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Atomistic Simulation - GDAFM03

Title:	Atomistická simulace
Guaranteed by:	Department of Biophysics and Physical Chemistry (16-16110)
Faculty:	Faculty of Pharmacy in Hradec Králové
Actual:	from 2023
Semester:	both
Points:	0
E-Credits:	0
Examination process:
Hours per week, examination:	0/0, Ex [HT]
Capacity:	winter:unknown / unknown (unknown) summer:unknown / unknown (unknown)
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
Key competences:
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Level:
Note:	course is intended for doctoral students only enabled for web enrollment you can enroll for the course in winter and in summer semester

Guarantor:	Eugen Hruška, Ph.D.

Examination dates WS schedule

Annotation -

The goal of this course is to teach atomistic-level computational methods useful for pharmaceutical science and drug development. Students will learn the principles and practical applications of atomistic simulations. The course teaches how to predict experimental properties and critically interpret the results of atomistic simulations.

Last update: Hruška Eugen, Ph.D. (25.09.2024)

Course completion requirements -

Demonstrate knowledge of the principles and ability to perform atomistic simulations.

Last update: Hruška Eugen, Ph.D. (15.08.2023)

Literature -

Obligatory:

Sydow, Dominique, et al. "TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research." Nucleic Acids Research 50.W1 (2022): W753-W760. https://projects.volkamerlab.org/teachopencadd

Drug Discovery Computing Techniques, PharmSci 175/275, David Mobley's course [online]. Dostupné z: https://github.com/MobleyLab/drug-computing

Recommended:

Ahdritz, Gustaf, et al. "OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization." Nature Methods (2024): 1-11. https://colab.research.google.com/github/aqlaboratory/openfold/blob/main/notebooks/OpenFold.ipynb

Eastman, Peter, et al. "OpenMM 7: Rapid development of high performance algorithms for molecular dynamics." PLoS computational biology 13.7 (2017): e1005659. https://openmm.github.io/openmm-cookbook/latest/tutorials

Scherer, Martin K., et al. "PyEMMA 2: A software package for estimation, validation, and analysis of Markov models." Journal of chemical theory and computation 11.11 (2015): 5525-5542. http://www.emma-project.org/latest/tutorial.html

RDKit: Open-source cheminformatics. [online]. Dostupné z: https://rdkit.org/docs/Cookbook.html

Nash, Jessica A., et al. "MolSSI Education: Empowering the Next Generation of Computational Molecular Scientists." Computing in Science & Engineering 24.3 (2022): 72-76. https://education.molssi.org/resources.html

Last update: Hruška Eugen, Ph.D. (10.10.2024)

Syllabus -

Molecular representations:

molecular graph, conformations, SMILES

Quantum mechanics:

Schrödinger equation, Hartree-Fock method, ground state,

potential energy surface, ab initio forces, geometry optimization

Classical molecular dynamics:

equation of motion, force field, Verlet algorithm, system preparation,

periodic boundary conditions, solvation, thermostat, barostat, equilibration

Molecular dynamics analysis:

root-mean-square deviation (RMSD), hydrogen bond analysis,

data dimension reduction, kinetic models, Markov model, binding affinity prediction

Introduction to basic experiments, spectroscopic methods, and chemical equilibrium models suitable for studying interactions between host molecule and ligand (i.e., host-guest complexation models). Determination of the equilibrium binding constant of the formed complex, its thermodynamic interpretation, and temperature dependence

Last update: Hruška Eugen, Ph.D. (10.10.2024)