Improving protein solubility and activity by introducing small peptide tags designed with machine learning models

SUMO-G5C23-D208G@ZIF-F: A Novel Immobilized Enzyme with Enhanced Stability and Reusability for Organophosphorus Hydrolysis

Organophosphorus hydrolase (OPH) is a highly effective bioscavenger for detoxifying hazardous organophosphorus compounds. However, its practical application is hindered by low yield and poor stability. In this study, we employed Small Ubiquitin-like Modifier (SUMO) fusion expression to enhance the solubility of the OPH mutant G5C23-D208G and, for the first time, immobilized the enzyme on a zeolitic imidazolate framework-F (ZIF-F) carrier to improve its stability. The SUMO-G5C23-D208G fusion protein was successfully expressed in Escherichia coli, resulting in a yield that was 2.4 times higher than that of native OPH and an 11-fold increase in solubility. The purified protein achieved a purity of 95%. The immobilized enzyme, SU-MO-G5C23-D208G@ZIF-F, exhibited a farfalle-shaped structure with a diameter of approximately 3-5 μm. Compared to the free enzyme, the immobilized enzyme maintained high catalytic efficiency (kcat/Km = 8.9 × 104 M-1·s-1) and demonstrated enhanced thermal stability, pH stability, and reusability. This study has significantly improved the yield and stability of OPH, thereby supporting its potential for industrial applications.

Unleashing the potential of microbial biosynthesis of monoterpenes via enzyme and metabolic engineering

Monoterpenes (MTPs) are valuable isoprenoids widely used in cosmetics, food flavorings, pharmaceuticals, etc. Compared to plant extraction and chemical synthesis, microbial biosynthesis offers superior sustainability and efficiency in producing natural MTPs, overcoming the limitations of raw material dependency, environmental impact, and racemic mixtures inherent in these methods. This review comprehensively discusses the development of natural or non-natural biosynthetic pathways for producing regular and irregular MTPs, emphasizing the importance of enzyme and metabolic engineering to optimize monoterpene synthases (MTPSs) in various engineered microbial cell factories (MCFs). The advances in functional expression of MTPS to enhance enzyme activity, substrate channeling of MTPS with critical biosynthesis enzymes, protein engineering of MTPS, targeted localization of MTPS in the subcellular organelle, and other favorable engineering strategies are discussed in detail. Leveraging these technologies, the engineered microbes will achieve the production of the defined product profile with higher titer/yield/productivity and improved industrial adaptability. Furthermore, we highlight the important development direction for optimizing MTPS performance and biosynthetic pathways, ensuring the microbial production of natural MTPs in a more efficient and application-specific manner.

A review on computational models for predicting protein solubility

Protein solubility is a critical factor in the production of recombinant proteins, which are widely used in various industries, including pharmaceuticals, diagnostics, and biotechnology. Predicting protein solubility remains a challenging task due to the complexity of protein structures and the multitude of factors influencing solubility. Recent advances in computational methods, particularly those based on machine learning, have provided powerful tools for predicting protein solubility, thereby reducing the need for extensive experimental trials. This review provides an overview of current computational approaches to predict protein solubility. We discuss the datasets, features, and algorithms employed in these models. The review aims to bridge the gap between computational predictions and experimental validations, fostering the development of more accurate and reliable solubility prediction models that can significantly enhance recombinant protein production.

AlphaFold 2-based stacking model for protein solubility prediction and its transferability on seed storage proteins

Accurate protein solubility prediction is crucial in screening suitable candidates for food application. Existing models often rely only on sequences, overlooking important structural details. In this study, a regression model for protein solubility was developed using both the sequences and predicted structures of 2983 E. coli proteins. The sequence and structural level properties of the proteins were bioinformatically extracted and subjected to multilayer perceptron (MLP). Moreover, residue level features and contact maps were utilized to construct a graph convolutional network (GCN). The out-of-fold predictions of the two models were combined and fed into multiple meta-regressors to create a stacking model. The stacking model with support vector regressor (SVR) achieved R2 of 0.502 and 0.468 on test and external validation datasets, respectively, displaying higher performance compared to existing regression models. Based on the improved performance compared to its based models, the stacking model effectively captured the strength of its base models as well as the significance of the different features used. Furthermore, the model's transferability was indirectly validated on a dataset of seed storage proteins using Osborne definition as well as on a case study using molecular dynamic simulation, showing potential for application beyond microbial proteins to food and agriculture-related ones.

