How does AlphaFold work?

DeepMind’s 2021 methods paper is the best reference for this. It gives an overview of the most important ideas, and there is a detailed description of all aspects of the system in the Supplementary Information.
Note that the architecture of the system used at CASP14 differs significantly from the version used at CASP13, making it important to refer to the 2021 publication.

Visit our online training course to learn more about AlphaFold.

What is AlphaMissense?

AlphaMissense is an AI model that builds on Google DeepMind’s AlphaFold2 to categorise ‘missense’ mutations in different proteins as either ‘likely pathogenic’, ‘likely benign’ or ‘uncertain’, producing a score that estimates the likelihood of a variant being pathogenic. AlphaMissense leverages AlphaFold2’s capability to model protein structure, and its capacity to learn evolutionary constraints from related sequences. The implementation is closely aligned with AlphaFold2, with some architectural differences.

AlphaMissense was used to classify the effects of all possible 216 million single amino acid sequence substitutions across the 19,233 canonical human proteins.

Using an amino acid sequence as an input, AlphaMissense:

• Gives an indication of which mutations are more likely to underlie human diseases - such as rare genetic disorders or developmental diseases - by categorising missense mutations into likely pathogenic or likely benign. Combined with other types of information, it can help to decipher what mutations may be causing a disease.
• Helps to highlight potential functionally important regions of the protein.

Note that AlphaMissense does not predict the change in protein structure, or biophysical properties such as stability, upon mutation. Instead, it uses related protein sequences and protein structure as contextual information to estimate pathogenicity.

For more information about AlphaMissense, please refer to the paper: Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023).

AlphaMissense scores for all human missense variants are available on the Google Cloud Public Dataset.

What if I can’t find the protein I’m interested in?

If you can’t find the structure you’re looking for, here are some suggestions to improve your search results:

• Try searching by protein or gene name rather than specific UniProt accession.
• If running a sequence search, the input query should contain at least 20 amino acids, and only standard amino acids are accepted.
• Check that the protein isn’t excluded by any of the criteria covered in a previous FAQ.

How can I download and use the Predicted Aligned Error (PAE) file?

The PAE is displayed as an interactive plot for each of the structure predictions. If you need the raw data with PAE for all residue pairs, you can download the PAE as a JSON file using the button at the top of the structure page.

This file is in a custom format and it isn't supported by any existing software – you will have to use Python or another programming language to analyse or plot the information that is contained in it.

The fields in the JSON file are:

• predicted_aligned_error: The PAE value of the residue pair, rounded to the closest integer. For the PAE value at position (i, j), i is the residue on which the structure is aligned, j is the residue on which the error is predicted.
• max_predicted_aligned_error: A number that denotes the maximum possible value of PAE. The smallest possible value of PAE is 0.

We updated the PAE JSON file format on 28th July 2022 to reduce file size by 4x. Please ensure you read the 2D matrix of PAE values from the predicted_aligned_error field instead of the removed 1D "distances" field and avoid using the old "residue1" and "residue2" fields.

If you are using a script or third party tool to read the PAE JSON file programmatically and you are seeing errors (e.g. missing field "distance"), check with the author of the program whether the latest PAE JSON format is supported.

Who should I contact with enquiries?

For sharing feedback on structure predictions, please use the feedback buttons on each structure page.

For other questions about AlphaFold not directly related to the database, please contact the AlphaFold team at alphafold@deepmind.com. Please do not share anything confidential with Google DeepMind.

For questions and feedback about the AlphaFold DB website, please contact afdbhelp@ebi.ac.uk.

For press enquiries, please contact press@deepmind.com or comms@ebi.ac.uk.