Prediction and Analysis of Protein Structures

Overview

Advances in machine learning have transformed structural biology, making accurate protein structure predictions widely accessible. This course introduces practical approaches for exploring and analysing protein structures using modern tools such as AlphaFold and ChimeraX. You will learn how to retrieve structural models from biological databases, visualise protein structures, and analyse key features such as domains, ligand-binding sites, and protein-protein interfaces.

TipLearning Objectives

• Understand the basic principles behind protein folding and de novo protein structure prediction.
• Select and justify which software to use for predicting 3D structures.
• Use several web-based tools for structural prediction and analysis.
• Evaluate, compare and select high quality 3D structural protein model(s).
• Perform structural and functional analysis with proteins and their ligands in ChimeraX.

Target Audience

Researchers interested in generating, interpreting and exploring protein structures.

Prerequisites

Understanding of the basics of protein structure is expected.

Citation & Authors

Please cite these materials if:

• You adapted or used any of them in your own teaching.
• These materials were useful for your research work. For example, you can cite us in the methods section of your paper: “We carried our analyses based on the recommendations in YourReferenceHere”.

You can cite these materials as:

Salehe, B., Jackson, M., Zane, I., Zhu, Y., Tavares, H. (2025). Course Development Guidelines. https://cambiotraining.github.io/protein-structure-prediction

Or in BibTeX format:

@misc{YourReferenceHere,
  author = {Salehe, Bajuna and Jackson, Matthew and Zane, Isabelle and Zhu, Yu and Tavares, Hugo},
  month = {4},
  title = {Course Development Guidelines},
  url = {https://cambiotraining.github.io/protein-structure-prediction},
  year = {2025}
}

About the authors:

• Bajuna Salehe
  Affiliation: Cambridge Centre for Research Informatics Training
  Roles: conceptualisation; primary author; data curation; software
• Matthew Jackson
  Affiliation: MRC Toxicology Unit, University of Cambridge
  Roles: conceptualisation; primary author; software
• Isabelle Zane
  Affiliation: Wellcome Sanger Institute
  Roles: conceptualisation; primary author; data curation; software
• Yu Zhu
  Affiliation: Department of Pharmacology, University of Cambridge
  Roles: conceptualisation; primary author; data curation; software
• Hugo Tavares
  Affiliation: Cambridge Centre for Research Informatics Training
  Roles: editor