ProSol-multi: Protein solubility prediction via amino acids multi-level correlation and discriminative distribution

Protein solubility prediction is useful for the careful selection of highly effective candidate proteins for drug development. In recombinant proteins synthesis, solubility prediction is valuable for optimizing key protein characteristics, including stability, functionality, and ease of purification. It contains valuable information about potential biomarkers or therapeutic targets and helps in early forecasting of neurodegenerative diseases, cancer, and cardiovascular disorders. Traditional wet-lab experimental protein solubility prediction approaches are error-prone, time-consuming, and costly. Researchers harnessed the competence of Artificial Intelligence approaches for replacing experimental approaches with computational predictors. These predictors inferred the solubility of proteins by analyzing amino acids distributions in raw protein sequences. There is still a lot of room for the development of robust computational predictors because existing predictors remain fail in extracting comprehensive discriminative distribution of amino acids. To more precisely discriminate soluble proteins from insoluble proteins, this paper presents ProSol-Multi predictor that makes use of a novel MLCDE encoder and Random Forest classifier. MLCDE encoder transforms protein sequences into informative statistical vectors by capturing amino acids multi-level correlation and discriminative distribution within raw protein sequences. The performance of proposed encoder is evaluated against 56 existing protein sequence encoding methods on a widely used protein solubility prediction benchmark dataset under two different experimental settings namely intrinsic and extrinsic. Intrinsic evaluation reveals that from all sequence encoders, proposed MLCDE encoder manages to generate non-overlapping clusters of soluble and insoluble classes. In extrinsic evaluation, 10 machine learning classifiers achieve better performance with proposed MLCDE encoder as compared to 56 existing protein sequence encoders. Moreover, across 4 public benchmark datasets, proposed ProSol-Multi predictor outshines 20 existing predictors by an average accuracy of 3%, MCC and AU-ROC of 2%. ProSol-Multi interactive web application is available at https://sds_genetic_analysis.opendfki.de/ProSol-Multi.

ThermoLink: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction

Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostability. Although an increasing number of tools can assist with this task, significant amounts of time and resources are often wasted owing to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we initially collected disulfide bond and protein thermostability data from extensive literature sources. Thereafter, we extracted various sequence- and structure-based features and constructed machine-learning models to predict whether disulfide bonds can improve protein thermostability. Among all models, the neighborhood context model based on the Adaboost-DT algorithm performed the best, yielding "area under the receiver operating characteristic curve" and accuracy scores of 0.773 and 0.714, respectively. Furthermore, we also found AlphaFold2 to exhibit high superiority in predicting disulfide bonds, and to some extent, the coevolutionary relationship between residue pairs potentially guided artificial disulfide bond design. Moreover, several mutants of imine reductase 89 (IR89) with artificially designed thermostable disulfide bonds were experimentally proven to be considerably efficient for substrate catalysis. The SS-bond data have been integrated into an online server, namely, ThermoLink, available at guolab.mpu.edu.mo/thermoLink.

Challenges in Peptide Solubilization - Amyloids Case Study

Peptide science has been a rapidly growing research field because of the enormous potential application of these biocompatible and bioactive molecules. However, many factors limit the widespread use of peptides in medicine, and low solubility is among the most common problems that hamper drug development in the early stages of research. Solubility is a crucial, albeit poorly understood, feature that determines peptide behavior. Several different solubility predictors have been proposed, and many strategies and protocols have been reported to dissolve peptides, but none of them is a one-size-fits-all method for solubilization of even the same peptide. In this review, we look for the reasons behind the difficulties in dissolving peptides, analyze the factors influencing peptide aggregation, conduct a critical analysis of solubilization strategies and protocols available in the literature, and give some tips on how to deal with the so-called difficult sequences. We focus on amyloids, which are particularly difficult to dissolve and handle such as amyloid beta (Aβ), insulin, and phenol-soluble modulins (PSMs).

Immune targeting of filarial glutaredoxin through a multi-epitope peptide-based vaccine: A reverse vaccinology approach

