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Lecturer(s)
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Course content
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credit: attendance at practicals (max. 3 absences), seminar work, project presentation. exam: oral (min. 50 %)
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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Students will learn to explore three dimensional protein structures through computational analysis. We will provide an overview of existing computational techniques, to validate, simulate, predict and analyze protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
Content of lectures: Relation between sequence, structure, and function of biomolecules. Structural basis for macromolecular dynamics, binding specificity, and catalysis. Practicals: Structure visualization, formats of 3D structure data. Overview of biological databases, servers, and information centres. Practicals: Using R and Python to access the databases. Sequence comparisons. Basics of macromolecular structure: three-dimensional structure, PDB coordinates, classification of proteins in structure families. Practicals: Programs for analysis, and comparison of structures, structural alignment, and graph-based structural signatures, basic operations with PDB files. Introduction to the theory of classification and comparison of sequences and extraction of common distinctive features (e.g., motifs). Sequence analysis for prediction of secondary and tertiary structures, and homology modelling of three-dimensional structures based on sequence data. Practicals: secondary structure prediction, homology modelling Advanced prediction of protein structure. Practicals: AlphaFold, AlphaLink Robetta Protein-ligand docking. Practicals: docking examples Virtual screening for ligands. Practicals: ligand screening Basics of molecular dynamics. Practicals: molecular dynamics in Gromacs Normal mode analysis. Practicals: normal mode analysis of static structures in R Principal component analysis. Practicals: principal component analysis of simulated data Content of tutorials: project assignment, presentation and discussion
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Recommended literature
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Baek et al, Accurate prediction of protein structures and interactions using a three-track neural network, SCIENCE, 19 Aug 2021, Vol 373, Issue 6557, pp. 871-876, DOI: 10.1126/science.abj8754.
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Eswar N., Eramian D., Webb B., Shen M.Y., Sali A.: Protein structure modelling with MODELLER. Methods Mol. Biol..
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GROMACS user manual (https://doi.org/10.5281/zenodo.7588711.
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Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589 (2021). https://doi.org/10.1038/s41586-021-03819-2.
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Yuriev E., Agostino M., Ramsland P.A.: Challenges and advances in computational docking: 2009 in review. J. Mol. Recognit. 24, 149-164 (2011), ISSN: 1099-1352.
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