Project work Moodle group for the Master Bioinformatics.
Elective Module for Master Bioinformatics.

Bioinformatics Master Student Course “Structural Bioinformatics”


apl. Prof. Dr. habil. Dirk Walther


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)

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.   Deep Learning 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

Elective module master Bioinformatics.
Mandatory course for Master Bioinformatics.
Elective module in Master Biochemistry and Molecular Biology. Hint: This is not a statistics but a programming course.
Mandatory course for Master Biochemistry and Molecular Biology.
Elective module Master Bioinformatics.
Bridge module Master Bioinformatics for students with Biology background. As well an elective module for master Bioinformatics students with background in Bioinformatics and Computer Science.

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.