Cílem tohoto kurzu je naučit výpočetní metody na atomistické úrovni užitečné pro farmaceutickou vědu a vývoj léčiv. Studenti se seznámí s principy a praktickými aplikacemi používanými při atomistických simulacích. Kurz učí, jak předpovídat experimentální vlastnosti a kriticky interpretovat výsledky atomistických simulací.
Témata zahrnují molekulární reprezentaci (molekulární graf, konformace, SMILES), kvantovou mechaniku (Schrödingerova rovnice, Hartreeho–Fockova metoda, základní stav, povrch potenciální energie, ab initio síly, optimalizace geometrie), klasickou molekulární dynamiku (pohybová rovnice, silové pole, Verletův algoritmus, příprava systému, periodické okrajové podmínky, solvatace, termostat, barostat, ekvilibrace), analýzu molekulární dynamiky
(odmocnina střední kvadratické chyby (RMSD), analýza vodíkových vazeb, redukce dimenze dat, analýza hlavních komponent (PCA), kinetické modely, Markovův model), atomistické strojové učení (predikce molekulárních vlastností, predikce struktury proteinů, de novo návrh proteinů), strukturní modelování (molekulární dokování, detekce vazebných míst, predikce vazebné afinity).
Poslední úprava: Eugen Hruška, Ph.D. (15.08.2023)
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.
Topics include:
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, principal component analysis (PCA), kinetic models, Markov model.
Atomistic machine learning: molecular property prediction, protein structure prediction, de novo protein design.
Structural modeling: molecular docking, binding site detection, binding affinity prediction.
Podmínky zakončení předmětu -
Poslední úprava: Eugen Hruška, Ph.D. (15.08.2023)
Prokázání znalosti principů a schopnosti provádět atomistické simulace.
Poslední úprava: Eugen Hruška, Ph.D. (15.08.2023)
Demonstrate knowledge of the principles and ability to perform atomistic simulations.
Literatura -
Poslední úprava: Eugen Hruška, Ph.D. (15.08.2023)
Povinná:
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
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
Doporučená:
Ahdritz, Gustaf, et al. "OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization." Preprint (2022). https://colab.research.google.com/github/aqlaboratory/openfold/blob/main/notebooks/OpenFold.ipynb
Watson, Joseph L., et al. "De novo design of protein structure and function with RFdiffusion." Nature (2023): 1-3. https://colab.research.google.com/github/sokrypton/ColabDesign/blob/v1.1.1/rf/examples/diffusion.ipynb
Corso, Gabriele, et al. "Diffdock: Diffusion steps, twists, and turns for molecular docking." Preprint (2022). https://huggingface.co/spaces/simonduerr/diffdock
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
White, Andrew D. "Deep learning for molecules and materials." Living Journal of Computational Molecular Science 3.1 (2021): 1499-1499. https://dmol.pub/
Poslední úprava: Eugen Hruška, Ph.D. (15.08.2023)
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
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
Recommended:
Ahdritz, Gustaf, et al. "OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization." Preprint (2022). https://colab.research.google.com/github/aqlaboratory/openfold/blob/main/notebooks/OpenFold.ipynb
Watson, Joseph L., et al. "De novo design of protein structure and function with RFdiffusion." Nature (2023): 1-3. https://colab.research.google.com/github/sokrypton/ColabDesign/blob/v1.1.1/rf/examples/diffusion.ipynb
Corso, Gabriele, et al. "Diffdock: Diffusion steps, twists, and turns for molecular docking." Preprint (2022). https://huggingface.co/spaces/simonduerr/diffdock
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
White, Andrew D. "Deep learning for molecules and materials." Living Journal of Computational Molecular Science 3.1 (2021): 1499-1499. https://dmol.pub/
