Gn protein expressed in plants for diagnosis of severe fever with thrombocytopenia syndrome virus.

Severe fever with thrombocytopenia syndrome virus (SFTSV) causes the highly fatal disease in humans. To facilitate diagnosis, the native form of subunit glycoprotein (Gn), a prime target for potential vaccines and therapies, was produced in Nicotiana benthamiana using a Bamboo mosaic virus-based vector system. By fusion with secretory signal tags, SSExt, derived from the extension protein, and the (SP)10 motif, the yield of the recombinant Gn (rGn) was remarkably increased to approximately 7 mg/kg infiltrated leaves. Ultimately, an rGn-based ELISA was successfully established for the detection of SFTSV-specific antibodies in serum samples from naturally infected monkeys. As validated with the reference method, the specificity and sensitivity of rGn-ELISA were 94% and 96%, respectively. In conclusion, utilizing well-suited fusion tags facilitates rGn production and purification in substantial quantities while preserving its antigenic properties. The rGn-ELISA, characterized by its commendable sensitivity and specificity could serve as a viable alternative diagnostic method for assessing SFTSV seroprevalence. KEY POINTS: • SFTSV Gn, fused with secretory signal tags, was expressed by the BaMV-based vector. • The plant fusion tags increased expression levels and eased the purification of rGn. • The rGn-ELISA was established and validated; its specificity and sensitivity > 94%.

HybAVPnet: a Novel Hybrid Network Architecture for Antiviral Peptides Prediction.

Viruses pose a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, it is critical to use computational methods to predict antiviral peptides accurately and rapidly. However, due to limited data, accurate prediction of antiviral peptides is still challenging and extracting effective feature representations from sequences is crucial for creating accurate models. This study introduces a novel two-step approach, named HybAVPnet, to predict antiviral peptides with a hybrid network architecture based on neural networks and traditional machine learning methods. We adopted a stacking-like structure to capture both the long-term dependencies and local evolution information to achieve a comprehensive and diverse prediction using the predicted labels and probabilities. Using an ensemble technique with the different kinds of features can reduce the variance without increasing the bias. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability.

Development of a tag-free plant-made interferon gamma production system with improved therapeutic efficacy against viruses.

Plants offer a promising platform for cost-effective production of biologically active therapeutic glycoproteins. In previous studies, we have developed a plant expression system based on Bamboo mosaic virus (BaMV) by incorporating secretory signals and an affinity tag, which resulted in notably enhanced yields of soluble and secreted fusion glycoproteins (FGs) in Nicotiana benthamiana. However, the presence of fusion tags on recombinant glycoproteins is undesirable for biomedical applications. This study aimed to develop a refined expression system that can efficiently produce tag-free glycoproteins in plants, with enhanced efficacy of mature interferon gamma (mIFNγ) against viruses. To accommodate the specific requirement of different target proteins, three enzymatically or chemically cleavable linkers were provided in this renovated BaMV-based expression system. We demonstrated that Tobacco etch virus (TEV) protease could process the specific cleavage site (LTEV) of the fusion protein, designated as SSExtHis(SP)10LTEV-mIFNγ, with optimal efficiency under biocompatible conditions to generate tag-free mIFNγ glycoproteins. The TEV protease and secretory-affinity tag could be effectively removed from the target mIFNγ glycoproteins through Ni2+-NTA chromatography. In addition, the result of an antiviral assay showed that the tag-free mIFNγ glycoproteins exhibited enhanced biological properties against Sindbis virus, with comparable antiviral activity of the commercialized HEK293-expressed hIFNγ. Thus, the improved BaMV-based expression system developed in this study may provide an alternative strategy for producing tag-free therapeutic glycoproteins intended for biomedical applications.

SuccSPred2.0: A Two-Step Model to Predict Succinylation Sites Based on Multifeature Fusion and Selection Algorithm.

Protein succinylation is a novel type of post-translational modification in recent decade years. It played an important role in biological structure and functions verified by experiments. However, it is time consuming and laborious for the wet experimental identification of succinylation sites. Traditional technology cannot adapt to the rapid growth of the biological sequence data sets. In this study, a new computational method named SuccSPred2.0 was proposed to identify succinylation sites in the protein sequences based on multifeature fusion and maximal information coefficient (MIC) method. SuccSPred2.0 was implemented based on a two-step strategy. At first, high-dimension features were reduced by linear discriminant analysis to prevent overfitting. Subsequently, MIC method was employed to select the important features binding classifiers to predict succinylation sites. From the compared experiments on 10-fold cross-validation and independent test data sets, SuccSPred2.0 obtained promising improvements. Comparative experiments showed that SuccSPred2.0 was superior to previous tools in identifying succinylation sites in the given proteins.

i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification.

