Improvement of Heterologous Soluble Expression of L-amino Acid Oxidase Using Logistic Regression

Successful implementation of enzymes in practical application hinges on the development of efficient mass production techniques. However, in a heterologous expression system, the protein is often unable to fold correctly and, thus, forms inclusion bodies, resulting in the loss of its original activity. In this study, we present a new and more accurate model for predicting amino acids associated with an increased L-amino acid oxidase (LAO) solubility. Expressing LAO from Rhizoctonia solani in Escherichia coli and combining random mutagenesis and statistical logistic regression, we modified 108 amino acid residues by substituting hydrophobic amino acids with serine and hydrophilic amino acids with alanine. Our results indicated that specific mutations in Euclidean distance, glycine, methionine, and secondary structure increased LAO expression. Furthermore, repeated mutations were performed for LAO based on logistic regression models. The mutated LAO displayed a significantly increased solubility, with the 6-point and 58-point mutants showing a 2.64- and 4.22-fold increase, respectively, compared with WT-LAO. Ultimately, using recombinant LAO in the biotransformation of α-keto acids indicates its great potential as a biocatalyst in industrial production.

Characterization of Yeast Protein Hydrolysate for Potential Application as a Feed Additive

Yeast protein can be a nutritionally suitable auxiliary protein source in livestock food. The breakdown of proteins and thereby generating high-quality peptide, typically provides nutritional benefits. Enzyme hydrolysis has been effectively uesed to generate peptides; however, studies on the potential applications of different types of enzymes to produce yeast protein hydrolysates remain limited. This study investigated the effects of endo- (alcalase and neutrase) and exotype (flavourzyme and prozyme 2000P) enzyme treatments on yeast protein. Endotype enzymes facilitate a higher hydrolysis efficiency in yeast proteins than exotype enzymes. The highest degree of hydrolysis was observed for the protein treated with neutrase, which was followed by alcalase, prozyme 2000P, and flavourzyme. Furthermore, endotype enzyme treated proteins exhibited higher solubility than their exotype counterparts. Notably, the more uniform particle size distribution was observed in endotype treated yeast protein. Moreover, compared with the original yeast protein, the enzymatic protein hydrolysates possessed a higher content of β-sheets structures, indicating their higher structural stability. Regardless of enzyme type, enzyme treated protein possessed a higher total free amino acid content including essential amino acids. Therefore, this study provides significant insights into the production of protein hydrolysates as an alternative protein material.

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.

Targeting Efficient Features of Urate Oxidase to Increase Its Solubility

With the demand for mass production of protein drugs, solubility has become a serious issue. Extrinsic and intrinsic factors both affect this property. A homotetrameric cofactor-free urate oxidase (UOX) is not sufficiently soluble. To engineer UOX for optimum solubility, it is important to identify the most effective factor that influences solubility. The most effective feature to target for protein engineering was determined by measuring various solubility-related factors of UOX. A large library of homologous sequences was obtained from the databases. The data was reduced to six enzymes from different organisms. On the basis of various sequence- and structure-derived elements, the most and the least soluble enzymes were defined. To determine the best protein engineering target for modification, features of the most and least soluble enzymes were compared. Metabacillus fastidiosus UOX was the most soluble enzyme, while Agrobacterium globiformis UOX was the least soluble. According to the comparison-constant method, positive surface patches caused by arginine residue distribution are appropriate targets for modification. Two Arg to Ala mutations were introduced to the least soluble enzyme to test this hypothesis. These mutations significantly enhanced the mutant's solubility. While different algorithms produced conflicting results, it was difficult to determine which proteins were most and least soluble. Solubility prediction requires multiple algorithms based on these controversies. Protein surfaces should be investigated regionally rather than globally, and both sequence and structural data should be considered. Several other biotechnological products could be engineered using the data reduction and comparison-constant methods used in this study.

Optimization of Water Lentil (Duckweed) Leaf Protein Purification: Identification, Structure, and Foaming Properties

Water lentil, commonly known as duckweed, is an aquatic plant with great agronomic potential, as it can double its biomass in less than 24 h and contains up to 45% leaf proteins on a dry matter basis. However, extracting proteins from leaves is an arduous process due to the complexity of the matrix, which limits their uses in the food industry. In this study, water lentil protein extraction by solubilization was maximized using response surface methodology. By heating at 80 °C at pH 11 with a water lentil powder concentration of 2% or 4% for 2 h, up to 77.8% of total proteins were solubilized. Then, by precipitating the solubilized proteins at pH 4, a protein purity of 57.6% combined with a total protein yield of 60.0% was achieved. To the best of our knowledge, this is the highest leaf protein extraction yield reported in the literature with such protein purity. Proteomics analyses showed that the protein concentrate was composed of around 85.0% RubisCO, and protein structure analyses using ATR-FTIR and DSC were linked to a high protein solubility in water at pH 7. Moreover, a 1.5% protein solution of the protein concentrate at pH 7 showed excellent foaming properties compared to a 10.3% protein egg white solution. It had a superior foaming capacity (194% vs. 122%, respectively) for the same foaming stability after 60 min, which confirms water lentil proteins' potential for human nutrition and food formulation.

The Use of In Silico Methods to Identify and Assess Antigenic Regions Suitable for the Development of Peptide-based Pan-viral Vaccines

The constant evolution of pathogenic viral variants and the emergence of new viruses have reinforced the need for broad-spectrum vaccines to combat such threats. The spread of new viral variants leading to epidemic and pandemic infection can be effectively contained, if broad-spectrum vaccines effective against the newer viral variants are readily available. The development of broad-spectrum, pan-neutralising antibodies against viruses which, in general terms, are very antigenically different - such as HIV, influenza virus and paramyxoviruses - has been reported in the literature. The amino acid sequences used to generate a range of approved recombinant anti-viral vaccines were analysed by using in silico methods, with the aim of identifying highly antigenic peptide regions that may be suitable for the development of broad-spectrum peptide-based anti-viral vaccines. This was achieved through the use of open-source data, an algorithm-driven probability matrix, and published in silico prediction tools (SVMTriP, IEDB-AR, VaxiJen 2.0, AllergenFP v. 1.0, AllerTOP v. 2.0, ToxinPred and ProtParam) to evaluate antigenicity, MHC-I and MHC-II binding potential, immunogenicity, allergenicity, toxicity and physicochemical properties. We report a pan-antigenic peptide region with strong affinity for MHC-I and MHC-II, and good immunogenic potential. According to the output from the relevant in silico tools, the peptide was predicted to be non-toxic, non-allergic and to possess the desired physicochemical properties for potentially successful vaccine production. With further investigation and optimisation, this peptide could be considered for use in the development of a broad-spectrum anti-viral vaccine that may protect against emerging new viruses. Our approach of using in silico methods to identify candidate antigenic peptides with the desired physicochemical properties could potentially circumvent the use of some animal studies for peptide vaccine candidate evaluation.

Factors involved in heterologous expression of proteins in E. coli host

The production of recombinant proteins is one of the most significant achievements of biotechnology in the last century. These proteins are produced in the eukaryotic or prokaryotic heterologous hosts. By increasing the omics data especially related to different heterologous hosts as well as the presence of new amenable genetic engineering tools, we can artificially engineer heterologous hosts to produce recombinant proteins in sufficient quantities. Numerous recombinant proteins have been produced and applied in various industries, and the global recombinant proteins market size is expected to be cast to reach USD 2.4 billion by 2027. Therefore, identifying the weakness and strengths of heterologous hosts is critical to optimize the large-scale biosynthesis of recombinant proteins. E. coli is one of the popular hosts to produce recombinant proteins. Scientists reported some bottlenecks in this host, and due to the increasing demand for the production of recombinant proteins, there is an urgent need to improve this host. In this review, we first provide general information about the E. coli host and compare it with other hosts. In the next step, we describe the factors involved in the expression of the recombinant proteins in E. coli. Successful expression of recombinant proteins in E. coli requires a complete elucidation of these factors. Here, the characteristics of each factor will be fully described, and this information can help to improve the heterologous expression of recombinant proteins in E. coli.

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism

Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https://github.com/saizhou1/DeepGpgs.

Differences in levels of E. coli contamination of point of use drinking water in Bangladesh

This study aimed to quantify the inequalities and identify the associated factors of the UN sustainable development goal (SDG) targets in relation to safe drinking water. The concentration of the gut bacterium Escherichia coli in drinking water at the point of use (POU) and other information were extracted from the latest wave of the nationally representative Bangladesh Multiple Indicator Cluster Survey (MICS 2019). Bivariate and multivariable multinomial logistic regression models were used to identify potential predictors of contamination, whereas, classification trees were used to determine specific combinations of background characteristics with significantly higher rates of contamination. A higher risk of contamination from drinking water was observed for households categorized as middle or low wealth who collected water from sources with higher concentrations of E. coli. Treatment of drinking water significantly reduced the risk of higher levels of contamination, whereas owning a pet was significantly associated with recontamination. Regional differences in the concentrations of E. coli present in drinking water were also observed. Interventions in relation to water sources should emphasize reducing the level of E. coli contamination. Our results may help in developing effective policies for reducing diarrheal diseases by reducing water contamination risks.

DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks

Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein's function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharmaceutical) applications. Experimental approaches to predict protein solubility are costly, time-consuming, and frequently offer only low success rates. To reduce cost and expedite the development of therapeutic and industrially relevant proteins, a highly accurate computational tool for predicting protein solubility from protein sequence is sought. While a number of in silico prediction tools exist, they suffer from relatively low prediction accuracy, bias toward the soluble proteins, and limited applicability for various classes of proteins. In this study, we developed a novel deep learning sequence-based solubility predictor, DSResSol, that takes advantage of the integration of squeeze excitation residual networks with dilated convolutional neural networks and outperforms all existing protein solubility prediction models. This model captures the frequently occurring amino acid k-mers and their local and global interactions and highlights the importance of identifying long-range interaction information between amino acid k-mers to achieve improved accuracy, using only protein sequence as input. DSResSol outperforms all available sequence-based solubility predictors by at least 5% in terms of accuracy when evaluated by two different independent test sets. Compared to existing predictors, DSResSol not only reduces prediction bias for insoluble proteins but also predicts soluble proteins within the test sets with an accuracy that is at least 13% higher than existing models. We derive the key amino acids, dipeptides, and tripeptides contributing to protein solubility, identifying glutamic acid and serine as critical amino acids for protein solubility prediction. Overall, DSResSol can be used for the fast, reliable, and inexpensive prediction of a protein's solubility to guide experimental design.

Recombinant expression of insoluble enzymes in Escherichia coli: a systematic review of experimental design and its manufacturing implications

Recombinant enzyme expression in Escherichia coli is one of the most popular methods to produce bulk concentrations of protein product. However, this method is often limited by the inadvertent formation of inclusion bodies. Our analysis systematically reviews literature from 2010 to 2021 and details the methods and strategies researchers have utilized for expression of difficult to express (DtE), industrially relevant recombinant enzymes in E. coli expression strains. Our review identifies an absence of a coherent strategy with disparate practices being used to promote solubility. We discuss the potential to approach recombinant expression systematically, with the aid of modern bioinformatics, modelling, and ‘omics’ based systems-level analysis techniques to provide a structured, holistic approach. Our analysis also identifies potential gaps in the methods used to report metadata in publications and the impact on the reproducibility and growth of the research in this field.

nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning

Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identification of Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and expensive when compared with computational approaches. To date, several predictors for Kcr site prediction have been developed, most of which are capable of predicting crotonylation sites on either histones alone or mixed histone and nonhistone proteins together. These methods exhibit high diversity in their algorithms, encoding schemes, feature selection techniques and performance assessment strategies. However, none of them were designed for predicting Kcr sites on nonhistone proteins. Therefore, it is desirable to develop an effective predictor for identifying Kcr sites from the large amount of nonhistone sequence data. For this purpose, we first provide a comprehensive review on six methods for predicting crotonylation sites. Second, we develop a novel deep learning-based computational framework termed as CNNrgb for Kcr site prediction on nonhistone proteins by integrating different types of features. We benchmark its performance against multiple commonly used machine learning classifiers (including random forest, logitboost, naïve Bayes and logistic regression) by performing both 10-fold cross-validation and independent test. The results show that the proposed CNNrgb framework achieves the best performance with high computational efficiency on large datasets. Moreover, to facilitate users' efforts to investigate Kcr sites on human nonhistone proteins, we implement an online server called nhKcr and compare it with other existing tools to illustrate the utility and robustness of our method. The nhKcr web server and all the datasets utilized in this study are freely accessible at http://nhKcr.erc.monash.edu/.

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.

In silico identification of epitope-based vaccine candidates against HTLV-1

Human T cell leukemia virus type-1 (HTLV-1) is the cause of adult T cell leukemia/lymphoma (ATL), uveitis, and certain pulmonary diseases. In recent decades, many scientists have proposed the development of different treatment and prevention strategies to combat HTLV-1 infection. In this study, we used bioinformatics tools to predict peptide and protein vaccine candidates against HTLV-1 that can potentially induce antibody production and both CD4+ and CD8+ T cell immune responses. Five critical proteins, viz., Hbz, Tax, Pol, Gag, and Env, were analyzed for predicting immunogenic T and B cell epitopes and subsequently evaluated using bioinformatics tools. Based on the predictions, the most antigenic epitopes were selected, and their interaction with immune receptors was investigated. We also designed a protein vaccine candidate with an eight-epitopes-rich domain, including overlapping epitopes detected on both B and T cells. Then, the interaction of the epitope and the designed protein with immune receptors was validated in an in silico docking study. The docking analysis showed that the O2 epitope and D8 protein interact strongly with immune receptors, especially the HLA-A*02:01 receptor. The stability of the interactions was investigated by molecular dynamics (MD) for 100 ns. The root mean square deviation, radius of gyration, hydrogen bonds, and solvent-accessible surface area were calculated for the 100 ns trajectory period. MD studies demonstrated that the O2-HLA-A*02:01 and D8-HLA-A*02:01 complexes were stable during the simulation. Analysis of in silico results showed that the peptide and the designed protein could elicit humoral and cell-mediated immune responses.Communicated by Ramaswamy H. Sarma.

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.

Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2

The novel Corona Virus Disease 2019 (COVID-19) pandemic has set the fatality rates ablaze across the world. So, to combat this disease, we have designed a multi-epitope vaccine from various proteins of Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) with an immuno-informatics approach, validated in silico to be stable, non-allergic and antigenic. Cytotoxic T-cell, helper T-cell, and B-cell epitopes were computationally predicted from six conserved protein sequences among four viral strains isolated across the world. The T-cell epitopes, overlapping with the B-cell epitopes, were included in the vaccine construct to assure the humoral and cell-mediated immune response. The beta-subunit of cholera toxin was added as an adjuvant at the N-terminal of the construct to increase immunogenicity. Interferon-gamma inducing epitopes were even predicted in the vaccine. Molecular docking and binding energetics studies revealed strong interactions of the vaccine with immune-stimulatory toll-like receptors (TLR) −2, 3, 4. Molecular dynamics simulation of the vaccine ensured in vivo stability in the biological system. The immune simulation of vaccine evinced elevated immune response. The efficient translation of the vaccine in an expression vector was assured utilizing in silico cloning approach. Certainly, such a vaccine construct could reliably be effective against COVID-19.

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

Improving catalytic ability of enzymes is critical to the success of many metabolic engineering projects, but the search space of possible protein mutants is too large to explore exhaustively through experiments. To some extent, highly soluble enzymes tend to exhibit high activity due to their better folding quality. Here, we demonstrate that an optimization algorithm based on a regression model can effectively design short peptide tags to improve solubility of a few model enzymes. Based on the protein sequence information, a support vector regression model we recently developed was used to evaluate protein solubility after small peptide tags were introduced to a target protein. The optimization algorithm guided the sequences of the tags to evolve towards variants that had higher solubility. The optimization results were validated successfully by measuring solubility and activity of the model enzyme with and without the identified tags. The solubility of one protein (tyrosine ammonia lyase) was more than doubled and its activity was improved by 250%. This strategy successfully increased solubility of another two enzymes (aldehyde dehydrogenase and 1-deoxy-D-xylulose-5-phosphate synthase) we tested. The presented optimization methodology thus provides a valuable tool for improving enzyme performance for metabolic engineering and other biotechnology projects.

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Short tags were added to proteins to improve their protein solubility.

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Machine learning model was used to optimize amino acid composition of the tags.

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Multiple proteins’ solubility was substantially improved.

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Catalytic activity of one enzyme was increased when its solubility was enhanced.

A vaccine using Anaplasma marginale subdominant type IV secretion system recombinant proteins was not protective against a virulent challenge

Anaplasma marginale is the most prevalent tick-borne livestock pathogen with worldwide distribution. Bovine anaplasmosis is a significant threat to cattle industry. Anaplasmosis outbreaks in endemic areas are prevented via vaccination with live A. centrale produced in splenectomized calves. Since A. centrale live vaccine can carry other pathogens and cause disease in adult cattle, research efforts are directed to develop safe recombinant subunit vaccines. Previous work found that the subdominant proteins of A. marginale type IV secretion system (T4SS) and the subdominant elongation factor-Tu (Ef-Tu) were involved in the protective immunity against the experimental challenge in cattle immunized with the A. marginale outer membrane (OM). This study evaluated the immunogenicity and protection conferred by recombinant VirB9.1, VirB9.2, VirB10, VirB11, and Ef-Tu proteins cloned and expressed in E. coli. Twenty steers were randomly clustered into four groups (G) of five animals each. Cattle from G1 and G2 were immunized with a mixture of 50 μg of each recombinant protein with Quil A® or Montanide™ adjuvants, respectively. Cattle from G3 and G4 (controls) were immunized with Quil A and Montanide adjuvants, respectively. Cattle received four immunizations at three-week intervals and were challenged with 107 A. marginale-parasitized erythrocytes 42 days after the fourth immunization. After challenge, all cattle showed clinical signs, with a significant drop of packed cell volume and a significant increase of parasitized erythrocytes (p<0.05), requiring treatment with oxytetracycline to prevent death. The levels of IgG2 induced in the immunized groups did not correlate with the observed lack of protection. Additional strategies are required to evaluate the role of these proteins and their potential utility in the development of effective vaccines.

Amino acid substitutions in a polygalacturonase inhibiting protein (OsPGIP2) increases sheath blight resistance in rice

BACKGROUND

An economic strategy to control plant disease is to improve plant defense to pathogens by deploying resistance genes. Plant polygalacturonase inhibiting proteins (PGIPs) have a vital role in plant defense against phytopathogenic fungi by inhibiting fungal polygalacturonase (PG) activity. We previously reported that rice PGIP1 (OsPGIP1) inhibits PG activity in Rhizoctonia solani, the causal agent of rice sheath blight (SB), and is involved in regulating resistance to SB.

RESULT

Here, we report that OsPGIP2, the protein ortholog of OsPGIP1, does not possess PGIP activity; however, a few amino acid substitutions in a derivative of OsPGIP2, of which we provide support for L233F being the causative mutation, appear to impart OsPGIP2 with PG inhibition capability. Furthermore, the overexpression of mutated OsPGIP2^L233F in rice significantly increased the resistance of transgenic lines and decreased SB disease rating scores. OsPGIP2^L233F transgenic lines displayed an increased ability to reduce the tissue degradation caused by R. solani PGs as compared to control plants. Rice plants overexpressing OsPGIP2^L233F showed no difference in agronomic traits and grain yield as compared to controls, thus demonstrating its potential use in rice breeding programs.

