This course provides  a guide to the commonly used methods and tools in structural bioinformatics to analyse and interpret experimentally determined and AI-predicted macromolecular structure data.

Structural biology, determining the three-dimensional shapes of biomacromolecules and their complexes, can tell us a lot about how these molecules function and the roles they play within a cell. Data derived from structure determination experiments and Artificial Intelligence (AI)-assisted structure prediction enables life-science researchers to address a wide variety of questions.  

This course explores bioinformatics data resources and tools for the investigation, analysis, and interpretation of both experimentally determined and predicted biomacromolecular structures. It will focus on how best to analyse and interpret available structural data to gain useful information given specific research contexts. The course content will also cover predicting function and exploring interactions with other macromolecules.

Successful participants may be sent materials prior to the course. These might include pre-recorded talks and required reading or online training that will be essential to fully engage with the course.

Who is this course for?

This course is aimed at scientists generating structural data or scientists utilising structural data in their analysis and/or interpretation. No previous experience in the field of structural bioinformatics is required, however good knowledge of protein structure and function would be of benefit.

What will I learn?

Learning outcomes

After the course you should be able to:

• Access and browse a range of structural data repositories
• Determine whether appropriate structural information exists about a given small molecule, macromolecule or complex, applying available structure-quality information
• Build a structural model for a protein which has a structurally characterised relative and evaluate its quality
• Predict the function of a protein, based on sequence and structure data, and navigate and assess AI-predicted protein structures
• Explore protein-complex modelling approaches

Course content

During this course you will learn about: 

• Public repositories of structural data: Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB), and tools to search and analyse information in these repositories from Protein Data Bank in Europe (PDBe) including PDBe-KB
• UniProt and basic Sequence alignment tools
• Protein structure analysis and classification: HMMER, InterPro, CATH
• Protein structure prediction and docking: PHYRE2 and HADDOCK
• Structure validation and assessment tools and strategies: PDB-REDO
• Tools and resources for drug discovery: ChEMBL
• AI-predicted protein structures: AlphaFoldDB and AlphaFill

Trainers

Please read our page on application support before starting your application. In order to be considered for a place on this course, you must do the following:

• Complete the online application form.
• Ensure you add relevant information to the ‘submission details’ section where you are asked to provide information on your:
  • pre-requisite skills and knowledge
  • current work and course expectations
  • data availability
• Upload one letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for the course.

Please submit all documents during the application process by 23:59 BST on 28 July 2024. Items marked * in the application are mandatory. Incomplete registrations will not be processed. 

All applicants will be informed of the status of their application (successful, waiting list, unsuccessful) by 12 August 2024. If you have any questions regarding the application process please contact Juanita Riveros. 

Course materials

The course materials from the 2023 edition of the course are now live and available for you to use. They provide a mixture of pre-recorded lectures, presentations, and practicals from the course, and will give you a snapshot of what to expect in the 2023 edition.

Posters

All participants are expected to present a poster that will be shared via the course handbook and other virtual platforms used for the course. Successful applicants will be asked to submit their poster upon registration. 

All posters should:

• PDF format
• A2 in size - 420mm x 594 mm 
• portrait orientation
• include your photograph (ideally the same photo you have uploaded for use in the course handbook)
• high resolution
• include contact information, if you wish

We expect the posters to act as a talking point between you, other participants and the trainers on the course. The posters can be viewed before and during the course. They should give the reader an idea of the work you are engaged in, what you are planning to do next, and anything of interest that might be useful for sharing with other participants.

Fee bursary

Limited financial assistance is available in the form of a registration fee waiver, which will cover the registration fee to attend the course. The recipient will be informed about their bursary application together with the outcome of their course application.

You may apply for financial assistance when submitting your application. During the application, you will be asked to justify your need to receive a fee bursary and how your attendance will make a difference to your career. Application for financial support will not affect the outcome of your application.

The scientific organisers will select the recipient of all financial assistance during the participant selection process. Bursary selection results do not impact your admission to the course. Selection is based on scientific merit, your current work or study location, the reasons for needing financial support, and the impact this event will have on your career.