| 1/24 | lecture | CHARMM introduction |
| 1/26 | lab | CHARMM lab I - basics |
| 2/02 | lecture | Simplified protein models |
| 2/07 | lecture | MONSSTER introduction |
| 2/09 | lab | MONSSTER lab |
| 2/21 | lecture | Bonding and Electrostatics |
| 2/23 | lecture | Biomolecular force fields |
| 2/28 | lecture | Long-range electrostatics |
| 3/02 | lecture | Explicit water models and ions |
| 3/16 | lecture | Implicit solvent in simulations |
| 3/21 | lecture | Practical simulation of biomolecules |
| 3/23 | lab | CHARMM lab II - biomolecular simulations |
| 3/30 | lab | CHARMM lab III - continuum electrostatics |
| 4/4 | lecture | Analysis of stretching simulations |
| 4/6 | lab | CHARMM lab IV - stretching simulations |
| 4/18 | lecture | Advanced sampling methods |
| 4/20 | lab | CHARMM lab V - advanced sampling |
| 4/4 | paper introduction |
| 4/4-4/11 | paper selection |
| 4/13 | project setup lab session |
| 5/4 | project results presentation |
| 2:00 - 2:20 | Yi Zheng | Computational Alanine Scannning of hGH-Receptor Complex |
| 2:20 - 2:40 | Jiwu Liu | |
| 2:45 - 3:05 | Matt Tonero | Effect of G40R Mutation on hSRY-DNA Binding |
| 3:05 - 3:25 | Kai-Chun Chen | |
| 3:40 - 4:00 | Steve Waldauer | Unfolding of beta2-microglobulin |
| 4:00 - 4:20 | Brian Connelly | |
| 4:25 - 4:45 | Neil Aaronson | Solvation and Electrostatics in MD Simulations of Ubiquitin |
| 4:45 - 5:05 | Andrew Stumpff-Kane | |
| 1. | M. Falconi, F. Venerini, A. Desideri: A spectroscopic and molecular dynamics study of native and of mutant Xenopus laevis Cu,Zn superoxide dismutase: mechanistic consequences of replacing four charged amino acids on the 'electrostatic' loop. Biophysical Chemistry (1998) 75, 235-248
PDF Project A: Repeat explicit solvent simulation of wildtype X. laevis superoxide dismutase dimer with counterions over 300 ps with "state-of-the-art" simulation methods. Project B: Carry out the same simulation as in A but with a mutated enzyme where residues LYS120, ASP130, GLU131, and LYS134 are changed to Leu, Gln, Gln, and Thr, respectively. A+B: Calculate RMSD as a function of simulation (Fig. 3d) and compare dynamic cross-correlation maps shown in Fig. 6a and b between the wildtype and mutated enzyme. |
| 2. | T. Fox, P. Kollman: The Application of Different Solvation and Electrostatic Models in Molecular Dynamics Simulations of Ubiquitin: How Well Is the X-Ray Structure "Maintained"?. Proteins (1996), 25, 315-334
PDF Stumpff-Kane/Aaronson Project A: Repeat explicit solvent molecular dynamics simulations of ubiquitin with particle-mesh Ewald, an 8 Angstrom cutoff, and a 14 Angstrom cutoff, each over 300 ps. Calculate the RMSD from the crystal structure and compare with Fig. 1. Project B: Run molecular dynamics simulations of ubiquitin with implicit solvent (GBMV), also over 300 ps, and compare with results from Project A. Then, rescore both the implicit and explicit trajectories with an MMGB/SA-based free energy estimate. How do the energetics differ? |
| 3. | D. Mohanty, B. Dominy, A. Kolinski, C. Brooks, J. Skolnick: Correlation Between Knowledge-Based and Detailed Atomic Potentials: Application to the Unfolding of the GCN4 Leucine Zipper. Proteins (1999), 35, 447-452
PDF Project A: Starting from the experimental structure for the GCN4 leucine zipper use MONSSTER to generate an ensemble of 200 structures with RMSDs ranging from 1 to 10 Angstrom. Plot the lattice energy vs. RMSD and compare with Fig. 1. Reconstruct all-atom models for each of the decoys and minimize the structures with CHARMM. Project B: Use the minimized structures from A score them with an all-atom energy function (MMPB/SA or MMGB/SA). Plot the all-atom energy score vs. RMSD and also vs. the lattice energy from A. Compare with Figs 2 and 3. A+B: Repeat the experiment, but this time take a decoy near 5 Angstrom as the starting structure. Compare the results. |
