Rational identification of aggregation hotspots based on secondary structure and amino acid hydrophobicity

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.

Nitrile-synthesizing enzymes and biocatalytic synthesis of volatile nitrile compounds: A review

Nitriles (R-CN) comprise a broad group of chemicals industrially produced and used in fine chemicals, pharmaceuticals, and bulk applications, polymer chemistry, solvents, etc. Nitriles are important starting materials for producing carboxylic acids, amides, amines, and several other compounds. In addition, some volatile nitriles have been evaluated for their potential as ingredients in fragrance and flavor formulations. However, many nitrile synthesis methods have drawbacks, such as drastic reaction conditions, limited substrate scope, lack of readily available reagents, poor yields, and long reaction times. In contrast to chemical synthesis, biocatalytic approaches using enzymes can produce nitriles without harsh conditions, such as high temperatures and pressures, or toxic compounds. In this review, we summarize the nitrile-synthesizing enzymes from microorganisms, plants, and animals. Furthermore, we introduce several examples of biocatalytic synthesis of volatile nitrile compounds, particularly those using aldoxime dehydratase.

First identification of canine adenovirus 1 in mink and bioinformatics analysis of its 100 K protein

Introduction

Animal trade favors the spreading of emerging canine adenovirus 1 (CAdV-1) in mink. Because the 100K protein is not exposed to the viral surface at any stage, it can be used to differentiate the vaccine from wild virus infection. However, no related research has been conducted. This study aimed to find evidence of CAdV-1 in mink and predict the character of the 100K protein in the current circulating CAdV-1 strain of mink.

Method

In this experiment, the identification of CAdV-1, the phylogenetic tree, homology, and bioinformatics analysis of 100K were conducted.

Results

The results showed that the CAdV-1 was identified in the mink and that its Fiber was located in a separate branch. It was closely related to strains isolated from Norwegian Arctic fox and Red fox. 100K was located in a separate branch, which had the closest genetic relationship with skunks, porcupines, raccoons, and hedgehogs and a far genetic relationship with the strains in dogs. 100K protein is an unstable and hydrophobic protein. It had evidence of selective pressure and recombination, 1 glycosylation site, 48 phosphorylation sites, 60 dominant B cell epitopes, and 9 peptides of MHC-I and MHC-II. Its subcellular localization was mainly in the endoplasmic reticulum and mitochondria. The binding sites of 100K proteins were DBP proteins and 33K proteins.

Discussion

The stains in the mink were different from fox. The exploration of its genomic characteristics will provide us with a deeper understanding of the prevention of canine adenovirus.

PseAraUbi: predicting arabidopsis ubiquitination sites by incorporating the physico-chemical and structural features

We makes three kinds of important features from Arabidopsis thaliana: protein secondary structure based on the Chou-Fasman parameter, amino acids hydrophobicity and polarity information, and analyze their properties. Ubiquitination modification is an important post-translational modification of proteins, which participates in the regulation of many important life activities in cells. At present, ubiquitination proteomics research is mostly concentrated in animals and yeasts, while relatively few studies have been carried out in plants. It can be said that the calculation and prediction of Arabidopsis thaliana ubiquitination sites is still in its infancy. Based on this, we describe a calculation method, PseAraUbi (Prediction of Arabidopsis thaliana ubiquitination sites using pseudo amino acid composition), that can effectively detect ubiquitination sites on Arabidopsis thaliana using support vector machine learning classifiers. Based on protein sequence information, extract features from the Chou-Fasman parameter, amino acids hydrophobicity features, polarity information and selected for classification with the Boruta algorithm. PseAraUbi achieves promising performances with an AUC score of 0.953 with fivefold cross-validation on the training dataset, which are significantly better than that of the pioneer Arabidopsis thaliana ubiquitination sites method. We also proved the ability of our proposed method on independent test sets, thus gaining a competitive advantage. In addition, we also in-depth analyzed the physicochemical properties of amino acids in the region adjacent to the ubiquitination site. To facilitate the community, the source code, optimal feature subset, ubiquitination sites dataset in the Arbidopsis proteome are available at GitHub ( https://github.com/HNUBioinformatics/PseAraUbi.git ) for interest users.

