Structural Bioinformatics

Course Objective

Why Structural Bioinformatics?

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 with 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.

After completing the course successfully,

• You understand the basics of statistical thermodynamics and the relation to protein structures, evolution and function [Knowledge and understanding]
• You are able to evaluate protein structures with knowledge of their experimental source, bias and validation techniques [Applying knowledge and understanding]
• You are able to compare different protein structures, and evaluate similarity [Applying knowledge and understanding]
• You know how and when to use structure prediction methods [Making judgements; Applying knowledge and understanding]
• You can perform calculations on protein structures to gain new biological insights [Applying knowledge and understanding]
• You are able to compare different simulation techniques for biological macro-molecules, and be able to analyse the simulated data computationally [Applying knowledge and understanding]
• You are able to read and understand scientific papers in the field of Structural Bioinformatics [Communication; Lifelong learning skills]
• You are able to independently search for answers to open problems and ask a question about something that is unclear [Communication, Lifelong learning skills]

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)

Additional Information Teaching Methods

• 13 lectures (2 two-hour lectures per week)
• Exercises during/in between lectures
• 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%)
(assessment of practical assignments will be adjusted based on ongoing developments in generative language models)

Theoretical: (50%)

• Oral, written or digital exam (depending on number of course students) to assess lecture topics.
• As part of the exam research papers on Structural Bioinformatics topics need to be analysed in detail.
• You will be prepared for your exam through exercises and paper discussions during the lectures, as well as through the practical assignments.

Assessment and grading details will be posted on Canvas.

Entry Requirements

Bachelor in any science discipline (including medicine), with an interest in molecular biology and applying algorithmic approaches to protein structures.
Experience with programming (preferably python).
Basic understanding of calculus and of organic chemistry.
When in doubt please contact the coordinator.

Literature

• course material on canvas.vu.nl
• book: Marketa Zvelebil and Jeremy O. Baum. Understanding Bioinformatics. Garland Science 2008 ISBN-10: 0-8153-4024-9
• book "Introduction to Structural Bioinformatics": The intro is published on ArXiv [https://arxiv.org/abs/1801.09442], and the chapters are available from the table of contents. The book will also be posted on canvas.

Additional Information Target Audience

M Bioinformatics and Systems Biology
M Biomolecular Sciences
M Artificial Intelligence
M Computational Science

Additional Information

BYOD policy (Bring Your Own Device)
We expect students in this course to use their own laptop. This laptop should at the very least support an SSH client, for remote shell access to the VU Linux servers. We will provide some help in setting this up at the start of the course.
Lectures may be recorded; these recordings will only be used within the course, including possibly in subsequent years as background information.

Recommended background knowledge

Some experience with the linux command line is preferred.