Structural Bioinformatics

Knowledge:   

• 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

Skills:

• 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 

Competences:

• 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.