Despite the efforts of global programme to eliminate lymphatic filariasis (GPELF), the threat of lymphatic filariasis (LF) still looms over humanity in terms of long-term disabilities, and morbidities across the globe. In light of this situation, investigators have chosen to focus on the development of immunotherapeutics targeting the physiologically important filarial-specific proteins. Glutaredoxin (16.43 kDa) plays a pivotal role in filarial redox biology, serving as a vital contributor. In the context of the intra-host survival of filarial parasites, this antioxidant helps in mitigating the oxidative stress imposed by the host immune system. Given its significant contribution, the development of a vaccine targeting glutaredoxin holds promise as a new avenue for achieving a filaria-free world. Herein, multi-epitope-based vaccine was designed using advanced immunoinformatics approach. Initially, 4B-cell epitopes and 6 T-cell epitopes (4 MHC I and 2 MHC II) were identified from the 146 amino acid long sequence of glutaredoxin of the human filarid, Wuchereria bancrofti. Subsequent clustering of these epitopes with linker peptides finalized the vaccine structure. To boost TLR-mediated innate immunity, TLR-specific adjuvants were incorporated into the designed vaccine. After that, experimental analyses confirm the designed vaccine, Vac4 as anefficient ligand of human TLR5 to elicit protective innate immunity against filarial glutaredoxin. Immune simulation further demonstrated abundant levels of IgG and IgM as crucial contributors in triggering vaccine-induced adaptive responses in the recipients. Hence, to facilitate the validation of immunogenicity of the designed vaccine, Vac4 was cloned in silico in pET28a(+) expression vector for recombinant production. Taken together, our findings suggest that vaccine-mediated targeting of filarial glutaredoxin could be a future option for intervening LF on a global scale.

Long-term stability of the genome structure of the cyanobacterium, Dolichospermum in a deep German lake

Dolichospermum is a cyanobacterial genus commonly associated with toxic blooms in lakes and brackish water bodies worldwide, and is a long-term resident of Lake Stechlin, northeastern Germany. In recent decades, shifts in the phosphorus loading and phytoplankton species composition have seen increased biomass of Dolichospermum during summer blooms from 1998, peaking around 2005, and declining after 2020. Cyanobacteria are known to rapidly adapt to new environments, facilitated by genome adaptation. To investigate the changes in genomic features that may have occurred in Lake Stechlin Dolichospermum during this time of increased phosphorus loading and higher biomass, whole genome sequence analysis was performed on samples of ten akinetes isolated from ten, 1 cm segments of a sediment core, representing a ∼45-year period from 1970 to 2017. Comparison of these genomes with genomes of extant isolates revealed a clade of Dolichospermum that clustered with the ADA-6 genus complex, with remarkable genome stability, without gene gain or loss events in response to recent environmental changes. The genome characteristics indicate that this species is suited to a deep-chlorophyll maximum, including additional light-harvesting and phosphorus scavenging genes. Population SNP analysis revealed two sub-populations that shifted in dominance as the lake transitioned between oligotrophic and eutrophic conditions. Overall, the results show little change within the population, despite diversity between extant populations from different geographic locations and the in-lake changes in phosphorus concentrations.

Functional small peptides for enhanced protein delivery, solubility, and secretion in microbial biotechnology

In microbial biotechnology, there is a constant demand for functional peptides to give new functionality to engineered proteins to address problems such as direct delivery of functional proteins into bacterial cells, enhanced protein solubility during the expression of recombinant proteins, and efficient protein secretion from bacteria. To tackle these critical issues, we selected three types of functional small peptides: cell-penetrating peptides (CPPs) enable the delivery of diverse cargoes into bacterial cytoplasm for a variety of purposes, protein-solubilizing peptide tags demonstrate remarkable efficiency in solubilizing recombinant proteins without folding interference, and signal peptides play a key role in enabling the secretion of recombinant proteins from bacterial cells. In this review, we introduced these three functional small peptides that offer effective solutions to address emerging problems in microbial biotechnology. Additionally, we summarized various engineering efforts aimed at enhancing the activity and performance of these peptides, thereby providing valuable insights into their potential for further applications.

Organelle-dependent polyprotein designs enable stoichiometric expression of nitrogen fixation components targeted to mitochondria

Introducing nitrogen fixation (nif   ) genes into eukaryotic genomes and targeting Nif components to mitochondria or chloroplasts is a promising strategy for engineering nitrogen-fixing plants. A prerequisite for achieving nitrogen fixation in crops is stable and stoichiometric expression of each component in organelles. Previously, we designed a polyprotein-based nitrogenase system depending on Tobacco Etch Virus protease (TEVp) to release functional Nif components from five polyproteins. Although this system satisfies the demand for specific expression ratios of Nif components in Escherichia coli, we encountered issues with TEVp cleavage of polyproteins targeted to yeast mitochondria. To overcome this obstacle, a version of the Nif polyprotein system was constructed by replacing TEVp cleavage sites with minimal peptide sequences, identified by knowledge-based engineering, that are susceptible to cleavage by the endogenous mitochondrial-processing peptidase. This replacement not only further reduces the number of genes required, but also prevents potential precleavage of polyproteins outside the target organelle. This version of the polyprotein-based nitrogenase system achieved levels of nitrogenase activity in E. coli, comparable to those observed with the TEVp-based polyprotein nitrogenase system. When applied to yeast mitochondria, stable and balanced expression of Nif components was realized. This strategy has potential advantages, not only for transferring nitrogen fixation to eukaryotic cells, but also for the engineering of other metabolic pathways that require mitochondrial compartmentalization.

