From 426894b9cd99748534a5c684e9dc1d0bab90a660 Mon Sep 17 00:00:00 2001
From: James Procter
Importing PAE Matrices @@ -56,10 +69,9 @@ href="../features/structurechooser.html">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 + them both together via the Load PDB + File dropdown menu in the 3D structure chooser, providing it is in a supported PAE format.
@@ -81,23 +93,93 @@ 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. -
+ associated PAE data appears.- PAE matrix annotation rows behave like any other sequence associated annotation, with the following additional features: + 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 eachother, which is important when evaluating + whether domain-domain interactions or other contacts can be trusted.
+To make it more 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 + ∥ +
++ To create 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, and by default these will + also be overlaid on the matrix annotation row. +
+
+ 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. -- 1.7.10.2