mRNA MERS computational research support
Research assistant internship at Pusat Riset Bioteknologi dan Bioinformatika UNPAD, translating molecular dynamics outputs into clear RMSD, RMSF, and SASA interpretation for MERS-CoV mRNA research.
From simulation output to research-ready interpretation
This research assistant work centered on making molecular dynamics output easier to interpret: comparing four MERS-CoV molecular systems, reading RMSD stability behavior, locating RMSF flexibility peaks, and summarizing SASA side-chain exposure tables.
How the raw files became portfolio evidence
The source files included an Excel workbook of trajectory data, an mRNA MERS plot report, and revised SASA tables. I reorganized those materials into a cleaner sequence that shows research discipline: data handling, visualization, interpretation, and reporting.
Collect outputs
Organized RMSD and RMSF trajectory outputs from native, mutated, optimized mutant, and optimized native MERS systems.
Build comparison plots
Converted spreadsheet trajectories into clean visual comparisons so structural drift and residue flexibility could be read quickly.
Screen accessibility
Condensed SASA residue tables for tetanus, HBsAg, and diphtheria references into exposure patterns and standout residues.
Write interpretation
Turned numerical outputs into research notes that explain stability, flexibility, and exposed side-chain behavior in plain language.
Stability comparison across four systems
RMSD traces were used to compare structural drift across the 100 ns simulation window. The optimized native system showed the lowest late-stage deviation, while optimized mutant and mutated systems moved into higher-deviation profiles.
Four MERS-CoV systems plotted together from the Excel trajectory data.
| System | Final RMSD | Late avg. | RMSF peak | Portfolio reading |
|---|---|---|---|---|
| Optimized native | 2.76 | 2.58 | 7.05 at residue 3145 | Most compact trajectory in the comparison, with a lower late-stage RMSD average than the native and mutated systems. |
| Native MERS | 5.15 | 5.04 | 26.92 at residue 3621 | Shows larger structural drift and a strong terminal flexibility spike that needs careful interpretation in reporting. |
| Native mutated | 5.41 | 5.25 | 6.88 at residue 1674 | Mutation raised the late-stage deviation profile compared with optimized native and preserved several flexible regions. |
| Optimized mutant | 5.81 | 5.70 | 6.75 at residue 1198 | Highest final RMSD in this set, suggesting the optimized mutant moved into a more shifted conformational ensemble. |
Residue flexibility made readable
RMSF output helped separate global stability from local movement. The portfolio version highlights flexible residue regions and peak positions so the result can be understood without opening the raw spreadsheet.
Small-multiple plots show residue-level fluctuation and peak regions.
Turning residue tables into screening insight
The revised SASA tables were condensed into exposure patterns for tetanus, HBsAg, and diphtheria references. This makes side-chain accessibility visible as a screening story, not just a dense table.
| Antigen | Exposure pattern | Notable residues | Portfolio reading |
|---|---|---|---|
| Tetanus | 42 highly exposed, 29 moderate, 54 buried | HIE983, HID263 | Broadest exposure profile; useful for discussing accessible side-chain candidates. |
| HBsAg | 11 highly exposed, 9 moderate, 28 buried | CYS76, CYS107, CYS138 | Mixed exposure pattern with several cysteine residues repeatedly visible across criteria. |
| Diphtheria | 1 highly exposed, 0 moderate, 19 buried | CYX186, CYX461 | Mostly buried profile, with CYX186 standing out as the main accessible residue in the table. |
Research value
This case study shows practical computational chemistry skills: organizing trajectory outputs, reading stability indicators, comparing variants, and explaining technical results for scientific reporting.
Tools and strengths
Relevant tools include AMBER/AmberTools, Biovia Discovery Studio, UCSF Chimera, VMD, Excel-based data handling, and technical report writing for molecular modeling outputs.
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