NBIA05014U Structural Bioinformatics

Volume 2022/2023

MSc Programme in Biochemistry
MSc Programme in Bioinformatics
MSc Programme in Molecular Biomedicine


Overview of protein structure, experimental structure determination and structure analysis, calculation of RMSD and structure superposition, fold classification (CATH, SCOP, new methods), inference of function from structure, 1D prediction (secondary structure, solvent exposure, neural networks, HMMSTR) & sampling of protein conformations (TorusDBN, directional statistics), homology modelling & the loop closure problem, knowledge based energy functions, de novo prediction methods (ROSETTA, LINUS). RNA 2D and 3D structure prediction. Python based practicals using Biopython's Bio.PDB module.

Learning Outcome


  • Know basic aspects of protein physics (folding, physical forces, thermodynamics, statistical physics)
  • Know goals and methods of fold classification and function-from-structure for proteins
  • 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 ab initio and homology modelling of proteins.
  • Know the basics of probabilistic structure prediction
  • Know the RMSD algorithm in mathematical detail
  • To obtain insight in and background for bioinformatic 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
  • Use databases such as SCOP and CATH
  • 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


  • 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 are made more reliable wen using multiple sequences.

See Absalon.

Biological sequence analysis or similar. Linux and python programming or similar.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures (4-6 pr week), exercises (2-4 hours pr. week)
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 91
  • Practical exercises
  • 21
  • Project work
  • 5
  • Guidance
  • 7
  • Exam
  • 54
  • Total
  • 206
Feedback by final exam (In addition to the grade)
7,5 ECTS
Type of assessment
Written assignment, 54 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). You will have time for the take home assignment in week 3 and 4.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.

Re-exam: 3 day take home exam (54 hours).

Criteria for exam assesment

In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.