soluble form in Escherichia coli. The solubility prediction employs the in-house SoluProt predictor developed using machine learning. EnzymeMiner reduces the time devoted to data gathering, multi-step analysis, sequence prioritization and selection from days to hours. The successful use case for the haloalkane dehalogenase family is described in a comprehensive tutorial available on the EnzymeMiner web page. EnzymeMiner is a universal tool applicable to any enzyme family that provides an interactive and easy-to-use web interface freely available at https://loschmidt.chemi.muni.cz/enzymeminer/.

A Haloalkane Dehalogenase from Saccharomonospora viridis Strain DSM 43017, a Compost Bacterium with Unusual Catalytic Residues, Unique (S)-Enantiopreference, and High Thermostability

Haloalkane dehalogenases can cleave a carbon-halogen bond in a broad range of halogenated aliphatic compounds. However, a highly conserved catalytic pentad composed of a nucleophile, a catalytic base, a catalytic acid, and two halide-stabilizing residues is required for their catalytic activity. Only a few family members, e.g., DsaA, DmxA, or DmrB, remain catalytically active while employing a single halide-stabilizing residue. Here, we describe a novel haloalkane dehalogenase, DsvA, from a mildly thermophilic bacterium, Saccharomonospora viridis strain DSM 43017, possessing one canonical halide-stabilizing tryptophan (W125). At the position of the second halide-stabilizing residue, DsvA contains the phenylalanine F165, which cannot stabilize the halogen anion released during the enzymatic reaction by a hydrogen bond. Based on the sequence and structural alignments, we identified a putative second halide-stabilizing tryptophan (W162) located on the same α-helix as F165, but on the opposite side of the active site. The potential involvement of this residue in DsvA catalysis was investigated by the construction and biochemical characterization of the three variants, DsvA01 (F165W), DsvA02 (W162F), and DsvA03 (W162F and F165W). Interestingly, DsvA exhibits a preference for the (S)- over the (R)-enantiomers of β-bromoalkanes, which has not been reported before for any characterized haloalkane dehalogenase. Moreover, DsvA shows remarkable operational stability at elevated temperatures. The present study illustrates that protein sequences possessing an unconventional composition of catalytic residues represent a valuable source of novel biocatalysts.IMPORTANCE The present study describes a novel haloalkane dehalogenase, DsvA, originating from a mildly thermophilic bacterium, Saccharomonospora viridis strain DSM 43017. We report its high thermostability, remarkable operational stability at high temperatures, and an (S)-enantiopreference, which makes this enzyme an attractive biocatalyst for practical applications. Sequence analysis revealed that DsvA possesses an unusual composition of halide-stabilizing tryptophan residues in its active site. We constructed and biochemically characterized two single point mutants and one double point mutant and identified the noncanonical halide-stabilizing residue. Our study underlines the importance of searching for noncanonical catalytic residues in protein sequences.

Exploration of enzyme diversity: High-throughput techniques for protein production and microscale biochemical characterization

Enzymes are being increasingly utilized for acceleration of industrially and pharmaceutically critical chemical reactions. The strong demand for finding robust and efficient biocatalysts for these applications can be satisfied via the exploration of enzyme diversity. The first strategy is to mine the natural diversity, represented by millions of sequences available in the public genomic databases, by using computational approaches. Alternatively, metagenomic libraries can be targeted experimentally or computationally to explore the natural diversity of a specific environment. The second strategy, known as directed evolution, is to generate man-made diversity in the laboratory using gene mutagenesis and screen the constructed library of mutants. The selected hits must be experimentally characterized in both strategies, which currently represent the rate-limiting step in the process of diversity exploration. The traditional techniques used for biochemical characterization are time-demanding, cost, and sample volume ineffective, and low-throughput. Therefore, the development and implementation of high-throughput experimental methods are essential for discovering novel enzymes. This chapter describes the experimental protocols employing the combination of robust production and high-throughput microscale biochemical characterization of enzyme variants. We validated its applicability against the model enzyme family of haloalkane dehalogenases. These protocols can be adapted to other enzyme families, paving the way towards the functional characterization and quick identification of novel biocatalysts.

