Supantha Dey, Jennifer Bruner, Maria Brown, Mike Roof, Ratul Chowdhury
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引用次数: 0
Abstract
Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) have been a critical threat to swine health since 1987 due to its high mutation rate and substantial economic loss over half a billion dollar in USA. The rapid mutation rate of PRRSV presents a significant challenge in developing an effective vaccine. Even though surveillance and intervention studies have recently (2019) unveiled utilization of PRRSV glycoprotein 5 (GP5; encoded by gene) to induce immunogenic reaction and production of neutralizing antibodies in porcine populations, the future viral generations can accrue escape mutations. In this study we identify 63 porcine-PRRSV protein-protein interactions which play primary or ancillary roles in viral entry and infection. Using genome–proteome annotation, protein structure prediction, multiple docking experiments, and binding energy calculations, we identified a list of 75 epitope locations on PRRSV proteins crucial for infection. Additionally, using machine learning-based diffusion model, we designed 56 stable immunogen peptides that contain one or more of these epitopes with their native tertiary structures stabilized through optimized N– and C–terminus flank sequences and interspersed with appropriate linker regions. Our workflow successfully identified numerous known interactions and predicted several novel PRRSV-porcine interactions. By leveraging the structural and sequence insights, this study paves the way for more effective, high-avidity, multi-valent PRRSV vaccines, and leveraging neural networks for immunogen design.
期刊介绍:
Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:
Structure and function of proteins, nucleic acids and other macromolecules
Structure and function of multi-component complexes
Protein folding, processing and degradation
Enzymology
Computational and structural studies of plant systems
Microbial Informatics
Genomics
Proteomics
Metabolomics
Algorithms and Hypothesis in Bioinformatics
Mathematical and Theoretical Biology
Computational Chemistry and Drug Discovery
Microscopy and Molecular Imaging
Nanotechnology
Systems and Synthetic Biology