Virtual course

Structural bioinformatics

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 virtual 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.

This course will be a virtual event delivered via a mixture of live-streamed sessions, pre-recorded lectures, and tutorials with live support. We will be using Zoom to run the live sessions (all fully password protected with automated English closed captioning and transcription) with support and both scientific and social networking opportunities provided by Slack and other methods, taking different time zones into account. In order to make the most out of the course, you should make sure to have a stable internet connection throughout the week and are available between 09:00-18:00 UTC-0 each day. In the week before the course there will be a brief induction session. Computational practicals will run on EMBL-EBI's virtual training infrastructure, meaning participants will not require access to a powerful computer or install complex software on their own machines.

Selected 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 & assess AI-predicted protein structures.
  • Explore protein-complex modeling 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 PDBe (Protein Data Bank in Europe) including PDBe-KB
  • UniProt and basic Sequence alignment tools
  • Protein structure analysis and classification: InterPro, Pfam, CATH
  • Protein docking: HADDOCK
  • Structure validation and assessment tools and strategies
  • Tools and resources for drug discovery: ChEMBL
  • AI-predicted protein structures: AlphaFoldDB and AlphaFill


David Armstrong
Piv Gopalasingam
Gerard Kleywegt
Typhaine Paysan-Lafosse
Sara Rocio Chuguransky
Mihaly Varadi

Please read our page on application advice 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 "application submission" section where you are asked to provide three 100-word paragraphs that cover your:
    • scientific biography
    • work history
    • current research interests
  • Upload a letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for this course; and also that you can dedicate your time to this virtual course.

Please submit all documents during the application process by 8th July 2022.

Incomplete applications will not be considered.

All applicants will be informed of the status of their application (successful, waiting list, rejected) by 22th July 2022. If you have any questions regarding the application process please contact Juanita Riveros.

Applications close
08 July 2022

17 - 21 October 2022
£200 GBP
Juanita Riveros
Open application with selection
30 places

  • Gerard Kleywegt
  • Christine Orengo
    University College London (UCL)
  • David Armstrong
  • Piv Gopalasingam

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