CONCLUSIONS

In summary, our results provide a new target gene for breeding SB resistance through genome-editing or natural allele mining.

Amino acid substitutions in a polygalacturonase inhibiting protein (OsPGIP2) increases sheath blight resistance in rice

BACKGROUND

An economic strategy to control plant disease is to improve plant defense to pathogens by deploying resistance genes. Plant polygalacturonase inhibiting proteins (PGIPs) have a vital role in plant defense against phytopathogenic fungi by inhibiting fungal polygalacturonase (PG) activity. We previously reported that rice PGIP1 (OsPGIP1) inhibits PG activity in Rhizoctonia solani, the causal agent of rice sheath blight (SB), and is involved in regulating resistance to SB.

RESULT

Here, we report that OsPGIP2, the protein ortholog of OsPGIP1, does not possess PGIP activity; however, a few amino acid substitutions in a derivative of OsPGIP2, of which we provide support for L233F being the causative mutation, appear to impart OsPGIP2 with PG inhibition capability. Furthermore, the overexpression of mutated OsPGIP2^L233F in rice significantly increased the resistance of transgenic lines and decreased SB disease rating scores. OsPGIP2^L233F transgenic lines displayed an increased ability to reduce the tissue degradation caused by R. solani PGs as compared to control plants. Rice plants overexpressing OsPGIP2^L233F showed no difference in agronomic traits and grain yield as compared to controls, thus demonstrating its potential use in rice breeding programs.

CONCLUSIONS

In summary, our results provide a new target gene for breeding SB resistance through genome-editing or natural allele mining.

Residue Mutations in Murine Herpesvirus 68 Immunomodulatory Protein M3 Reveal Specific Modulation of Chemokine Binding

The M3 protein (M3) encoded by murine gammaherpesvirus 68 (MHV-68) is a unique viral immunomodulator with a high-affinity for a broad spectrum of chemokines, key mediators responsible for the migration of immune cells to sites of inflammation. M3 is currently being studied as a very attractive and desirable tool for blocking the chemokine signaling involved in some inflammatory diseases and cancers. In this study, we elucidated the role of M3 residues E70 and T272 in binding to chemokines by examining the effects of the E70A and T272G mutations on the ability of recombinant M3, prepared in Escherichia coli cells, to bind the human chemokines CCL5 and CXCL8. We found that the E70A mutation enhanced binding of M3 to CCL5 two-fold but had little effect on its binding to CXCL8. In contrast, the T272G mutation was found to be important for the thermal stability of M3 and significantly decreased M3's binding to both CCL5 (by about 4×) and CXCL8 (by about 5×). We also constructed in silico models of the wild-type M3–CCL5 and M3–CCL8 complexes and found substantial differences in their physical and chemical properties. M3 models with single mutation E70A and T272G suggested the role of E70 and T272 in binding M3 protein to chemokines. In sum, we have confirmed that site-directed mutagenesis could be an effective tool for modulating the blockade of particular chemokines by M3, as desired in therapeutic treatments for severe inflammatory illnesses arising from chemokine network dysregulation.

Develop machine learning-based regression predictive models for engineering protein solubility

Motivation

Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. High activity of proteins reduces the cost of biocatalysts. A model that can predict protein activity from amino acid sequence is highly desired, as it aids experimental improvement of proteins. However, only limited data for protein activity are currently available, which prevents the development of such models. Since protein activity and solubility are correlated for some proteins, the publicly available solubility dataset may be adopted to develop models that can predict protein solubility from sequence. The models could serve as a tool to indirectly predict protein activity from sequence. In literature, predicting protein solubility from sequence has been intensively explored, but the predicted solubility represented in binary values from all the developed models was not suitable for guiding experimental designs to improve protein solubility. Here we propose new machine learning (ML) models for improving protein solubility in vivo.

Results

We first implemented a novel approach that predicted protein solubility in continuous numerical values instead of binary ones. After combining it with various ML algorithms, we achieved a R2 of 0.4115 when support vector machine algorithm was used. Continuous values of solubility are more meaningful in protein engineering, as they enable researchers to choose proteins with higher predicted solubility for experimental validation, while binary values fail to distinguish proteins with the same value—there are only two possible values so many proteins have the same one.

