🧬 Prot2Vac: mRNA Vaccine Design from Human Protein Sequences

Introduction

Prot2Vac is a web-based platform builds on this innovation by offering a fully automated pipeline for mRNA vaccine design. Starting with a simple protein, the tools predicts epitopes using a widely used database, IEDB. Next, the tool performs reverse translation, species-specific codon optimization, and modular antigen construction. It then appends key mRNA regulatory elements such as the 5′ cap, Kozak sequence, untranslated regions (UTRs), and a poly(A) tail. To ensure structural integrity and stability, the platform runs RNAfold to predict secondary structure, compute the minimum free energy (MFE), and generate a visual representation of the mRNA molecule.

Users can analyze GC content, view the full construct, and download both the final sequence and a detailed report. This application simplifies complex bioinformatics processes into an intuitive workflow, making it an essential resource for researchers, educators, and developers involved in vaccine design and synthetic biology.

Pipeline Overview

Key Features

• ✅ Predicts MHC-I and MHC-II epitopes using pretrained IEDB deep learning models
• ✅ Supports allele-specific predictions (e.g., HLA-A*02:01, HLA-DRB1*07:01)
• ✅ Ranks predicted epitopes by IC50 and percentile values
• ✅ Offers customization with signal peptides, spacers, and adjuvants
• ✅ Appends essential mRNA elements: 5′ cap, 5′ UTR, Kozak sequence, 3′ UTR, poly(A) tail
• ✅ Performs reverse translation & codon optimization using Kazusa tables
• ✅ Predicts mRNA secondary structure and MFE via RNAfold
• ✅ Outputs full sequence summary, dot-bracket notation, and structural visualization

🖼️ Visuals

Technologies Used

• Python
• IEDB Tools API — MHC epitope prediction
• Kazusa Codon Usage — Reverse translation & optimization
• ViennaRNA RNAfold — RNA secondary structure prediction
• Flask for backend API and React frontend

📥 Input Requirements

• ✅ A protein sequence (FASTA or plain string)
• ✅ User-selected:
  • MHC Class I allele (e.g., HLA-A*02:01)
  • MHC Class II allele (e.g., HLA-DRB1*07:01)
• ✅ Optional:
  • Signal peptide (e.g., tissue-specific)
  • Adjuvants (e.g., defensins)
  • Spacers (e.g., GPGPG)

📤 Output

• ✅ Codon-optimized mRNA vaccine sequence
• ✅ RNA secondary structure in dot-bracket notation
• ✅ Minimum Free Energy (MFE) of the folded RNA
• ✅ Output summary:
  • Selected epitopes and MHC alleles
  • Regulatory and immunological components used
  • Full final sequence with annotations

Final mRNA Vaccine Construct
m7G GGGAAAUUUCUUAUUGCAGCCGCCAC GCCACCAUGG [Signal] [Adjuvant] [Spacer] [Epitope] ... UAA UUAUUUUAUUAAGCUAUAAA [Poly(A)]

MFE: -33.45 kcal/mol
Dot-Bracket: (((..((....))...)))...

🔭 Future Work

• Support multiple epitopes across alleles
• Add B-cell epitope prediction (linear & conformational)
• Add antigenicity, allergenicity, and toxicity prediction
• Allow codon optimization for other host organisms (e.g., E. coli, mouse)

📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

🙌 Acknowledgments

• IEDB Analysis Resource – http://tools.iedb.org/
• ViennaRNA Package – https://www.tbi.univie.ac.at/RNA/
• Kazusa Codon Usage Database – https://www.kazusa.or.jp/codon/