* Load is given in academic hour (1 academic hour = 45 minutes)
1 To explain basic principles of computational methods for investigation of biochemical systems.
2 To visualize biological macromolecules using program VMD.
3 To prepare and to conduct molecular dynamics simulations.
4 To conduct analysis of molecular dynamics (MD) simulations.
5 To explain the results of analysis of MD simulations.
6 To define basic simulation techniques for investigation of biochemical systems
1 Introduction to structural computational biophysics. Basic concepts of molecular biophysics.
2 Examples of computational investigation of biochemical/biological systems.
3 Basic principles of computational biochemistry methods. Differences between force field based methods and quantum mechanics methods.
4 Force field. Parametrization. Elementary statistical mechanics.
5 Molecular mechanics. Optimization algorithms.
6 Physical principles of biomolecular simulations I: degrees of freedom, interactions and generation of configurations.
7 Physical principles of biomolecular simulations II: algorithms, thermostats, boundary conditions.
8 Implementation of MD algorithms: from electrostatics and solvent treatment to choice of force fields.
9 Analysis of molecular dynamics trajectories.
10 Monte Carlo algorithms for conformational search of biomacromolecules.
11 Random acceleration molecular dynamics.
14 Validation of computational results with experimental data. Examples from literature.
15 Computational pharmacology: molecular dynamics, docking algorithms. Achievements and open challenges.
1-4 Visualization of biomacromolecules. Sofware for visualization of biomacromolecules. Protein data bank (PDB) search.
5 Introduction of force filed.
6-9 System preparation for computational simulation.
10 Geometry optimization of protein structure.
11 Equilibration of system by MD simulation.
12 Analysis of equilibration simulation.
13-14 Preparation and conduction of MD simulation.
15-20 Analysis of MD simulation (visualization of trajectory, quantitative analysis).
21-22 Preparation of students projects. Discussion of projects and literature data related to projects.
23 Preparation and conduction of students projects.
24-30 Analysis of results of students projects.
- A.R. Leach Molecular Modelling: Principles and Applications, 2nd edition, Prentice Hall, 2001.
- C.J. Cramer Essentials of Computational Chemistry: Theories and Models, 2nd edition, J. Wiely, Chichester, 2004.