Availability and implementation

We present the ML workflow as a series of IPython notebooks hosted on GitHub (https://github.com/xiaomizhou616/protein_solubility). The workflow can be used as a template for analysis of other expression and solubility datasets.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bacterial inclusion bodies are industrially exploitable amyloids

Understanding the structure, functionalities and biology of functional amyloids is an issue of emerging interest. Inclusion bodies, namely protein clusters formed in recombinant bacteria during protein production processes, have emerged as unanticipated, highly tunable models for the scrutiny of the physiology and architecture of functional amyloids. Based on an amyloidal skeleton combined with varying amounts of native or native-like protein forms, bacterial inclusion bodies exhibit an unusual arrangement that confers mechanical stability, biological activity and conditional protein release, being thus exploitable as versatile biomaterials. The applicability of inclusion bodies in biotechnology as enriched sources of protein and reusable catalysts, and in biomedicine as biocompatible topographies, nanopills or mimetics of endocrine secretory granules has been largely validated. Beyond these uses, the dissection of how recombinant bacteria manage the aggregation of functional protein species into structures of highly variable complexity offers insights about unsuspected connections between protein quality (conformational status compatible with functionality) and cell physiology.

Development of a novel fusion protein with Anaplasma marginale and A. centrale MSP5 improved performance of Anaplasma antibody detection by cELISA in infected and vaccinated cattle

Detection of antibodies to Anaplasma spp. using commercial competitive enzyme-linked immunosorbent assay (ccELISA) is based on the recombinant major surface protein 5 fused to maltose binding protein (MBP-MSP5) or glutathione S-transferase (GST-MSP5). To avoid false positive reactions due to the presence of antibodies against E. coli MBP in cattle, previous sera absorption is required. This study evaluated the replacement of MBP-MSP5 or GST-MSP5 antigens by the truncate MSP5 (residues 28–210) of A. marginale (tMSP5m), A. centrale (tMSP5c) and fusion protein MSP5 (tMSP5cm), expressed without N-terminus transmembrane helix in the ccELISA test. Immunoreactivity was evaluated by western blot using monoclonal antibodies against the tMSP5 and by in-house cELISA (hcELISA) with purified tMSP5m, tMSP5c or tMSP5cm using sera from cattle infected with A. marginale (n = 226) or vaccinated with A. centrale (n = 173) and uninfected cattle (n = 216). Results of hcELISA were compared with those of ccELISA. Recombinant protein was expressed highly soluble (> 95%) in E. coli without a molecular chaperone. Specificity of the hcELISA-tMSP5m, -MSP5c or -tMSP5cm was identical to (99.5%) and greater than that in ccELISA (96.3%). Sensitivity of hcELISA-tMSP5m and ccELISA was identical (95.5%), but lower than that of hcELISA-tMSP5cm (96.2%) and -tMSP5c (97.2%). The analysis of vaccinated cattle by hcELISA-tMSP5c showed sensitivity of 99.4%. In summary, the generation of fusion MSP5 A. marginale-A. centrale protein without transmembrane helix was a very effective method to express the recombinant protein highly soluble in the bacterial cytoplasm and contributed to an increased test performance for detecting antibodies in cattle naturally infected with A. marginale or vaccinated with A. centrale.

Exploring dengue proteome to design an effective epitope-based vaccine against dengue virus

Dengue, a mosquito-borne disease, is caused by four known dengue serotypes. This infection causes a range of symptoms from a mild fever to a sever homorganic fever and death. It is a serious public health problem in subtropical and tropical countries. There is no specific vaccine currently available for clinical use and study on this issue is ongoing. In this study, bioinformatics approaches were used to predict antigenic, immunogenic, non-allergenic, and conserved B and T-cell epitopes as promising targets to design an effective peptide-based vaccine against dengue virus. Molecular docking analysis indicated the deep binding of the identified epitopes in the binding groove of the most popular human MHC I allele (human leukocyte antigens [HLA] A*0201). The final vaccine construct was created by conjugating the B and T-cell identified epitopes using proper linkers and adding an appropriate adjuvant at the N-terminal. The characteristics of the new subunit vaccine demonstrated that the epitope-based vaccine was antigenic, non-toxic, stable, and soluble. Other physicochemical properties of the new designed construct including isoelectric point value, aliphatic index, and grand average of hydropathicity were biologically considerable. Molecular docking of the engineered vaccine with Toll-like receptor 2 (TLR2) model revealed the hydrophobic interaction between the adjuvant and the ligand binding regions in the hydrophobic channel of TLR2. The study results indicated the high potential capability of the new multi-epitope vaccine to induce cellular and humoral immune responses against the dengue virus. Further experimental tests are required to investigate the immune protection capacity of the new vaccine construct in animal models. Communicated by Ramaswamy H. Sarma.

Genome-Wide Identification and Characterization of Pectin Methylesterase Inhibitor Genes in Brassica oleracea

The activities of pectin methylesterases (PMEs) are regulated by pectin methylesterase inhibitors (PMEIs), which consequently control the pectin methylesterification status. However, the role of PMEI genes in Brassica oleracea, an economically important vegetable crop, is poorly understood. In this study, 95 B. oleracea PMEI (BoPMEI) genes were identified. A total of 77 syntenic ortholog pairs and 10 tandemly duplicated clusters were detected, suggesting that the expansion of BoPMEI genes was mainly attributed to whole-genome triplication (WGT) and tandem duplication (TD). During diploidization after WGT, BoPMEI genes were preferentially retained in accordance with the gene balance hypothesis. Most homologous gene pairs experienced purifying selection with ω (Ka/Ks) ratios lower than 1 in evolution. Five stamen-specific BoPMEI genes were identified by expression pattern analysis. By combining the analyses of expression and evolution, we speculated that nonfunctionalization, subfunctionalization, neofunctionalization, and functional conservation can occur in the long evolutionary process. This work provides insights into the characterization of PMEI genes in B. oleracea and contributes to the further functional studies of BoPMEI genes.

