Jalview</strong>
</p>
- <p>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, by giving for each residue the likely 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.</p>
+ <p>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.</p>
<p>
Jalview visualises PAE matrices as an alignment annotation track,
shaded from dark green to white, similar to the encoding used on the
href="https://alphafold.ebi.ac.uk/entry/O04090">O04090 3D model</a>
at EBI-AlphaFoldDB).
</p>
- <div style="display: flex; flex-wrap: wrap;" align="center"
- width="100%">
+ <img src="../structures/epas1_annotdetail.png" width="450" />
+ <br/><em>
+ Alignment of EPAS1 homologs from Human, Rat and Cow shaded by PLDDT, with
+ Predicted Alignment Error and secondary structure annotation shown for Human.
+ </em>
+ <!--</div>
+ <div width="35%">
<figure>
- <img src="../structures/epas1_annotdetail.png" height="300" />
- <figcaption>
- Alignment of EPAS1 homologs from Human, Rat and Cow<br />with
- predicted alignment error shown for Human
- </figcaption>
- </figure>
- <figure>
- <img src="../structures/epas1_pae_ebiaf.png" height="300" />
+ <img src="../structures/epas1_pae_ebiaf.png" />
<figcaption>
Predicted Alignment Error for Human EPAS1<br />from <a
href="https://alphafold.ebi.ac.uk/entry/Q99814">https://alphafold.ebi.ac.uk/entry/Q99814</a>
</figcaption>
</figure>
- </div>
+ </div>
+ </div> -->
<p>
<strong>Importing PAE Matrices</strong>
</p>
</p>
<p>
The <a href="../features/clarguments-basic.html">Command Line
- Interface</a> also provides a options for importing PAE matrices along
- side models, enabling the automated production of alignment figures
- annotated with PAE matrices and PLDDT scores.
+ Interface</a> 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.
</p>
<p>
<strong>Showing PAE Matrix Annotations </strong>
<p>
When viewing 3D structures from the EBI-AlphaFold database or local 3D
structures with an associated PAE file, the PAE is imported as <i>Reference
- Annotation</i>, which is not always automatically added to the alignment
+ Annotation</i>, and is not always automatically added to the alignment
view.
</p>
<p>To show the PAE, right click the sequence and locate the 'Add
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
+ positioned relative to each other, which is important when evaluating
whether domain-domain interactions or other contacts can be trusted.</p>
- <p>To make it more easy to identify regions of low PAE, Jalview can
+ <p>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.</p>
∥</strong>
</p>
<p>
- To create a PAE matrix tree, right click on a PAE annotation's label
+ <em>Creating a PAE matrix tree</em><br/>Right click on a PAE annotation's label
to open the annotation popup menu, and select <strong><em>Cluster
Matrix</em></strong>. 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.
+ 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 <em>Show groups on matrix</em>).</p>
<p>
+ The PAE matrix tree viewer behaves like other tree views, except:
<ul>
- <li>The PAE matrix tree viewer behaves like other tree views in
- Jalview, except selecting nodes or groups of nodes in the tree select
- columns in the alignment rather than sequences, and clicking adjust
+ <li>Selecting nodes or groups of nodes in the tree select
+ columns in the alignment, and clicking in the tree window adjust
the matrix's partition.</li>
<li>Only one tree and clustering can be defined for a PAE matrix,
regardless of whether it is displayed in different views or
alignments.</li>
- <li>Double clicking on a position in the PAE annotation where a
+ </ul>
+ Once the PAE annotation has clustering defined:
+ <ul><li>Double clicking on a position in the PAE annotation where a
clustering has been defined will select both the row and column
- clusters for the clicked position. This makes it easy to select
+ clusters for the clicked position. <br/><br/>This makes it easy to select
clusters corresponding to pairs of interacting regions.</li>
<li>Cluster colours for a PAE matrix can be used to colour
sequences or columns of the alignment via the <strong><em><a
git://source.jalview.org / jalview.git / commitdiff