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.  

---

Using these materials

These course materials provide a mixture of pre-recorded lectures, presentations and practicals to help advance your knowledge and skills in the analysis of biological data. You may select your topic of interest from the Course content page to view the relevant materials or work your way through all the course materials. 

To find out more about the trainers who created these materials, follow the links from the Course content page or go directly to the Trainer biographies page. You can also find the software requirements for the practicals in the Technical help sheet.

In the Further learning section you may explore the details about the EMBL-EBI’s free access online tutorials and webinars on a variety of life sciences topics.

---

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

Material collection editors

• Genevieve Laura Evans, EMBL-EBI
• Piv Gopalasingam, EMBL-EBI
• Gerard Kleywegt, EMBL-EBI
• Christine Orengo, UCL
• Paulyna Gabriela Magana Gomez, EMBL-EBI
• Sudakshina Ganguly, EMBL-EBI

DOI: DOI: 10.6019/TOL.StrucBioinformaticsMaterials-t.2022.00001.1