Solubility and aggregation behavior of protein fractions from the heterotrophically cultivated microalga Chlorella protothecoides

Protein solubility in water is a key property of food proteins. The aim of this work was to study the solubility and microstructural properties as a function of pH of both protein fractions (water-soluble (WSPE) and water-insoluble protein extracts (WISPE)) obtained from the microalga Chlorella protothecoides, which is promising for food use. Protein solubility was determined as the ratio of protein concentration in the supernatant after centrifugation to total protein concentration. An unusually high solubility and only slight gravitational separation across a very broad pH-range (2-12) were observed for the WSPE with a minimum protein solubility of 84.3 ± 2.2% at pH 2. The origin of this high pH-independent protein solubility was attributed to a high degree of glycosylation and a high amount of hydrophilic amino acids. In contrast, the WISPE was found to contain strongly aggregated proteins, and these large aggregates separated rapidly from solution by gravitation independent of the pH. This corresponded to their protein solubility, which was overall low in the pH-range of 2-11, and only increased at pH 12 to a maximum solubility of 26.9 ± 2.8%. These results suggest that the WSPE of Chlorella protothecoides may exhibit unique properties for food formulations, allowing for example for both acidified, neutral or slightly alkaline foods to be formulated, whereas WISPE may be more suited for foods where phase separation is rather slow.

Codon usage clusters correlation: towards protein solubility prediction in heterologous expression systems in E. coli

Production of soluble recombinant proteins is crucial to the development of industry and basic research. However, the aggregation due to the incorrect folding of the nascent polypeptides is still a mayor bottleneck. Understanding the factors governing protein solubility is important to grasp the underlying mechanisms and improve the design of recombinant proteins. Here we show a quantitative study of the expression and solubility of a set of proteins from Bizionia argentinensis. Through the analysis of different features known to modulate protein production, we defined two parameters based on the %MinMax algorithm to compare codon usage clusters between the host and the target genes. We demonstrate that the absolute difference between all %MinMax frequencies of the host and the target gene is significantly negatively correlated with protein expression levels. But most importantly, a strong positive correlation between solubility and the degree of conservation of codons usage clusters is observed for two independent datasets. Moreover, we evince that this correlation is higher in codon usage clusters involved in less compact protein secondary structure regions. Our results provide important tools for protein design and support the notion that codon usage may dictate translation rate and modulate co-translational folding.

Machine learning in computational biology to accelerate high-throughput protein expression

Motivation

The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40 000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility.

Results

Combining computational biology and machine learning identifies protein properties that hinder the HPA high-throughput antibody production pipeline. We predict protein expression and solubility with accuracies of 70% and 80%, respectively, based on a subset of key properties (aromaticity, hydropathy and isoelectric point). We guide the selection of protein fragments based on these characteristics to optimize high-throughput experimentation.

Availability and implementation

We present the machine learning workflow as a series of IPython notebooks hosted on GitHub (https://github.com/SBRG/Protein_ML). The workflow can be used as a template for analysis of further expression and solubility datasets.

Contact

ebrunk@ucsd.edu or johanr@biotech.kth.se.

Supplementary information

Supplementary data are available at Bioinformatics online.

[Construction and characterization of a recombinant mutant homolog of the CheW protein from Thermotoga petrophila RKU-1]

In the work a recombinant chemotaxis protein CheW from Thermotoga petrophila RKU-1 (TpeCheW) and its mutant homolog (TpeCheW-mut) were created. It was shown that, despite the low homology with CheW prototypes from intestinal bacteria, these proteins didn't cause metabolic overload and were well expressed by cells of E. coli laboratory strains. We have discovered a broad spectrum of industrial valuable properties of the TpeCheW-mut protein such as stability in a wide range of temperatures and pH, high expression level, solubility and possibility of the application of a simple low-stage purification methodology with the use of preliminary heat treatment. Possible directions of the scientific and industrial application of this protein were claimed.

Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation

Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.