Parasites can cause enormous damage to their hosts. Studies have shown that antiparasitic peptides can inhibit the growth and development of parasites and even kill them. Because traditional biological methods to determine the activity of antiparasitic peptides are time-consuming and costly, a method for large-scale prediction of antiparasitic peptides is urgently needed. We propose a computational approach called i2APP that can efficiently identify APPs using a two-step machine learning (ML) framework. First, in order to solve the imbalance of positive and negative samples in the training set, a random under sampling method is used to generate a balanced training data set. Then, the physical and chemical features and terminus-based features are extracted, and the first classification is performed by Light Gradient Boosting Machine (LGBM) and Support Vector Machine (SVM) to obtain 264-dimensional higher level features. These features are selected by Maximal Information Coefficient (MIC) and the features with the big MIC values are retained. Finally, the SVM algorithm is used for the second classification in the optimized feature space. Thus the prediction model i2APP is fully constructed. On independent datasets, the accuracy and AUC of i2APP are 0.913 and 0.935, respectively, which are better than the state-of-arts methods. The key idea of the proposed method is that multi-level features are extracted from peptide sequences and the higher-level features can distinguish well the APPs and non-APPs.

Fusion of a Novel Native Signal Peptide Enhanced the Secretion and Solubility of Bioactive Human Interferon Gamma Glycoproteins in Nicotiana benthamiana Using the Bamboo Mosaic Virus-Based Expression System.

Plant viruses may serve as expression vectors for the efficient production of pharmaceutical proteins in plants. However, the downstream processing and post-translational modifications of the target proteins remain the major challenges. We have previously developed an expression system derived from Bamboo mosaic virus (BaMV), designated pKB19, and demonstrated its applicability for the production of human mature interferon gamma (mIFNγ) in Nicotiana benthamiana. In this study, we aimed to enhance the yields of soluble and secreted mIFNγ through the incorporation of various plant-derived signal peptides. Furthermore, we analyzed the glycosylation patterns and the biological activity of the mIFNγ expressed by the improved pKB19 expression system in N. benthamiana. The results revealed that the fusion of a native N. benthamiana extensin secretory signal (SSExt) to the N-terminal of mIFNγ (designated SSExt mIFNγ) led to the highest accumulation level of protein in intracellular (IC) or apoplast washing fluid (AWF) fractions of N. benthamiana leaf tissues. The addition of 10 units of 'Ser-Pro' motifs of hydroxyproline-O-glycosylated peptides (HypGPs) at the C-terminal end of SSExt mIFNγ (designated SSExt mIFNγ(SP)10) increased the solubility to nearly 2.7- and 1.5-fold higher than those of mIFNγ and SSExt mIFNγ, respectively. The purified soluble SSExt mIFNγ(SP)10 protein was glycosylated with abundant complex-type N-glycan attached to residues N56 and N128, and exhibited biological activity against Sindbis virus and Influenza virus replication in human cell culture systems. In addition, suspension cell cultures were established from transgenic N. benthamiana, which produced secreted SSExt mIFNγ(SP)10 protein feasible for downstream processing. These results demonstrate the applicability of the BaMV-based vector systems as a useful alternative for the production of therapeutic proteins, through the incorporation of appropriate fusion tags.

Production of Human IFNγ Protein in Nicotiana benthamiana Plant through an Enhanced Expression System Based on Bamboo mosaic Virus.

Plant-based systems are safe alternatives to the current platforms for the production of biologically active therapeutic proteins. However, plant-based expression systems face certain major challenges, including the relatively low productivity and the generation of target proteins in biologically active forms. The use of plant virus-based expression systems has been shown to enhance yields, but further improvement is still required to lower the production cost. In this study, various strategies were employed to increase the yields of an important therapeutic protein, human interferon gamma (IFNγ), in Nicotiana benthamiana through modifications of expression vectors based on potexviruses. Among these, the vector based on a coat protein (CP)-deficient Bamboo mosaic virus (BaMV), pKB△CHis, was shown to exhibit the highest expression level for the unmodified IFNγ. Truncation of the N-terminal signal peptide of IFN (designated mIFNγ) resulted in a nearly seven-fold increase in yield. Co-expression of a silencing suppressor protein by replacing the coding sequence of BaMV movement protein with that of P19 led to a 40% increase in mIFNγ accumulation. The fusion of endoplasmic reticulum (ER) retention signal with mIFNγ significantly enhanced the accumulation ratio of biologically active dimeric mIFNγ to 87% relative to the non-active monomeric form. The construct pKB19mIFNγER, employing the combination of all the above enhancement strategies, gave the highest level of protein accumulation, up to 119 ± 0.8 µg/g fresh weight, accounting for 2.5% of total soluble protein (TSP) content. These findings advocate the application of the modified BaMV-based vector as a platform for high-level expression of therapeutic protein in N. benthamiana.