Rational Design of the Soluble Variant of l-Pipecolic Acid Hydroxylase using the α-Helix Rule and the Hydropathy Contradiction Rule

The production of recombinant proteins in Escherichia coli is an important application of biotechnology. 2-Oxoglutarate-dependent l-pipecolic acid hydroxylase derived from Xenorhabdus doucetiae (XdPH) is an excellent biocatalyst that catalyzes the hydroxylation of l-pipecolic acid to produce cis-5-hydroxy-l-pipecolic acid. However, the enzyme tends to form aggregates in the E. coli expression system. Our group established two rules, namely, the "α-helix rule" and the "hydropathy contradiction rule," to select residues to be altered for improving the heterologous recombinant production of proteins, by analyzing their primary structure. We rationally designed XdPH variants that are expressed in highly soluble and active forms in the E. coli expression system using these hotspot prediction methods, and the L142R variant showed a remarkably high soluble expression level compared to the wild-type XdPH. Further mutations were introduced into the L142R gene by site-directed mutagenesis. Moreover, the I28P/L142R and C76Y/L142R double variants displayed improved soluble expression levels compared to the single variants. These variants were also more thermostable than the wild-type XdPH. To analyze the effect of the alteration on one of the hotspots, L142 was replaced with various hydrophilic and positively charged residues. The remarkable increase in soluble protein expression caused by the alterations suggests that the decrease in the hydrophobicity of the protein surface and the enhancement of the interaction between nearby residues are important factors determining the solubility of the protein. Overall, this study demonstrated the effectiveness of our protocol in identifying aggregation hotspots for recombinant protein production and in basic biochemical research.

PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli

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The ensemble model considered both fermentation conditions and protein properties.

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Optimal fermentation conditions and periplasmic recombinant protein yield can be predicted.

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Predictor’s accuracy and Pearson correlation coefficient are 75% and 0.91, respectively.

Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu).

Chemical stabilization of γ-polyglutamate by chitosan and the effect of co-solvents on the stability

In protein-based formulations, conformational distortions and attractive interactions may cause insoluble and undesired aggregates. In the case of ionic peptides, including cationic or anionic, commonly electrostatic interactions are the main factors that control structure assembling. In this study, it was proposed that grafting of chitosan (CS) to γ-polyglutamic acid (γ-PGA) might exhibit much strong inhibiting effect on the formation of protein aggregates due to multiple amino groups and hydrophilic properties. To guarantee stable and safe biopharmaceutical formulation, the potency of a variety of stabilizers including sugars (glucose, sucrose), polyols (sorbitol, glycerol), surfactant (Tween 20), salting-out salt (PBS), and also different pH values have been evaluated on stabilizing or destabilizing the native state of CS-g-PGA copolymer using FTIR, CD, DLS, and SDS-PAGE. The comparable analysis revealed that the stability of CS-g-PGA was strongly dependent on pH owing to the polyelectrolyte characteristics of the polymers. Altogether these results implied that CS at optimized conditions might be an important precursor for the pharmaceutical industry and function as a new polymer for aggregation suppression and protein stabilization.

Partial Consensus Design and Enhancement of Protein Function by Secondary-Structure-Guided Consensus Mutations

Consensus design (CD) is a representative sequence-based protein design method that enables the design of highly functional proteins by analyzing vast amounts of protein sequence data. This study proposes a partial consensus design (PCD) of a protein as a derivative approach of CD. The method replaces the target protein sequence with a consensus sequence in a secondary-structure-dependent manner (i.e., regionally dependent and divided into α-helix, β-sheet, and loop regions). In this study, we generated several artificial partial consensus l-threonine 3-dehydrogenases (PcTDHs) by PCD using the TDH from Cupriavidus necator (CnTDH) as a target protein. Structural and functional analysis of PcTDHs suggested that thermostability would be independently improved when consensus mutations are introduced into the loop region of TDHs. On the other hand, enzyme kinetic parameters (k_cat/K_m) and average productivity would be synergistically enhanced by changing the combination of the mutations-replacement of one region of CnTDH with a consensus sequence provided only negative effects, but the negative effects were nullified when the two regions were replaced simultaneously. Taken together, we propose the hypothesis that there are protein regions that encode individual protein properties, such as thermostability and activity, and that the introduction of consensus mutations into these regions could additively or synergistically modify their functions.