The Versatile Biocatalyst of Cytochrome P450 CYP102A1: Structure, Function, and Engineering

Wild-type cytochrome P450 CYP102A1 from Bacillus megaterium is a highly efficient monooxygenase for the oxidation of long-chain fatty acids. The unique features of CYP102A1, such as high catalytic activity, expression yield, regio- and stereoselectivity, and self-sufficiency in electron transfer as a fusion protein, afford the requirements for an ideal biocatalyst. In the past three decades, remarkable progress has been made in engineering CYP102A1 for applications in drug discovery, biosynthesis, and biotechnology. The repertoire of engineered CYP102A1 variants has grown tremendously, whereas the substrate repertoire is avalanched to encompass alkanes, alkenes, aromatics, organic solvents, pharmaceuticals, drugs, and many more. In this article, we highlight the major advances in the past five years in our understanding of the structure and function of CYP102A1 and the methodologies used to engineer CYP102A1 for novel applications. The objective is to provide a succinct review of the latest developments with reference to the body of CYP102A1-related literature.

Folding of heterologous proteins in bacterial cell factories: Cellular mechanisms and engineering strategies

The expression of correctly folded and functional heterologous proteins is important in many biotechnological production processes, whether it is enzymes, biopharmaceuticals or biosynthetic pathways for production of sustainable chemicals. For industrial applications, bacterial platform organisms, such as E. coli, are still broadly used due to the availability of tools and proven suitability at industrial scale. However, expression of heterologous proteins in these organisms can result in protein aggregation and low amounts of functional protein. This review provides an overview of the cellular mechanisms that can influence protein folding and expression, such as co-translational folding and assembly, chaperone binding, as well as protein quality control, across different model organisms. The knowledge of these mechanisms is then linked to different experimental methods that have been applied in order to improve functional heterologous protein folding, such as codon optimization, fusion tagging, chaperone co-production, as well as strain and protein engineering strategies.

Protein expression/secretion boost by a novel unique 21-mer cis-regulatory motif (Exin21) via mRNA stabilization

Boosting protein production is invaluable in both industrial and academic applications. We discovered a novel expression-increasing 21-mer cis-regulatory motif (Exin21) that inserts between SARS-CoV-2 envelope (E) protein-encoding sequence and luciferase reporter gene. This unique Exin21 (CAACCGCGGTTCGCGGCCGCT), encoding a heptapeptide (QPRFAAA, designated as Qα), significantly (34-fold on average) boosted E production. Both synonymous and nonsynonymous mutations within Exin21 diminished its boosting capability, indicating the exclusive composition and order of 21 nucleotides. Further investigations demonstrated that Exin21/Qα addition could boost the production of multiple SARS-CoV-2 structural proteins (S, M, and N) and accessory proteins (NSP2, NSP16, and ORF3), and host cellular gene products such as IL-2, IFN-γ, ACE2, and NIBP. Exin21/Qα enhanced the packaging yield of S-containing pseudoviruses and standard lentivirus. Exin21/Qα addition on the heavy and light chains of human anti-SARS-CoV monoclonal antibody robustly increased antibody production. The extent of such boosting varied with protein types, cellular density/function, transfection efficiency, reporter dosage, secretion signaling, and 2A-mediated auto-cleaving efficiency. Mechanistically, Exin21/Qα increased mRNA synthesis/stability, and facilitated protein expression and secretion. These findings indicate that Exin21/Qα has the potential to be used as a universal booster for protein production, which is of importance for biomedicine research and development of bioproducts, drugs, and vaccines.