| 4. | H. Lu, B. Isralewitz, A. Krammer, V. Vogel, K. Schulten: Unfolding of Titin Immunoglobulin Domains by Steered Molecular Dynamics Simulation, Biophysical Journal (1998), 75, 662-671
PDF Project A: Carry out forced unfolding simulations of titin from the native structure with steered molecular dynamics (C-27-0.5 in the paper) with implicit solvent (GBMV). Compare the force extension profile with Fig. 5. Use the results from B in order to calculate a PMF profile according to Schulten's approximation to the Jarzynski equality. Project B: Run a short MD simulation of titin with implicit solvent in order to generate an ensemble of structures for the native state. Pick 20 representative structures and repeat the forced unfolding simulations as in A |
| 5. | S. Huo, I. Massova, P. Kollman: Computational Alanine Scanning of the 1:1 Human Growth Hormone-Receptor Complex. J. Comp. Chem. (2002), 23, 15-27
PDF Zheng/Liu Project A: Run a 50 ps molecular dynamics simulation of the human growth hormone-receptor complex with explicit solvent as in the paper. Compare with Fig 2a. Project B: Evaluate relative binding energies according to Eq. 1-4 for alanine mutations of the following residues: R70, W80, S98, S102, K167, V171. Compare the results with the simulation and experimental data given in the paper. |
| 6. | Y. Tang, L. Nilsson: Effect of G40R Mutation on the Binding of Human SRY Protein to DNA: A Molecular Dynamics View. Proteins (1999), 35, 101-113
PDF Chen/Tonero Project A: Setup and run 300 ps of the wildtype hSRY-HMG-DNA complex as in the paper with explicit solvent and counterions. Compare the RMSD with Fig. 1. Project B: Setup and run 300 ps of the G40R mutation as in A. Compare the RMSD with Fig. 1. A+B: Compare the structures at the end of the simulations and characterize the structural differences. |
| 7. | J. Skolnick, A. Kolinski, A. Ortiz: MONSSTER: A Method for Folding Globular Proteins with a Small Number of Distance Restraints. J. Mol. Biol. (1997), 265, 217-241
PDF Project A: Carry out ab initio folding of thioredoxin with MONSSTER using the restraints given in Table 3. Run 100 simulated annealing runs with suitable parameters and compare results with Table 3. Repeat the simulations with using only every second restraint and also with only every fourth restraint. Compare the ability to find the native topology with the first run where all of the restraints are used. Project B: Carry out the same kind of simulation but for the flavodoxin example (Table 4). |
| 8. | B. Huang, Y. Xia, M. Zhao, F. Li, X. Liu, Y. Ji, C. Song Distribution patterns and controllable transport of water inside and outside charged single-walled carbon nanotubes.. J. Chem. Phys. (2005), 122, 084708
PDF Project A: Set up the force field in CHARMM based on the parameters from the paper in order to carry out classical simulations of single-walled carbon nanotubes. Setup a neutral (10,10) SWNT tube with a length of 28 Angstroms as in the paper. Minimize and run a 20 ps simulation in vacuum in order to test the parameters. Project B: Solvate only the inside of the nanotube with explicit water molecules and carry out a simulation in implicit solvent otherwise for 100 ps. Analyze the distribution of water inside the nanotube and compare with Fig. 2 top. |
| 9. | B. Ma, R. Nussinov Molecular dynamics simulations of the unfolding of beta2-microglobulin and its variants. Protein Engineering (2003), 16, 561-575
PDF Connelly/Waldauer Project A: Set up an implicit solvent simulation (GBMV) of the native b2-microglobulin structure. Run 3 unfolding simulations at 800K over 1 ns each. Calculate the RMSD as in Fig. 2E and compare the unfolding pathways with the results from the paper. Project B: Carry out the same simulation as in A for the truncated delta N6 variant and compare results. |
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