Catalytically-active inclusion bodies-Carrier-free protein immobilizates for application in biotechnology and biomedicine

Bacterial inclusion bodies (IBs) consist of unfolded protein aggregates and represent inactive waste products often accumulating during heterologous overexpression of recombinant genes in Escherichia coli. This general misconception has been challenged in recent years by the discovery that IBs, apart from misfolded polypeptides, can also contain substantial amounts of active and thus correctly or native-like folded protein. The corresponding catalytically-active inclusion bodies (CatIBs) can be regarded as a biologically-active sub-micrometer sized biomaterial or naturally-produced carrier-free protein immobilizate. Fusion of polypeptide (protein) tags can induce CatIB formation paving the way towards the wider application of CatIBs in synthetic chemistry, biocatalysis and biomedicine. In the present review we summarize the history of CatIBs, present the molecular-biological tools that are available to induce CatIB formation, and highlight potential lines of application. In the second part findings regarding the formation, architecture, and structure of (Cat)IBs are summarized. Finally, an overview is presented about the available bioinformatic tools that potentially allow for the prediction of aggregation and thus (Cat)IB formation. This review aims at demonstrating the potential of CatIBs for biotechnology and hopefully contributes to a wider acceptance of this promising, yet not widely utilized, protein preparation.

Evolution of Arabidopsis protection of telomeres 1 alters nucleic acid recognition and telomerase regulation

Protection of telomeres (POT1) binds chromosome ends, recognizing single-strand telomeric DNA via two oligonucleotide/oligosaccharide binding folds (OB-folds). The Arabidopsis thaliana POT1a and POT1b paralogs are atypical: they do not exhibit telomeric DNA binding, and they have opposing roles in regulating telomerase activity. AtPOT1a stimulates repeat addition processivity of the canonical telomerase enzyme, while AtPOT1b interacts with a regulatory lncRNA that represses telomerase activity. Here, we show that OB1 of POT1a, but not POT1b, has an intrinsic affinity for telomeric DNA. DNA binding was dependent upon a highly conserved Phe residue (F65) that in human POT1 directly contacts telomeric DNA. F65A mutation of POT1a_OB1 abolished DNA binding and diminished telomerase repeat addition processivity. Conversely, AtPOT1b and other POT1b homologs from Brassicaceae and its sister family, Cleomaceae, naturally bear a non-aromatic amino acid at this position. By swapping Val (V63) with Phe, AtPOT1b_OB1 gained the capacity to bind telomeric DNA and to stimulate telomerase repeat addition processivity. We conclude that, in the context of DNA binding, variation at a single amino acid position promotes divergence of the AtPOT1b paralog from the ancestral POT1 protein.

Immunolocalization of Arthropsin in the Onychophoran Euperipatoides rowelli (Peripatopsidae)

Opsins are light-sensitive proteins that play a key role in animal vision and are related to the ancient photoreceptive molecule rhodopsin found in unicellular organisms. In general, opsins involved in vision comprise two major groups: the rhabdomeric (r-opsins) and the ciliary opsins (c-opsins). The functionality of opsins, which is dependent on their protein structure, may have changed during evolution. In arthropods, typically r-opsins are responsible for vision, whereas in vertebrates c-opsins are components of visual photoreceptors. Recently, an enigmatic r-opsin-like protein called arthropsin has been identified in various bilaterian taxa, including arthropods, lophotrochozoans, and chordates, by performing transcriptomic and genomic analyses. Since the role of arthropsin and its distribution within the body are unknown, we immunolocalized this protein in a representative of Onychophora – Euperipatoides rowelli – an ecdysozoan taxon which is regarded as one of the closest relatives of Arthropoda. Our data show that arthropsin is expressed in the central nervous system of E. rowelli, including the brain and the ventral nerve cords, but not in the eyes. These findings are consistent with previous results based on reverse transcription PCR in a closely related onychophoran species and suggest that arthropsin is a non-visual protein. Based on its distribution in the central brain region and the mushroom bodies, we speculate that the onychophoran arthropsin might be either a photosensitive molecule playing a role in the circadian clock, or a non-photosensitive protein involved in olfactory pathways, or both.

Higher efficiency soluble prokaryotic expression, purification, and structural analysis of antimicrobial peptide G13

G13 is a 19-residue cationic antimicrobial peptide derived from granulysin. In order to achieve high-level expression of G13 in Escherichia coli cells, and to reduce downstream processing costs, we introduced an Asp-Pro acid labile bond between the His-Patch thioredoxin and G13 and constructed the recombinant plasmid pThiohisA-DP-G13. The plasmid was transformed into E. coli BL21 (DE3). After induction with isopropyl-β-d-thiogalactopyranoside for 5 h, the fusion protein accumulated up to 200 mg/L in soluble form. The fusion protein was released by a high pressure homogenizer, cleaved using 13% acetic acid at 50 °C hydrolysis for 72 h. The recombinant G13 (r-G13) was then successively purified by fractional precipitation with ammonium sulfate and trichloroacetic acid, followed by one-step cation exchange chromatography. The purified r-G13 displayed a single band (about 2.2 kDa) as analyzed by Tris-Tricine buffered SDS-PAGE, and its precise molecular weight was confirmed using tandem mass spectrometry. Analysis of r-G13 by circular dichroism (CD) indicated that r-G13 contained predominantly β-sheet and random coil. Agar plate diffusion assay revealed that the r-G13 exhibited antibacterial activity against both Bacillus subtilis and E. coli.