Directed Evolution of Soluble α-1,2-Fucosyltransferase Using Kanamycin Resistance Protein as a Phenotypic Reporter for Efficient Production of 2'-Fucosyllactose

2'-Fucosyllactose (2'-FL), the most abundant fucosylated oligosaccharide in human milk, has multiple beneficial effects on human health. However, its biosynthesis by metabolically engineered Escherichia coli is often hampered owing to the insolubility and instability of α-1,2-fucosyltransferase (the rate-limiting enzyme). In this study, we aimed to enhance 2'-FL production by increasing the expression of soluble α-1,2-fucosyltransferase from Helicobacter pylori (FucT2). Because structural information regarding FucT2 has not been unveiled, we decided to improve the expression of soluble FucT2 in E. coli via directed evolution using a protein solubility biosensor that links protein solubility to antimicrobial resistance. For such a system to be viable, the activity of kanamycin resistance protein (Kan^R) should be dependent on FucT2 solubility. Kan^R was fused to the C-terminus of mutant libraries of FucT2, which were generated using a combination of error-prone PCR and DNA shuffling. Notably, one round of the directed evolution process, which consisted of mutant library generation and selection based on kanamycin resistance, resulted in a significant increase in the expression level of soluble FucT2. As a result, a batch fermentation with the ΔL M15 pBCGW strain, expressing the FucT2 mutant (F#1-5) isolated from the first round of the directed evolution process, resulted in the production of 0.31 g/l 2'-FL with a yield of 0.22 g 2'-FL/g lactose, showing 1.72- and 1.51-fold increase in the titer and yield, respectively, compared to those of the control strain. The simple and powerful method developed in this study could be applied to enhance the solubility of other unstable enzymes.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Protein Function Analysis through Machine Learning

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein-ligand binding, including allosteric effects, protein-protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

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.

Maximizing the recovery of the native p28 bacterial peptide with improved activity and maintained solubility and stability in Escherichia coli BL21 (DE3)

p28 is a natural bacterial product, which recently has attracted much attention as an efficient cell penetrating peptide (CPP) and a promising anticancer agent. Considering the interesting biological qualities of p28, maximizing its expression appears to be a prominent priority. The optimization of such bioprocesses might be facilitated by utilizing statistical approaches such as Design of Experiment (DoE). In this study, we aimed to maximize the expression of "biologically active" p28 in Escherichia coli BL21 (DE3) host by harnessing statistical tools and experimental methods. Using Minitab, Plackett-Burman and Box-Behnken Response Surface Methodology (RSM) designs were generated to optimize the conditions for the expression of p28. Each condition was experimentally investigated by assessing the biological activity of the purified p28 in the MCF-7 breast cancer cell line. Seven independent variables were investigated, and three of them including ethanol concentration, OD600 of the culture at the time of induction, and the post-induction temperature were demonstrated to significantly affect the p28 expression in E. coli. The cytotoxicity, penetration efficiency, and total process time were measured as dependent variables. The optimized expression conditions were validated experimentally, and the final products were investigated in terms of expression yield, solubility, and stability in vitro. Following the optimization, an 8-fold increase of the concentration of p28 expression was observed. In this study, we suggest an optimized combination of effective factors to produce soluble p28 in the E. coli host, a protocol that results in the production of a significantly high amount of the biologically active peptide with retained solubility and stability.

Fusion Tag Design Influences Soluble Recombinant Protein Production in Escherichia coli

Fusion protein technologies to facilitate soluble expression, detection, or subsequent affinity purification in Escherichia coli are widely used but may also be associated with negative consequences. Although commonly employed solubility tags have a positive influence on titers, their large molecular mass inherently results in stochiometric losses of product yield. Furthermore, the introduction of affinity tags, especially the polyhistidine tag, has been associated with undesirable changes in expression levels. Fusion tags are also known to influence the functionality of the protein of interest due to conformational changes. Therefore, particularly for biopharmaceutical applications, the removal of the fusion tag is a requirement to ensure the safety and efficacy of the therapeutic protein. The design of suitable fusion tags enabling the efficient manufacturing of the recombinant protein remains a challenge. Here, we evaluated several N-terminal fusion tag combinations and their influence on product titer and cell growth to find an ideal design for a generic fusion tag. For enhancing soluble expression, a negatively charged peptide tag derived from the T7 bacteriophage was combined with affinity tags and a caspase-2 cleavage site applicable for CASPase-based fusiON (CASPON) platform technology. The effects of each combinatorial tag element were investigated in an integrated manner using human fibroblast growth factor 2 as a model protein in fed-batch lab-scale bioreactor cultivations. To confirm the generic applicability for manufacturing, seven additional pharmaceutically relevant proteins were produced using the best performing tag of this study, named CASPON-tag, and tag removal was demonstrated.

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.