An Ultrasensitive Fluorescence Assay for the Detection of Halides and Enzymatic Dehalogenation

Halide assays are important for the study of enzymatic dehalogenation, a topic of great industrial and scientific importance. Here we describe the development of a very sensitive halide assay that can detect less than a picomole of bromide ions, making it very useful for quantifying enzymatic dehalogenation products. Halides are oxidised under mild conditions using the vanadium-dependent chloroperoxidase from Curvularia inaequalis, forming hypohalous acids that are detected using aminophenyl fluorescein. The assay is up to three orders of magnitude more sensitive than currently available alternatives, with detection limits of 20 nM for bromide and 1 μM for chloride and iodide. We demonstrate that the assay can be used to determine specific activities of dehalogenases and validate this by comparison to a well-established GC-MS method. This new assay will facilitate the identification and characterisation of novel dehalogenases and may also be of interest to those studying other halide-producing enzymes.

Deciphering the Structural Basis of High Thermostability of Dehalogenase from Psychrophilic Bacterium Marinobacter sp. ELB17

Haloalkane dehalogenases are enzymes with a broad application potential in biocatalysis, bioremediation, biosensing and cell imaging. The new haloalkane dehalogenase DmxA originating from the psychrophilic bacterium Marinobacter sp. ELB17 surprisingly possesses the highest thermal stability (apparent melting temperature Tm,app = 65.9 °C) of all biochemically characterized wild type haloalkane dehalogenases belonging to subfamily II. The enzyme was successfully expressed and its crystal structure was solved at 1.45 Å resolution. DmxA structure contains several features distinct from known members of haloalkane dehalogenase family: (i) a unique composition of catalytic residues; (ii) a dimeric state mediated by a disulfide bridge; and (iii) narrow tunnels connecting the enzyme active site with the surrounding solvent. The importance of narrow tunnels in such paradoxically high stability of DmxA enzyme was confirmed by computational protein design and mutagenesis experiments.

Controlled Oil/Water Partitioning of Hydrophobic Substrates Extending the Bioanalytical Applications of Droplet-Based Microfluidics

Functional annotation of novel proteins lags behind the number of sequences discovered by the next-generation sequencing. The throughput of conventional testing methods is far too low compared to sequencing; thus, experimental alternatives are needed. Microfluidics offer high throughput and reduced sample consumption as a tool to keep up with a sequence-based exploration of protein diversity. The most promising droplet-based systems have a significant limitation: leakage of hydrophobic compounds from water compartments to the carrier prevents their use with hydrophilic reagents. Here, we present a novel approach of substrate delivery into microfluidic droplets and apply it to high-throughput functional characterization of enzymes that convert hydrophobic substrates. Substrate delivery is based on the partitioning of hydrophobic chemicals between the oil and water phases. We applied a controlled distribution of 27 hydrophobic haloalkanes from oil to reaction water droplets to perform substrate specificity screening of eight model enzymes from the haloalkane dehalogenase family. This droplet-on-demand microfluidic system reduces the reaction volume 65 000-times and increases the analysis speed almost 100-fold compared to the classical test tube assay. Additionally, the microfluidic setup enables a convenient analysis of dependences of activity on the temperature in a range of 5 to 90 °C for a set of mesophilic and hyperstable enzyme variants. A high correlation between the microfluidic and test tube data supports the approach robustness. The precision is coupled to a considerable throughput of >20 000 reactions per day and will be especially useful for extending the scope of microfluidic applications for high-throughput analysis of reactions including compounds with limited water solubility.

Structural Biology and Protein Engineering of Thrombolytics

Myocardial infarction and ischemic stroke are the most frequent causes of death or disability worldwide. Due to their ability to dissolve blood clots, the thrombolytics are frequently used for their treatment. Improving the effectiveness of thrombolytics for clinical uses is of great interest. The knowledge of the multiple roles of the endogenous thrombolytics and the fibrinolytic system grows continuously. The effects of thrombolytics on the alteration of the nervous system and the regulation of the cell migration offer promising novel uses for treating neurodegenerative disorders or targeting cancer metastasis. However, secondary activities of thrombolytics may lead to life-threatening side-effects such as intracranial bleeding and neurotoxicity. Here we provide a structural biology perspective on various thrombolytic enzymes and their key properties: (i) effectiveness of clot lysis, (ii) affinity and specificity towards fibrin, (iii) biological half-life, (iv) mechanisms of activation/inhibition, and (v) risks of side effects. This information needs to be carefully considered while establishing protein engineering strategies aiming at the development of novel thrombolytics. Current trends and perspectives are discussed, including the screening for novel enzymes and small molecules, the enhancement of fibrin specificity by protein engineering, the suppression of interactions with native receptors, liposomal encapsulation and targeted release, the application of adjuvants, and the development of improved production systems.

