ssRNA Virus Capsid Stability Prediction

ISEF Category: Microbiology

Subcategory: Virology  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Some viruses can survive on surfaces or in water far longer than others. The shell around the virus, called the capsid, helps decide how tough it is. You can test whether computer-predicted capsid stability matches real-world persistence data from papers. That turns public sequences into a research question with real virology value.

What Is It?

A virus capsid is the protein shell that protects the viral genome. Think of it like a shipping box. If the box holds together well, the virus may tolerate heat, drying, or other stress better than a fragile one. Your project asks whether that idea shows up in the data for ssRNA viruses.

You start with public protein sequences, then build predicted 3D structures with ColabFold. Next, you use FoldX to estimate interface ΔΔG, which is a way to measure how much a mutation or interaction change may affect protein stability. Lower or more negative values often suggest a tighter, more stable interaction, while higher values can point to a weaker one. Then you compare those predictions with published reports of how long each virus stays infectious in the environment.

Why This Is a Good Topic

This topic works well because it is measurable, data-rich, and connected to a real biological question. You can test a clear hypothesis, use public databases, and compare your prediction against published persistence data instead of waiting for lab infections. You will also learn sequence analysis, structure prediction, protein stability scoring, and basic correlation statistics, all of which matter in modern virology research.

Research Questions

• How does predicted capsid interface stability relate to reported environmental persistence across ssRNA viruses?
• What is the effect of capsid protein length on FoldX-predicted interface ΔΔG values?
• Does the structural symmetry class of a capsid predict higher stability scores?
• To what extent do RNA genome type and envelope status change the relationship between capsid stability and persistence?
• Which virus family shows the strongest match between predicted capsid stability and literature-based persistence?
• How does removing outlier viruses change the correlation between predicted stability and environmental persistence?

Basic Materials

• Laptop or desktop computer with enough memory for structure prediction
• Stable internet connection
• Public virus sequence databases such as NCBI Virus and UniProt
• Spreadsheet software such as Google Sheets or Excel
• Reference manager such as Zotero
• Graphing software such as Google Sheets, Excel, or Prism if available
• Notes document for tracking virus IDs, accession numbers, and literature sources.

Advanced Materials

• Access to a workstation or university server with a GPU or strong CPU support
• ColabFold notebook access
• FoldX software access through an academic installation
• Python environment with pandas, scipy, matplotlib, and seaborn
• Structural visualization software such as PyMOL or UCSF ChimeraX
• Sequence alignment tools such as MAFFT
• Literature database access through PubMed and journal portals.

Software & Tools

• ColabFold: Predicts protein structures from sequence so you can compare capsid models across viruses.
• FoldX: Estimates interface stability changes and mutation effects for protein complexes.
• Python: Organizes your dataset, runs statistics, and makes plots.
• PyMOL: Lets you inspect capsid interfaces and check whether the model looks reasonable.
• Zotero: Tracks your papers, persistence notes, and source quality.

Experiment Steps

1. Define your virus set and set strict rules for which ssRNA viruses you will include.
2. Build a single data table that links each virus sequence, capsid protein, and literature persistence measure.
3. Predict or collect one structural model per virus and record model quality metrics before analysis.
4. Decide how you will extract a comparable stability score from each capsid interface.
5. Plan your statistical test for correlation and your method for handling outliers, missing values, and family-level clustering.
6. Pre-register the analysis order so you do not change the rules after seeing the results.

Common Pitfalls

• Mixing viruses with very different envelope status, which can blur the link between capsid stability and persistence.
• Using persistence values from papers that measure different conditions, which makes the comparison uneven.
• Trusting every predicted structure equally, which can hide low-confidence models with bad interfaces.
• Comparing raw ΔΔG values across unrelated proteins without normalizing for capsid size or assembly state.
• Pulling literature numbers from summaries instead of the original paper, which can introduce citation errors.

What Makes This Competitive

A strong version of this project goes beyond a simple correlation plot. You would control for virus family, envelope status, and assay type, then test whether the relationship still holds. You could also compare several ways of scoring stability, not just one FoldX output. A competitive entry asks a sharper question, uses cleaner data rules, and shows that the signal survives tougher analysis.

Project Variations

• Compare predicted capsid stability in only plant-infecting ssRNA viruses, then test whether persistence patterns still match.
• Swap FoldX interface scores for Rosetta stability estimates and see whether the conclusion stays the same.
• Focus on one virus family, such as Picornaviridae or Caliciviridae, and test whether within-family stability predicts persistence better than across all viruses.

Learn More

• NCBI Virus: Search complete viral genomes and protein sequences for public ssRNA virus data.
• PubMed: Search review articles and primary papers on capsid stability and environmental persistence.
• NCBI Bookshelf: Read free molecular biology and virology background chapters.
• MIT OpenCourseWare: Find free lectures on bioinformatics, protein structure, and statistical analysis.
• USGS National Wildlife Health Center publications: Review public reports that discuss virus survival and environmental context.
• NIH National Library of Medicine resources: Explore free access points for biomedical background and literature searching.