Charged Polypeptide Tail Boosts the Salt Resistance of Enzyme-Containing Complex Coacervate Micelles

Encapsulation of proteins can have advantages for their protection, stability, and delivery purposes. One of the options to encapsulate proteins is to incorporate them in complex coacervate core micelles (C3Ms). This can easily be achieved by mixing aqueous solutions of the protein and an oppositely charged neutral-hydrophilic diblock copolymer. However, protein-containing C3Ms often suffer from salt-inducible disintegration due to the low charge density of proteins. The aim of this study is to improve the salt stability of protein-containing C3Ms by increasing the net charge of the protein by tagging it with a charged polypeptide. As a model protein, we used CotA laccase and generated variants with 10, 20, 30, and 40 glutamic acids attached at the C-terminus of CotA using genetic engineering. Micelles were obtained by mixing the five CotA variants with poly(N-methyl-2-vinyl-pyridinium)-block-poly(ethylene oxide) (PM2VP128-b-PEO477) at pH 10.8. Hydrodynamic radii of the micelles of approximately 31, 27, and 23 nm for native CotA, CotA-E20, and CotA-E40, respectively, were determined using dynamic light scattering (DLS) and fluorescence correlation spectroscopy (FCS). The encapsulation efficiency was not affected using enzymes with a polyglutamic acid tail but resulted in more micelles with a smaller number of enzyme molecules per micelle. Furthermore, it was shown that the addition of a polyglutamic acid tail to CotA indeed resulted in improved salt stability of enzyme-containing C3Ms. Interestingly, the polyglutamic acid CotA variants showed an enhanced enzyme activity. This study demonstrates that increasing the net charge of enzymes through genetic engineering is a promising strategy to improve the practical applicability of C3Ms as enzyme delivery systems.

Direct capture and selective elution of a secreted polyglutamate-tagged nanobody using bare magnetic nanoparticles

BACKGROUND

The secretion and direct capture of proteins from the extracellular medium is a promising approach for purification, thus enabling integrated bioprocesses.

MAJOR RESULTS

We demonstrate the secretion of a nanobody (VHH) to the extracellular medium (EM) and its direct capture by bare, non-functionalized magnetic nanoparticles (MNPs). An ompA signal peptide for periplasmic localization, a polyglutamate-tag (E₈ ) for selective MNP binding, and a factor Xa protease cleavage site were fused N-terminally to the nanobody. The extracellular production of the E₈ -VHH (36 mg L^-1 ) was enabled using a growth-decoupled Escherichia coli-based expression system. The direct binding of E₈ -VHH to the bare magnetic nanoparticles was possible and could be drastically improved up to a yield of 88% by adding polyethylene glycol (PEG). The selectivity of the polyglutamate-tag enabled a selective elution of the E₈ -VHH from the bare MNPs while raising the concentration factor (5x) and purification factor (4x) significantly.

CONCLUSION

Our studies clearly show that the unique combination of a growth-decoupled E. coli secretion system, the polyglutamate affinity tag, non-functionalized magnetic nanoparticles, and affinity magnetic precipitation is an innovative and novel way to capture and concentrate nanobodies. This article is protected by copyright. All rights reserved.

Solution NMR and racemic crystallography provide insights into a novel structural class of cyclic plant peptides

Head-to-tail cyclic and disulfide-rich peptides are natural products with applications in drug design. Among these are the PawS-Derived Peptides (PDPs) produced in seeds of the daisy plant family. PDP-23 is a unique member of this class in that it is twice the typical size and adopts two β-hairpins separated by a hinge region. The β-hairpins, both stabilised by a single disulfide bond, fold together into a V-shaped tertiary structure creating a hydrophobic core. In water two PDP-23 molecules merge their hydrophobic cores to form a square prism quaternary structure. Here, we synthesised PDP-23 and its enantiomer comprising d-amino acids and achiral glycine, which allowed us to confirm these solution NMR structural data by racemic crystallography. Furthermore, we discovered the related PDP-24. NMR analysis showed that PDP-24 does not form a dimeric structure and it has poor water solubility, but in less polar solvents adopts near identical secondary and tertiary structure to PDP-23. The natural role of these peptides in plants remains enigmatic, as we did not observe any antimicrobial or insecticidal activity. However, the plasticity of these larger PDPs and their ability to change structure under different conditions make them appealing peptide drug scaffolds.

Larger members of the PawS-Derived family of cyclic plant peptides form complex structures. The graphical abstract shows the racemic crystal structure of the homodimeric PDP-23 as well as the solution NMR structure of PDP-24.

