Group Final Project

From Brainstorming to Project Direction

During our first group brainstorming session, We kept talking about the L protein stability, how the MS2 L protein interacts with the bacterial chaperone DnaJ, whether specific amino acid changes affect lysis behavior, and how modifying certain regions could potentially improve protein function. We also discussed improving lysis efficiency and helping MS2 overcome possible host resistance.

stability alone was too broad. The bigger question became: Can we help the L protein maintain function while reducing its dependence on host processing? This became important because proteins that cannot maintain proper folding or structural integrity may lose function before successfully carrying out membrane-associated activity.

Previous studies identified Lodj mutants that could overcome DnaJ-related lysis defects, suggesting that modifications in specific regions of the L protein may influence how strongly the protein depends on host processing.

Goal: For this project, we want to investigate whether selected modifications can help the MS2 L protein maintain function while preserving the biological properties required for lysis.

• maintain folding robustness • preserve structural persistence • maintain membrane-associated activity • potentially reduce reliance on DnaJ-assisted processing • maintain or improve lysis efficiency

Previous studies showed that relatively small amino acid changes in the MS2 L protein can significantly affect lysis behavior and host dependence. To build on this, we will first identify regions predicted to contribute to membrane-associated activity, DnaJ-related processing, and overall structural stability while distinguishing regions that may tolerate modification. We will use Lodj variants as a reference point because previous studies suggest that changes in these variants can influence how strongly the L protein depends on DnaJ-assisted processing.

Sequence conservation analysis: We will compare available MS2 L protein sequences and related lysis proteins to identify:

• highly conserved residues that are likely important for function • less conserved regions that may better tolerate modification • candidate regions for mutation selection

Because highly conserved residues are often linked to important biological functions, these regions may be less suitable targets for engineering. Less conserved regions may provide safer locations for introducing modifications while minimizing disruption of essential activity.

Structural prediction and visualization: We will use AlphaFold, ESMFold & ChimeraX to examine:

• predicted structural organization of the L protein • membrane-associated regions • candidate residues selected from conservation analysis • flexible regions that may contribute to instability

The goal is not only to visualize the protein structure, but also to determine whether candidate mutations are located in regions likely to tolerate modification without disrupting membrane-associated activity.

Computational stability analysis: Computational prediction tools will be used to compare wild-type and candidate mutant designs and estimate whether modifications are predicted to stabilize or destabilize protein behavior before moving into experimental testing.

Note: This project direction was built from ideas discussed during our first group brainstorming session and our shared discussion notes. Since we had limited time for additional discussions, I continued developing and expanding the project while keeping our original ideas and overall direction as the foundation of the work.

References

1. Chamakura KR, Tran JS, Young R. (2017). MS2 Lysis of Escherichia coli Depends on Host Chaperone DnaJ. Journal of Bacteriology, 199(12): e00058–17.
2. Chamakura KR et al. (2017). Mutational Analysis of the MS2 Lysis Protein L. Microbiology.
3. Mezhyrova J, et al. (2023). In vitro Characterization of the Phage Lysis Protein MS2-L. Microbiome Research Reports, 3:28.
4. Jumper J, Evans R, Pritzel A, et al. (2021). Highly Accurate Protein Structure Prediction with AlphaFold. Nature, 596:583–589.
5. Lin Z, Akin H, Rao R, et al. (2023). Evolutionary-scale Prediction of Atomic-Level Protein Structure with a Language Model. Science, 379(6637):1123–1130.