Protein engineering of the aldoxime dehydratase from Bacillus sp. OxB-1 based on a rational sequence alignment approach

Recently, the program INTMSAlign_HiSol for identifying aggregation hotspots in proteins only requiring secondary structure data was introduced. We explored the utility of this program further and applied it for engineering of the aldoxime dehydratase from Bacillus sp. OxB-1. Towards this end, the effect of inverting the hydropathy at selected positions of the amino acid sequence on the enzymatic activity was studied leading to 60% of our constructed variants, which showed improved activity. In part, this activity increase can be rationalised by an improved heme incorporation of the variants. For example, a single mutation gave a 1.8 fold increased enzymatic activity and 30% improved absolute heme incorporation.

Ultraefficiently Calming Cytokine Storm Using Ti3C2T x MXene

During the global outbreak of COVID-19 pandemic, "cytokine storm" conditions are regarded as the fatal step resulting in most mortality. Hemoperfusion is widely used to remove cytokines from the blood of severely ill patients to prevent uncontrolled inflammation induced by a cytokine storm. This article discoveres, for the first time, that 2D Ti3C2T x MXene sheet demonstrates an ultrahigh removal capability for typical cytokine interleukin-6. In particular, MXene shows a 13.4 times higher removal efficiency over traditional activated carbon absorbents. Molecular-level investigations reveal that MXene exhibits a strong chemisorption mechanism for immobilizing cytokine interleukin-6 molecules, which is different from activated carbon absorbents. MXene sheet also demonstrates excellent blood compatibility without any deleterious side influence on the composition of human blood. This work can open a new avenue to use MXene sheets as an ultraefficient hemoperfusion absorbent to eliminate the cytokine storm syndrome in treatment of severe COVID-19 patients.

Biochemical characterization of a glycosyltransferase Gtf3 from Mycobacterium smegmatis: a case study of improved protein solubilization

Glycosyltransferases (GTs) are widely present in several organisms. These enzymes specifically transfer sugar moieties to a range of substrates. The processes of bacterial glycosylation of the cell wall and their relations with host-pathogen interactions have been studied extensively, yet the majority of mycobacterial GTs involved in the cell wall synthesis remain poorly characterized. Glycopeptidolipids (GPLs) are major class of glycolipids present on the cell wall of various mycobacterial species. They play an important role in drug resistance and host-pathogen interaction virulence. Gtf3 enzyme performs a key step in the biosynthesis of triglycosylated GPLs. Here, we describe a general procedure to achieve expression, purification, and crystallization of recombinant protein Gtf3 from Mycobacterium smegmatis using an E. coli expression system. We reported also a combined bioinformatics and biochemical methods to predict aggregation propensity and improve protein solubilization of recombinant Gtf3. NVoy, a carbohydrate-based polymer reagent, was added to prevent protein aggregation by binding to hydrophobic protein surfaces of Gtf3. Using intrinsic tryptophan fluorescence quenching experiments, we also demonstrated that Gtf3-NVoy enzyme interacted with TDP and UDP nucleotide ligands. This case report proposes useful tools for the study of other glycosyltransferases which are rather difficult to characterize and crystallize.

AggreRATE-Pred: a mathematical model for the prediction of change in aggregation rate upon point mutation

MOTIVATION

Protein aggregation is a major unsolved problem in biochemistry with implications for several human diseases, biotechnology and biomaterial sciences. A majority of sequence-structural properties known for their mechanistic roles in protein aggregation do not correlate well with the aggregation kinetics. This limits the practical utility of predictive algorithms.