Integrated bioinformatics analysis of As, Au, Cd, Pb and Cu heavy metal responsive marker genes through Arabidopsis thaliana GEO datasets

BACKGROUND

Current environmental pollution factors, particularly the distribution and diffusion of heavy metals in soil and water, are a high risk to local environments and humans. Despite striking advances in methods to detect contaminants by a variety of chemical and physical solutions, these methods have inherent limitations such as small dimensions and very low coverage. Therefore, identifying novel contaminant biomarkers are urgently needed.

METHODS

To better track heavy metal contaminations in soil and water, integrated bioinformatics analysis to identify biomarkers of relevant heavy metal, such as As, Cd, Pb and Cu, is a suitable method for long-term and large-scale surveys of such heavy metal pollutants. Subsequently, the accuracy and stability of the results screened were experimentally validated by quantitative PCR experiment.

RESULTS

We obtained 168 differentially expressed genes (DEGs) which contained 59 up-regulated genes and 109 down-regulated genes through comparative bioinformatics analyses. Subsequently, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of these DEGs were performed, respectively. GO analyses found that these DEGs were mainly related to responses to chemicals, responses to stimulus, responses to stress, responses to abiotic stimulus, and so on. KEGG pathway analyses of DEGs were mainly involved in the protein degradation process and other biologic process, such as the phenylpropanoid biosynthesis pathways and nitrogen metabolism. Moreover, we also speculated that nine candidate core biomarker genes (namely, NILR1, PGPS1, WRKY33, BCS1, AR781, CYP81D8, NR1, EAP1 and MYB15) might be tightly correlated with the response or transport of heavy metals. Finally, experimental results displayed that these genes had the same expression trend response to different stresses as mentioned above (Cd, Pb and Cu) and no mentioned above (Zn and Cr).

CONCLUSION

In general, the identified biomarker genes could help us understand the potential molecular mechanisms or signaling pathways responsive to heavy metal stress in plants, and could be applied as marker genes to track heavy metal pollution in soil and water through detecting their expression in plants growing in those environments.

Gene Editing and Systems Biology Tools for Pesticide Bioremediation: A Review

Bioremediation is the degradation potential of microorganisms to dissimilate the complex chemical compounds from the surrounding environment. The genetics and biochemistry of biodegradation processes in datasets opened the way of systems biology. Systemic biology aid the study of interacting parts involved in the system. The significant keys of system biology are biodegradation network, computational biology, and omics approaches. Biodegradation network consists of all the databases and datasets which aid in assisting the degradation and deterioration potential of microorganisms for bioremediation processes. This review deciphers the bio-degradation network, i.e., the databases and datasets (UM-BBD, PAN, PTID, etc.) aiding in assisting the degradation and deterioration potential of microorganisms for bioremediation processes, computational biology and multi omics approaches like metagenomics, genomics, transcriptomics, proteomics, and metabolomics for the efficient functional gene mining and their validation for bioremediation experiments. Besides, the present review also describes the gene editing tools like CRISPR Cas, TALEN, and ZFNs which can possibly make design microbe with functional gene of interest for degradation of particular recalcitrant for improved bioremediation.

Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks

The rapidly expanding number of protein sequences found in public databases can improve our understanding of how protein functions evolve. However, our current knowledge of protein function likely represents a small fraction of the diverse repertoire that exists in nature. Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions through the observation and investigation of the complex sequence-structure-function relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) to identify previously unexplored sequence and function space. We exemplify this approach using the nitroreductase (NTR) superfamily. We demonstrate that SSN investigations can provide a rapid and effective means to classify groups of proteins, therefore exposing experimentally unexplored sequences that may exhibit novel functionality. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes and their associated physiological roles.