Bioinformatik
Elective course Master Bioinformatics.
Elective course Master Bioinformatics.
Mandatory course Master Bioinformatics.

OBJECTIVES of the course

Comprehensive and in-depth introduction to Structural Bioinformatics, current and past approaches, theories, computational tools & algorithms.


Lectures

Seminars (synced with lecture topics)

1.       Methods of experimental structure elucidation

·         X-ray crystallography and interpretation of structure quality

·         NMR

·         Various other methods (SAXS, CD, cryo-EM)

2.       Principles of protein structures

·         Properties of amino acids, Peptide bond, main degrees of freedom/ main chain dihedral angles

·         Hierarchies of protein structure, secondary structure assignments (DSSP),

·         Packing constraints, cavities

3.       Fold classification and structure comparison

·         Folding topologies, term “topology”, fold databases

·         Structure comparison (RMSD, contact map overlap)

4.       Protein folding

·         Thermodynamic hypothesis

·         Detailed discussion of enthalpic/entropic contributions to delta-G (bonds, angles, electrostatics, van-der Waals, solvation etc.),

·         Levinthal paradox, folding funnel, folding pathways, concepts of folding (two-state, framework model)

·         “lattice”-proteins

5.       Molecular dynamics

·         Force fields (AMBER, ECEPP-2, CHARMM)

·         Newtonian equations of motion, Taylor expansion, Verlet algorithm, boundary conditions, time step, neighbor lists

6.       Energy minimization

·         Search problem (global vs. local minima)

·         Steepest descent, conjugate gradient, ensemble properties, Monte Carlo simulation, simulated annealing.

7.       Database-derived potentials

·         Inverse Boltzmann statistic, pairwise potentials of mean force, solvent exposure interactions, fold quality assessment

8.       Docking

·         Protein-protein interaction and docking

·         Small-molecule docking, binding pocket identification, geometric  hashing

·         Intro to cheminformatics (descriptors of small molecules, comparison, binding sites etc.)

9.       Secondary structure prediction

·         Chou-Fasman, GOR, neural networks, ab-initio methods, Deep Learning methods

10.   Homology modeling

·         Profile methods, true homology modeling (incl. loop modelling with dead-end elimination)

·         Public resources for Homol. Modelling

11.   Threading

·         Optimal threading (branch-and-bound)

·         Structure quality assessment

·         CASP competition

12.   DeepLearning methods/ AlphaFold

13.   Principles of DNA/RNA structure

·         Principles governing nucleic acid structures

·         Descriptors of RNA structures

·         Concept of isostericity

14.   RNA structure prediction

·         RNA secondary structure prediction (Nussinov algorithm, energy minimization)

 

1.       Introduction to the PDB (protein databank, search, structure stats),

2.       Molecular geometry (distances, angles, dihedral angle, plane normals etc.)

·         Exercise/ homework: dihedral angle calculation (Ramachandran plot)

3.       Molecular graphics

·         Types of structure renderings,  visualization software (Pymol) side-by-side stereo, contact maps, cartoons, surfaces, marching cube algorithm

4.       Structural superposition and selected aspects of polymer physics relevant to structural biology (Rotation matrix, Radius of gyration, Inertia matrix, Moments of inertia)

·         Exercise/ homework: superposition of two helices

5.       Molecular dynamics: analytical solution of the harmonic oscillator (2nd order differential equ.)

6.       Derivation of the Boltzmann distribution

7.       Introduction to MD software (Abalone)

·         Exercise/ homework: MD and energy minimization of two poly-peptides

8.       Branch-and-bound (homology modelling, sidechain placement)

 

Student presentations: As part of the seminar, every student is given an article on relevant subjects that expand on the material covered in the lectures (e.g. methods of structure comparison) and are both classical “landmark” papers as well as contemporary contributions. For the latter, focus if placed on approaches that bridge between different themes, e.g. network analysis approaches towards structure analysis


This is a bridge course for Master Bioinformatics. Students of Master Biochemistry and Molecular Biology can take this course as well as elective course.
Elective course Master Biochemistry and Molecular Biology. No programming knowledge is required. We learn R as a general purpose programming language without doing statistics.
This is an elective course in the Master Bioinformatics. Good knowledge in R or Python and in Statistics is required.
This is a mandatory course for Master Biochemistry and Molecular Biology.

The lecture gives an introduction to the mathematical concepts, methods and approaches in modern systems biology. It focusses on the stochastic and deterministic formulation of biochemical reaction kinetics, illustrated in applications to important biological signal transduction pathway and gene regulatory systems. Further topics include parameter estimation in deterministic reaction systems and network motifs in gene regulatory networks.