RESULTS

We analyzed experimental data on 183 unique single point mutations that lead to change in aggregation rates for 23 polypeptides and proteins. Our initial mathematical model obtained a correlation coefficient of 0.43 between predicted and experimental change in aggregation rate upon mutation (P-value <0.0001). However, when the dataset was classified based on protein length and conformation at the mutation sites, the average correlation coefficient almost doubled to 0.82 (range: 0.74-0.87; P-value <0.0001). We observed that distinct sequence and structure-based properties determine protein aggregation kinetics in each class. In conclusion, the protein aggregation kinetics are impacted by local factors and not by global ones, such as overall three-dimensional protein fold, or mechanistic factors such as the presence of aggregation-prone regions.

AVAILABILITY AND IMPLEMENTATION

The web server is available at http://www.iitm.ac.in/bioinfo/aggrerate-pred/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Aβ, Tau, and α-Synuclein aggregation and integrated role of PARK2 in the regulation and clearance of toxic peptides

Alzheimer's and Parkinson's diseases are one of the world's leading causes of death. >50 million people throughout the world are suffering with these diseases. They are two distinct progressive neurodegenerative disorders affecting different regions of the brain with diverse symptoms, including memory and motor loss respectively, but with the advancement of diseases, both affect the whole brain and exhibit some common biological symptoms. For instance, >50% PD patients develop dementia in their later stages, though it is a hallmark of Alzheimer's disease. In fact, latest research has suggested the involvement of some common pathophysiological and genetic links between these diseases, including the deposition of pathological Aβ, Tau, and α-synuclein in both the cases. Therefore, it is pertinent to diagnose the shared biomarkers, their aggregation mechanism, their intricate relationships in the pathophysiology of disease and therapeutic markers to target them. This would enable us to identify novel markers for the early detection of disease and targets for the future therapies. Herein, we investigated molecular aspects of Aβ, Tau, and α-Synuclein aggregation, and characterized their functional partners involved in the pathology of AD and PD. Moreover, we identified the molecular-crosstalk between AD and PD associated with their pathogenic proteins- Aβ, Tau, and α-Synuclein. Furthermore, we characterized their ubiquitinational enzymes and associated interaction network regulating the proteasomal clearance of these pathological proteins.

Self-assemble peptide biomaterials and their biomedical applications

Inspired by self-assembling peptides found in native proteins, deliberately designed engineered peptides have shown outstanding biocompatibility, biodegradability, and extracellular matrix-mimicking microenvironments. Assembly of the peptides can be triggered by external stimuli, such as electrolytes, temperature, and pH. The formation of nanostructures and subsequent nanocomposite materials often occur under physiological conditions. The respective properties of side chains in each amino acids provide numerous sites for chemical modification and conjugation choices of the peptides, enabling various resulting supramolecular nanostructures and hydrogels with adjustable mechanical and physicochemical properties. Moreover, additional functionalities can be easily induced into the hydrogels, including shear-thinning, bioactivity, self-healing, and shape memory. It further broaden the scope of application of self-assemble peptide materials. This review outlines designs of self-assembly peptide (β-sheet, α-helix, collagen-like peptides, elastin-like polypeptides, and peptide amphiphiles) with potential additional functionalities and their biomedical applications in bioprinting, tissue engineering, and drug delivery.

Surface patches on recombinant erythropoietin predict protein solubility: engineering proteins to minimise aggregation

BACKGROUND

Protein solubility characteristics are important determinants of success for recombinant proteins in relation to expression, purification, storage and administration. Escherichia coli offers a cost-efficient expression system. An important limitation, whether for biophysical studies or industrial-scale production, is the formation of insoluble protein aggregates in the cytoplasm. Several strategies have been implemented to improve soluble expression, ranging from modification of culture conditions to inclusion of solubility-enhancing tags.

