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.
Institution profile
Pusat Riset Bioteknologi dan Bioinformatika
Pusat Riset Bioteknologi dan Bioinformatika UNPAD is a research center at Padjadjaran University dedicated to advancing life sciences through computational approaches. The center conducts research in molecular modeling, protein engineering, drug discovery, genomics analysis, and bioinformatics pipeline development, collaborating with faculty members and graduate students across multiple departments.
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. |
Impact & skills gained
This research internship allowed me to contribute meaningful analysis to an active computational biology project while developing advanced skills in molecular simulation and scientific data interpretation.
Impact on the research team
- Organized and visualized trajectory data from 4 MERS-CoV systems, accelerating the team's interpretation workflow.
- Processed 10,000+ RMSD frames per system into clean comparison plots for research publications.
- Condensed 3 SASA antigen tables into accessible exposure summaries, reducing manual analysis time.
- Generated research-ready visualizations that were used in internal team presentations and progress reports.
Technical skills
- AMBER/AmberTools molecular dynamics simulation workflows
- RMSD, RMSF, and SASA trajectory data analysis
- Protein structure visualization (Biovia Discovery Studio, UCSF Chimera, VMD)
- Linux-based high-performance computing environments
- Scientific data processing with Excel and Python
Professional skills
- Scientific report writing and research documentation
- Interpreting computational results for non-specialist audiences
- Research collaboration and team communication
- Critical analysis of molecular simulation outputs
- Self-directed learning in bioinformatics methodologies