A protein's 3D structure determines what it does, and the evolutionary relationships between sequences show how genes and species are connected. This module covers both, two ways of getting more from a sequence than its letters alone.
You'll start with protein structure prediction. AI tools such as AlphaFold can predict a 3D model from an amino acid sequence, and you'll learn how to run one and how to interpret the result, including how confident the model is in each region.
Then you'll turn to phylogenetics. Starting from a multiple sequence alignment, you'll build a phylogenetic tree in MEGA and add bootstrap values to show how well each branch is supported. The alignment skills from earlier in the course carry over directly. Once you've finished, move on to Workflows & Reproducibility.
An EMBL-EBI webinar introducing AlphaFold and how to read its predicted protein structures, designed for experimental biologists.
A step-by-step workflow for predicting protein 3D structures with AlphaFold through ColabFold.
A beginner-friendly walkthrough of building a phylogenetic tree in MEGA, from sequence alignment onward.
How to open a multiple sequence alignment in MEGA, choose the best evolution model, and build a maximum likelihood tree with bootstrap support.