Structural Bioinformatics

2018-2019

Course Objective

Why Structural Bioinformatics?
Generally speaking, the function of a protein is determined by its three
dimensional structure, and therefore structural information is crucial
for understanding the working of proteins. However, experiments,
prediction and simulation of protein structures remain difficult. This
course will provide you an overview of existing computational
techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will provide practical knowledge about
how and when to use such techniques.

Goals:
• Being able to evaluate protein structures with knowledge of their
experimental source and validation techniques
• Being able to compare different protein structures, and evaluate
similarity
• Learning how and when to use structure prediction methods
• Being able to create scripts that connect different Structural
Bioinformatics methods.
• Being able to compare different simulation techniques for biological
macro-molecules, and be able to analyse the simulated data
computationally.
• Reading and understanding scientific papers in the field of Structural
Bioinformatics.

Course Content

Theory:
• Protein and DNA structure sources
• Experimental methods
• Structure validation
• Protein fold prediction (from homology modelling to ab initio
prediction)
• Structural classification and structural alignment
• Protein folding and energetics
• Molecular Dynamics & Monte Carlo simulation
• Function from structure

Practical:
• Obtaining geometric features from PDB files
• Homology modelling with Modeller
• Protein interaction as a 'computational experiment' (simulation).

Teaching Methods

13 Lectures (2 two-hour lectures per week)
12 computer practicals (2 two-hour sessions per week)
Feedback (theoretical and practical) will be given during the computer
practical sessions.

Method of Assessment

The final grade for this course will consist of 50% practical work and
50% theoretical assessment.

Practical Assignments: (50%)
(1) Obtaining geometric features from PDB files
(2) Homology modelling with Modeller (including structural alignment)
(3) Protein interaction as a 'computational experiment' (simulation).

Theoretical: (50%)
• Oral or written exam (depending on number of course students).
• As part of the exam a research paper on a Structural Bioinformatics
topic needs to be analysed in detail.
• You will be prepared for you exam through exercises and paper
discussions during the lectures

Entry Requirements

Bachelor in any science discipline (including medicine), with an
interest in applying algorithmic approaches to molecular structures in
biology.
Some experience with programming (preferably python). Note that at the
start of the course a small scripting practical will be given, this
means that in practice students without scripting experience can follow
the course if they are motivated to learn during the course, and willing
to put in the extra effort - when in doubt please contact the
coordinator.

Literature

- course material on canvas.vu.nl
- Marketa Zvelebil and Jeremy O. Baum. Understanding Bioinformatics.
Garland Science 2008 ISBN-10: 0-8153-4024-9
- book "Introduction to Structural Bioinformatics": Two chapters are
published on ArXiv (https://arxiv.org/abs/1801.09442
https://arxiv.org/abs/1712.00407 https://arxiv.org/abs/1712.00407), the
other chapters will be made available during the course.

Target Audience

mAI, mBSB (JD), mCS, mPDCS, mMNS, mBMOL, mNS, mBIO

Additional Information

- Compulsory course for students in Bioinformatics Profile of MSc
Bioinformatics & Systems Biology (mBSB).
- Optional course for mAI, mCS, mPDCS, mMNS, mBMOL, mNS, mBSB (JD).

General Information

Course Code X_405019
Credits 6 EC
Period P4
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. ir. K.A. Feenstra
Examiner dr. ir. K.A. Feenstra
Teaching Staff dr. ir. K.A. Feenstra
dr. S. Abeln

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Practical
Target audiences

This course is also available as: