The course is divided in two parts: the structural bioinformatics of proteins (2/3) and of RNA (1/3). Overview of protein structure: from chemical properties over protein physics to mathematical representations of protein structure. Protein structure analysis, superimposition of protein structures (Kabsch algorithm, probabilistic superimposition using Theseus), fold classification. Probabilistic models of protein structure and directional statistics. Protein structure prediction: from homology modeling over knowledge based potentials to cutting edge contemporary methods based on deep learning and probabilistic deep generative models. Overview of the architecture of AlphaFold2, the deep learning method that “solved the protein folding problem” according to CASP in 2020. RNA 2D prediction (Nussinov algorithm) and 3D prediction. The course includes an introduction to relevant topics in linear algebra and Bayesian statistics for novices. Python based practicals using Biopython’s Bio.PDB module. The course assumes good Python programming skills.
MSc Programme in Biochemistry
MSc Programme in Bioinformatics
MSc Programme in Molecular Biomedicine
- Know basic aspects of protein physics (folding, physical forces, thermodynamics, statistical physics)
- Know goals and methods of fold classification methods
- Know goals and methods of 1D structure prediction and parameterization (secondary structure, backbone structure, solvent exposure)
- Know basic vector and matrix algebra.
- Know goals and methods of de novo and homology modelling of proteins.
- Know the basics of probabilistic models of proteins
- Know the RMSD algorithm in mathematical detail
- Know bioinformatics methods for RNA secondary structure prediction
- Knowledge about structural (2D) RNA alignments.
- Knowledge of RNA 3D structure and of 3D aspects of RNA structure prediction.
- Knowledge about the applied aspects of RNA structure prediction methods in genome analysis
- To be able to implement the RMSD algorithm
- To be able to use the PDB database and PDB files
- To be able to implement structural bioinformatics algorithms in Python and Biopython's Bio.PDB
- To be able to visualize and analyze biomolecular structures using PyMol
- To be able to implement the Nussinov algorithm at least to the level of computing the max score for an unbifurcated RNA secondary structure.
- Apply RNA structure prediction programs while processing the output from them
- Read and understand research articles on the topic of structural bioinformatics
- Analyze a protein structure with relevant methods and algorithms
- Analyze a problem in structural bioinformatics and outline a matching algorithm
- Distinguish different types of protein structure prediction and their degree of difficulty
- How the energy parameters, energy model, probabilistic models provides the core components for RNA structure prediction.
- Interpret the results of RNA structure prediction methods and relate them to that of prediction on "background" sequence
- How RNA structure prediction is made more reliable when using multiple sequences.
Lectures (4-6 hours pr week), exercises (2-4 hours pr. week)
The course "Biological sequence analysis" or similar.
Python programming for data science or similar.
Academic qualifications equivalent to a BSc degree is recommended.
- 7,5 ECTS
- Type of assessment
Written assignment, 173 hours
- Type of assessment details
- The exam will consist of a take home programming task, that is, the development of a program that solves a specific structural bioinformatics problem. The program needs to be implemented in Phython using Bio.PDB (Biophython's structural toolkit).
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners.
Criteria for exam assessment
In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.
Single subject courses (day)
- Practical exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 2
The number of seats may be reduced in the late registration period
- Study Board for the Biological Area
- Department of Biology
- Department of Veterinary and Animal Sciences
- Faculty of Science
- Thomas Wim Hamelryck (8-766a636f676e747b42646b71306d7730666d)
Jan Gorodkin (firstname.lastname@example.org)
Stefan Seemann (email@example.com)
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