+ Working with Predicted Alignment Error Matrices in + Jalview +
+ +Predicted Alignment Error (PAE) matrices are produced by + deep-learning based 3D-structure prediction pipelines such as + AlphaFold. They reflect how reliably two parts of a model have been + positioned in space. Each column in a PAE matrix corresponds to a + residue in the model, and each gives the likely RMS error (in + Ångstroms) between that residue and every other modelled + position the pair of residues' real relative position, if the model + and real 3D structure were superimposed at that residue.
++ Jalview visualises PAE matrices as an alignment annotation track, + shaded from dark green to white, similar to the encoding used on the + EBI-AlphaFold website (see O04090 3D model + at EBI-AlphaFoldDB). +
+
+ + Importing PAE Matrices +
++ Jalview retrieves PAE matrices when importing predicted 3D structures + from the EBI-AlphaFold database via Jalview's structure + chooser GUI. If you have produced your own models and accompanying + PAE matrices using a pipeline such as ColabFold, then you can load + them both together via the Load PDB + File dropdown menu in the 3D structure chooser, providing it is in a + supported PAE format. +
++ The Command Line + Interface also provides the option to import a PAE matrix alongside + a 3D structure file, enabling the automated production of alignment figures + annotated with PAE matrices and PLDDT scores. +
++ Showing PAE Matrix Annotations +
++ When viewing 3D structures from the EBI-AlphaFold database or local 3D + structures with an associated PAE file, the PAE is imported as Reference + Annotation, and is not always automatically added to the alignment + view. +
+To show the PAE, right click the sequence and locate the 'Add + Reference Annotation' entry in the Sequence ID submenu, or select all + sequences and locate the option in the Selection submenu. You can do + this in any alignment window (or view) where a sequence with + associated PAE data appears.
++ Adjusting the height of PAE matrix annotations +
++ PAE annotations behave in the same way as Jalview's line graph and + histogram tracks. Click+dragging up and down with the left (select) + mouse button held down will increase or decrease the height of the + annotation. You can also hold down SHIFT + whilst doing this to adjust the height of all PAE rows at once. +
+PAE matrix annotation rows behave like any other sequence + associated annotation, with the following additional features:
+PAE matrices are useful for identifying regions of 3D structure + predictions that are likely to be positioned in space in the same or + similar way as shown in the predicted structure data. Regions of low + PAE often correlate with high alphafold reliability (PLDDT) scores, + but also complement them since they highlight well-folded regions such + as domains, and how well those regions have been predicted to be + positioned relative to each other, which is important when evaluating + whether domain-domain interactions or other contacts can be trusted.
+To make it easy to identify regions of low PAE, Jalview can + cluster the PAE matrix, allowing columns of the matrix to be grouped + according to their similarity, using an Average Distance (UPGMA) tree + algorithm and the sum of differences between each column's PAE values.
++ distij = ∥ pi-pj + ∥ +
+
+ Creating a PAE matrix tree
Right click on a PAE annotation's label
+ to open the annotation popup menu, and select Cluster
+ Matrix. Once the calculation has finished, a tree viewer will open,
+ and columns of the matrix are then partitioned into groups such that
+ the third left-most node from the root is placed in its own group.
+ Colours are randomly assigned to each group. By default column group colours will
+ also be overlaid on the matrix annotation row - this can be turned off
+ via the PAE annotation row menu (by unticking Show groups on matrix).
+ The PAE matrix tree viewer behaves like other tree views, except: +
+ PAE matrices and Jalview Projects +
+Any PAE matrices imported to Jalview are saved along side any + trees and clustering defined on them in Jalview Projects.
++ Support for visualision and analysis of predicted alignment + error matrices was added in Jalview 2.11.3. +
+ +