Optimized production of full-length PCV2d virus-like particles in Escherichia coli: A cost-effective and high-yield approach for potential vaccine antigen development.

Porcine circovirus type 2 (PCV2) is a globally prevalent infectious pathogen affecting swine, with its capsid protein (Cap) being the sole structural protein critical for vaccine development. Prior research has demonstrated that PCV2 Cap proteins produced in Escherichia coli (E. coli) can form virus-like particles (VLPs) in vitro, and nuclear localization signal peptides (NLS) play a pivotal role in stabilizing PCV2 VLPs. Recently, PCV2d has emerged as an important strain within the PCV2 epidemic. In this study, we systematically optimized the PCV2d Cap protein and successfully produced intact PCV2d VLPs containing NLS using E. coli. The recombinant PCV2d Cap protein was purified through affinity chromatography, yielding 7.5 mg of recombinant protein per 100 ml of bacterial culture. We augmented the conventional buffer system with various substances such as arginine, ß-mercaptoethanol, glycerol, polyethylene glycol, and glutathione to promote VLP assembly. The recombinant PCV2d Cap self-assembled into VLPs approximately 20 nm in diameter, featuring uniform distribution and exceptional stability in the optimized buffer. We developed the vaccine and immunized pigs and mice, evaluating the immunogenicity of the PCV2d VLPs vaccine by measuring PCV2-IgG, IL-4, TNF-α, and IFN-γ levels, comparing them to commercial vaccines utilizing truncated PCV2 Cap antigens. The HE staining and immunohistochemical tests confirmed that the PCV2 VLPs vaccine offered robust protection. The results revealed that animals vaccinated with the PCV2d VLPs vaccine exhibited high levels of PCV2 antibodies, with TNF-α and IFN-γ levels rapidly increasing at 14 days post-immunization, which were higher than those observed in commercially available vaccines, particularly in the mouse trial. This could be due to the fact that full-length Cap proteins can assemble into more stable PCV2d VLPs in the assembling buffer. In conclusion, our produced PCV2d VLPs vaccine elicited stronger immune responses in pigs and mice compared to commercial vaccines. The PCV2d VLPs from this study serve as an excellent candidate vaccine antigen, providing insights for PCV2d vaccine research.

A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND - one of the most popular tools for function prediction - under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. This study emphasizes the critical role of search parameter settings in homology-based function transfer.

GPU-I-TASSER: a GPU accelerated I-TASSER protein structure prediction tool.

MOTIVATION: Accurate and efficient predictions of protein structures play an important role in understanding their functions. Iterative Threading Assembly Refinement (I-TASSER) is one of the most successful and widely used protein structure prediction methods in the recent community-wide CASP experiments. Yet, the computational efficiency of I-TASSER is one of the limiting factors that prevent its application for large-scale structure modeling. RESULTS: We present I-TASSER for Graphics Processing Units (GPU-I-TASSER), a GPU accelerated I-TASSER protein structure prediction tool for fast and accurate protein structure prediction. Our implementation is based on OpenACC parallelization of the replica-exchange Monte Carlo simulations to enhance the speed of I-TASSER by extending its capabilities to the GPU architecture. On a benchmark dataset of 71 protein structures, GPU-I-TASSER achieves on average a 10× speedup with comparable structure prediction accuracy compared to the CPU version of the I-TASSER. AVAILABILITY AND IMPLEMENTATION: The complete source code for GPU-I-TASSER can be downloaded and used without restriction from https://zhanggroup.org/GPU-I-TASSER/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions.

Sequence-based contact prediction has shown considerable promise in assisting non-homologous structure modeling, but it often requires many homologous sequences and a sufficient number of correct contacts to achieve correct folds. Here, we developed a method, C-QUARK, that integrates multiple deep-learning and coevolution-based contact-maps to guide the replica-exchange Monte Carlo fragment assembly simulations. The method was tested on 247 non-redundant proteins, where C-QUARK could fold 75% of the cases with TM-scores (template-modeling scores) ≥0.5, which was 2.6 times more than that achieved by QUARK. For the 59 cases that had either low contact accuracy or few homologous sequences, C-QUARK correctly folded 6 times more proteins than other contact-based folding methods. C-QUARK was also tested on 64 free-modeling targets from the 13th CASP (critical assessment of protein structure prediction) experiment and had an average GDT_TS (global distance test) score that was 5% higher than the best CASP predictors. These data demonstrate, in a robust manner, the progress in modeling non-homologous protein structures using low-accuracy and sparse contact-map predictions.