Trehalose treatment alters carbon partitioning and reduces the accumulation of individual metabolites but does not affect salt tolerance in the green microalga Dunaliella bardawil

The effects of trehalose (Tre), a non-reducing disaccharide, on metabolic changes, antioxidant status, and salt tolerance in Dunaliella bardawil cells were investigated. Algal suspensions containing 1, 2, and 3 M NaCl were treated with 5 mM Tre. While the content of pigments, reducing sugars, proteins, glycerol, and ascorbate pool accumulated with increasing salinity, the content of non-reducing sugars, starch, amino acids, proline, hydrogen peroxide, and lipid peroxidation level decreased significantly. Tre-treated cells showed a decrease in pigments content, reducing sugars, starch, proteins, amino acids, proline, glycerol, and the activity of non-specific peroxidase and polyphenol oxidase, but an increase in non-reducing sugars, oxidized ascorbate, and ascorbate peroxidase activity occurred unchanged in the ascorbate pool. However, the density and fresh weight of the cells remained statistically unchanged in all Tre-treated and untreated cultures. These results suggest that D. bardawil cells potentially tolerate different salt levels by accumulating metabolites, whereas Tre treatment changes carbon partitioning and significantly reduces beneficial metabolites without altering salt tolerance. Therefore, the regulation of carbon partitioning rather than the amount of assimilated carbon may play an important role in inducing salinity tolerance of D. bardawil. However, Tre is not able to enhance the salt tolerance of halotolerants and is even economically damaging due to the reduction of unique metabolites such as glycerol and β-carotene.

Enhanced Production of a Thermostable Carbonic Anhydrase in Escherichia coli by Using a Modified NEXT Tag

Carbonic anhydrase (CA) is an ultrafast enzyme that catalyzes the reversible conversion of carbon dioxide (CO2) to bicarbonate. CA is considered to be a green catalyst for enzyme-based CO2 capture and utilization. In particular, the CA of Thermovibrio ammonificans (taCA) has attracted increasing attention as a highly stable enzyme. However, the poor solubility and the low expression level in Escherichia coli have hampered further utilization of taCA. In a recent study, these limitations were partly resolved by using a small solubility-enhancing fusion tag named NEXT, which originates from the N-terminal extension of Hydrogenovibrio marinus CA. In this study, the NEXT tag was engineered by adding small peptides to the N terminus to further increase the production yield of NEXT-tagged taCA. The addition of ng3 peptide (His-Gly-Asn) originating from the N-terminal sequence of Neisseria gonorrhoeae CA improved the expression of NEXT-taCA, while the previously developed translation-enhancing element (TEE) and Ser-Lys-Ile-Lys (SKIK) tag were not effective. The expression test with all 16 codon combinations for the ng3 sequence revealed that the change in translation initiation rate brought about by the change in nucleotide sequence was not the primary determinant for the change in expression level. The modified ng3-NEXT tag may be applied to increase the production yields of various recombinant proteins.

Introduction of novel putative immunogenic targets against Proteus mirabilis using a reverse vaccinology approach

Multi-drug resistance of Proteus mirabilis, a frequent cause of catheter-associated urinary tract infections, renders ineffective treatment. Therefore, new advanced strategies are needed to overcome it. In the meantime, vaccination may be the most effective and promising method. In this study antigenicity, allergenicity, subcellular localization, human homology, B-cell epitopes and MHC-II binding sites, conserved domains and protein-protein interactions were predicted using different reverse vaccinology methods and bioinformatics databases to find new putative immunogenic targets against P. mirabilis. Finally, 5 putative immunogenic targets against P. mirabilis were identified. Considering all criteria, QKQ94350.1 (Cell envelope opacity-associated protein A), QKQ94681.1 (Porin), QKQ95001.1 (TonB-dependent hemoglobin/ transferrin/ lactoferrin family receptor), QKQ95221.1 (AsmA) and QKQ94335.1 (N-acetylmuramoyl-L-alanine amidase) are excellent putative immunogenic targets. Finally, a multi-epitope vaccine was designed using the conserved linear epitopes of two OMPs (QKQ94681.1 and QKQ95001.1) and N-acetylmuramoyl-L-alanine amidase (QKQ94335.1), which have promising properties for immunization. These findings can simplify the development of efficient vaccines against P. mirabilis.

