Few months ago, when I first started learning Molecular Dynamics (MD) simulations, I thought running a 100 ns simulation was the hardest part.
Later I realized, the real science actually begins after the simulation ends.
The trajectory file is not just a movie of atoms moving. Hidden inside it are answers about:
• protein stability
• flexibility
• conformational changes
• ligand binding
• allosteric communication
• hydration effects
• oligomeric interactions
and even possible biological mechanisms.
Over time, while working on structural biology projects involving cryo-EM, protein dynamics, and computational analysis, I compiled this visual summary of some of the most important MD analysis plots used in biomolecular simulations.
This figure includes 16 commonly used analyses:
1️⃣ RMSD - overall structural stability
2️⃣ RMSF - residue flexibility mapping
3️⃣ Radius of Gyration - compactness of protein
4️⃣ Hydrogen Bond Analysis - interaction stability
5️⃣ SASA - solvent exposure changes
6️⃣ DSSP - secondary structure evolution
7️⃣ PCA - dominant collective motions
8️⃣ Free Energy Landscape - conformational states
9️⃣ Distance Analysis - domain/ligand movements
🔟 Contact Maps - residue interaction networks
1️⃣1️⃣ DCCM - correlated residue motions
1️⃣2️⃣ MM-PBSA/MM-GBSA - binding free energy estimation
1️⃣3️⃣ Cluster Analysis - dominant conformational populations
1️⃣4️⃣ Water Density Analysis - hydration behavior
1️⃣5️⃣ Ramachandran Plot - stereochemical validation
1️⃣6️⃣ Interface H-Bond Analysis - oligomeric interaction stability
I also added commonly used software/tools for each analysis including:
Desmond, Maestro, Schrödinger Suite, GROMACS, CPPTRAJ, VMD, Python, R, and GraphPad Prism.
I hope this infographic will be useful for students, researchers, and anyone entering the field of computational structural biology or MD simulations.
Would love to hear your feedback:
• Which MD analysis do you use the most in your work?
• Are there any important analyses you think should also be included?
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