RESULTS

Surface patch analysis has been applied to predict amino acid changes that can alter the solubility of expressed recombinant human erythropoietin (rHuEPO) in E. coli, a factor that has importance for both yield and subsequent downstream processing of recombinant proteins. A set of rHuEPO proteins (rHuEPO E13K, F48D, R150D, and F48D/R150D) was designed (from the framework of wild-type protein, rHuEPO WT, via amino acid mutations) that varied in terms of positively-charged patches. A variant predicted to promote aggregation (rHuEPO E13K) decreased solubility significantly compared to rHuEPO WT. In contrast, variants predicted to diminish aggregation (rHuEPO F48D, R150D, and F48D/R150D) increased solubility up to 60% in relation to rHuEPO WT.

CONCLUSIONS

These findings are discussed in the wider context of biophysical calculations applied to the family of EPO orthologues, yielding a diverse range of calculated values. It is suggested that combining such calculations with naturally-occurring sequence variation, and 3D model generation, could lead to a valuable tool for protein solubility design.

Enhancing thermostability and removing hemin inhibition of Rhodopseudomonas palustris 5-aminolevulinic acid synthase by computer-aided rational design

OBJECTIVE

To enhance the thermostability and deregulate the hemin inhibition of 5-aminolevulinic acid (ALA) synthase from Rhodopseudomonas palustris (RP-ALAS) by a computer-aided rational design strategy.

RESULTS

Eighteen RP-ALAS single variants were rationally designed and screened by measuring their residual activities upon heating. Among them, H29R and H15K exhibited a 2.3 °C and 6.0 °C higher melting temperature than wild-type, respectively. A 6.7-fold and 10.3-fold increase in specific activity after 1 h incubation at 37 °C was obtained for H29R (2.0 U/mg) and H15K (3.1 U/mg) compared to wild-type (0.3 U/mg). Additionally, higher residual activities in the presence of hemin were obtained for H29R and H15K (e.g., 64% and 76% at 10 μM hemin vs. 27% for wild-type). The ALA titer was increased by 6% and 22% in fermentation using Corynebacterium glutamicum ATCC 13032 expressing H29R and H15K, respectively.

CONCLUSION

H29R and H15K showed high thermostability, reduced hemin inhibition and slightly high activity, indicating that these two variants are good candidates for bioproduction of ALA.

Designing artificial metabolic pathways, construction of target enzymes, and analysis of their function

Artificial design of metabolic pathways is essential for the production of useful compounds using microbes. Based on this design, heterogeneous genes are introduced into the host, and then various analysis and evaluation methods are conducted to ensure that the target enzyme reactions are functionalized within the cell. In this chapter, we list successful examples of useful compounds produced by designing artificial metabolic pathways, and describe the methods involved in analyzing, evaluating, and optimizing the target enzyme reaction.

Stabilizing Leaf and Branch Compost Cutinase (LCC) with Glycosylation: Mechanism and Effect on PET Hydrolysis

Cutinases are polyester hydrolases that show a remarkable capability to hydrolyze polyethylene terephthalate (PET) to its monomeric units. This revelation has stimulated research aimed at developing sustainable and green cutinase-catalyzed PET recycling methods. Leaf and branch compost cutinase (LCC) is particularly suited toward these ends given its relatively high PET hydrolysis activity and thermostability. Any practical enzymatic PET recycling application will require that the protein have kinetic stability at or above the PET glass transition temperature (T_g, i.e., 70 °C). This paper elucidates the thermodynamics and kinetics of LCC conformational and colloidal stability. Aggregation emerged as a major contributor that reduces LCC kinetic stability. In its native state, LCC is prone to aggregation owing to electrostatic interactions. Further, with increasing temperature, perturbation of LCC's tertiary structure and corresponding exposure of hydrophobic domains leads to rapid aggregation. Glycosylation was employed in an attempt to impede LCC aggregation. Owing to the presence of three putative N-glycosylation sites, expression of native LCC in Pichia pastoris resulted in the production of glycosylated LCC (LCC-G). LCC-G showed improved stability to native state aggregation while increasing the temperature for thermal induced aggregation by 10 °C. Furthermore, stabilization against thermal aggregation resulted in improved catalytic PET hydrolysis both at its optimum temperature and concentration.