Computational and synthetic biology approaches for the biosynthesis of antiviral and anticancer terpenoids from Bacillus subtilis

Recent advancements in medicinal research have identified several antiviral and anticancer terpenoids that are usually deployed as a source of flavor, fragrances and pharmaceuticals. Under the current COVID-19 pandemic conditions, natural therapeutics with least side effects are the need of the hour to save the patients, especially, which are pre-affected with other medical complications. Although, plants are the major sources of terpenoids; however, for the environmental concerns, the global interest has shifted to the biocatalytic production of molecules from microbial sources. The gram-positive bacterium Bacillus subtilis is a suitable host in this regard due to its GRAS (generally regarded as safe) status, ease in genetic manipulations and wide industrial acceptability. The B. subtilis synthesizes its terpenoid molecules from 1-deoxy-d-xylulose-5-phosphate (DXP) pathway, a common route in almost all microbial strains. Here, we summarize the computational and synthetic biology approaches to improve the production of terpenoid-based therapeutics from B. subtilis by utilizing DXP pathway. We focus on the in-silico approaches for screening the functionally improved enzyme-variants of the two crucial enzymes namely, the DXP synthase (DXS) and farnesyl pyrophosphate synthase (FPPS). The approaches for engineering the active sites are subsequently explained. It will be helpful to construct the functionally improved enzymes for the high-yield production of terpenoid-based anticancer and antiviral metabolites, which would help to reduce the cost and improve the availability of such therapeutics for the humankind.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

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

Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution

Protein solubility is based on the compatibility of the specific protein surface with the polar aquatic environment. The exposure of polar residues to the protein surface promotes the protein’s solubility in the polar environment. The aquatic environment also influences the folding process by favoring the centralization of hydrophobic residues with the simultaneous exposure to polar residues. The degree of compatibility of the residue distribution, with the model of the concentration of hydrophobic residues in the center of the molecule, with the simultaneous exposure of polar residues is determined by the sequence of amino acids in the chain. The fuzzy oil drop model enables the quantification of the degree of compatibility of the hydrophobicity distribution observed in the protein to a form fully consistent with the Gaussian 3D function, which expresses an idealized distribution that meets the preferences of the polar water environment. The varied degrees of compatibility of the distribution observed with the idealized one allow the prediction of preferences to interactions with molecules of different polarity, including water molecules in particular. This paper analyzes a set of proteins with different levels of hydrophobicity distribution in the context of the solubility of a given protein and the possibility of complex formation.

Emerging Emulsifiers: Conceptual Basis for the Identification and Rational Design of Peptides with Surface Activity

Emulsifiers are gradually evolving from synthetic molecules of petrochemical origin to biomolecules mainly due to health and environmental concerns. Peptides represent a type of biomolecules whose molecular structure is composed of a sequence of amino acids that can be easily tailored to have specific properties. However, the lack of knowledge about emulsifier behavior, structure-performance relationships, and the implementation of different design routes have limited the application of these peptides. Some computational and experimental approaches have tried to close this knowledge gap, but restrictions in understanding the fundamental phenomena and the limited property data availability have made the performance prediction for emulsifier peptides an area of intensive research. This study provides the concepts necessary to understand the emulsifying behavior of peptides. Additionally, a straightforward description is given of how the molecular structure and conditions of the system directly impact the peptides' ability to stabilize emulsion droplets. Moreover, the routes to design and discover novel peptides with interfacial and emulsifying activity are also discussed, along with the strategies to address some of their major pitfalls and challenges. Finally, this contribution reviews methodologies to build and use data sets containing standard properties of emulsifying peptides by looking at successful applications in different fields.

Solubility-Weighted Index: fast and accurate prediction of protein solubility

Motivation

Recombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified.

Results

We have discovered that global structural flexibility, which can be modeled by normalized B-factors, accurately predicts the solubility of 12 216 recombinant proteins expressed in Escherichia coli. We have optimized these B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the ‘Solubility-Weighted Index’ (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed ‘SoDoPE’ (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximizing both protein expression and solubility.

Availability and implementation

The SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper_2020.

Supplementary information

Supplementary data are available at Bioinformatics online.

Increasing metabolic pathway flux by using machine learning models

Machine learning is transforming many industries through self-improving models that are fueled by big data and high computing power. The field of metabolic engineering, which uses cellular biochemical network to manufacture useful small molecules, has also witnessed the first wave of machine learning applications in the past five years, covering reaction route design, enzyme selection, pathway engineering and process optimization. This review focuses on pathway engineering, and uses a few recent studies to illustrate (1) how machine learning models can be useful in overcoming an evident rate-limiting step, and (2) how the models may be used to exhaustively search - or guide optimization algorithms to search - a large design space when the cellular regulation of the reaction network is more convoluted.