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February 2, 20260 citationsOpen Access

Targeting Nipah Virus Phosphoprotein with mRNA: A Computational Framework for Rapid Vaccine Design

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RARami Alkhaleeli

Key Points

  • The research aims to create a rapid design framework for mRNA vaccines targeting the Nipah virus phosphoprotein.
  • Developed a multi-checkpoint in-silico pipeline for mRNA vaccine construction.
  • Analyzed raw protein FASTA sequences for antigen scoring and epitope density.
  • Conducted conservation assessment and human codon optimization.
  • Evaluated RNA structure and manufacturing feasibility.
  • Achieved a high composite vaccine score for Nipah virus phosphoprotein.
  • Demonstrated strong predicted antigenicity and dense T-cell epitope coverage.
  • Produced an mRNA construct with appropriate GC content and human-compatible codon usage.

Abstract

This preprint presents a computational framework for rapid mRNA vaccine design demonstrated using the Nipah virus phosphoprotein (P protein) as a model antigen. The study introduces a multi-checkpoint in-silico pipeline that transforms raw protein FASTA sequences into research-grade, manufacturing-compatible mRNA vaccine constructs through integrated antigen scoring, epitope density analysis, conservation assessment, human codon optimization, RNA structural evaluation, and manufacturing feasibility checks. Using this framework, the Nipah virus phosphoprotein achieved a high composite vaccine score driven by strong predicted antigenicity, dense T-cell epitope coverage, and favorable expression properties. The optimized mRNA construct exhibits appropriate GC content, codon usage compatible with human translation machinery, and structural features consistent with established mRNA vaccine architectures. This work is intended for educational and computational research purposes only and does not represent an experimentally validated or clinically approved vaccine. The framework provides a standardized approach for rapidly prioritizing and designing mRNA vaccine candidates against emerging viral threats and supports pandemic preparedness through open, reproducible computational methods.

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Cite This Study

Rami Alkhaleeli (2026) studied this question.

synapsesocial.com/papers/6980ff49c1c9540dea812243https://doi.org/10.5281/zenodo.18433821
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