JABAWS Protein Disorder Prediction Services
The Web Services→Disorder menu in the
alignment window allows access to protein disorder prediction
services provided by the configured JABAWS
servers. Each service operates on sequences in the alignment or
currently selected region (since Jalview 2.8.0b1) to
identify regions likely to be unstructured or flexible, or
alternately, fold to form globular domains.
Predictor results include both sequence features and sequence associated alignment annotation rows. Features display is controlled from the Feature Settings dialog box. Clicking on the ID for a disorder prediction annotation row will highlight or select (if double clicked) the associated sequence for that row. You can also use the Sequence Associated option in the Colour By Annotation dialog box to colour sequences according to the results of predictors shown as annotation rows.
JABAWS 2.2 provides four disorder predictors which are described below:
DisEMBL
(Linding et al., 2003)
DisEMBL is a set of
machine-learning based predictors trained to recognise
disorder-related annotation found on PDB structures.
| Name | Annotation type | Description |
| COILS | Sequence Feature & Annotation Row |
Predicts loops/coils according to DSSP definition[1]. Features mark range(s) of residues predicted as loops/coils, and annotation row gives raw value for each residue. Value over 0.516 indicates loop/coil. |
| HOTLOOPS | Sequence Feature & Annotation Row |
"Hot loops constitute a refined subset of COILS,
namely those loops with a high degree of mobility as determined
from Cα temperature factors (B factors). It follows that
highly dynamic loops should be considered protein
disorder." Features mark range(s) of residues predicted to be hot loops and annotation row gives raw value for each residue. Values over 0.6 indicates hot loop. |
| REMARK465 | Sequence Feature & Annotation Row |
"Missing coordinates in X-ray structure as defined
by remark465 entries in PDB. Nonassigned electron densities most
often reflect intrinsic disorder, and have been used early on in
disorder prediction." Features gives range(s) of residues predicted as disordered, and annotation row gives raw value for each residue. Value over 0.1204 indicates disorder. |
[1]. DSSP Classification: α-helix (H), 310-helix (G), β-strand (E) are ordered, and all other states (β-bridge (B), β-turn (T), bend (S), π-helix (I), and coil (C)) considered loops or coils.
RONN a.k.a.
Regional Order Neural Network
This predictor employs an
approach known as the 'bio-basis' method to predict regions of
disorder in sequences based on their local similarity with a
gold-standard set of disordered protein sequences. It yields a set
of disorder prediction scores, which are shown as sequence
annotation below the alignment.
| Name | Annotation type | Description |
| JRonn[2] | Annotation Row | RONN score for each residue in the sequence. Scores above 0.5 identify regions of the protein likely to be disordered. |
[2]. JRonn denotes the score for this server because JABAWS runs a Java port of RONN developed by Peter Troshin and distributed as part of Biojava 3
IUPred
IUPred employs an empirical model to estimate likely regions of
disorder. There are three different prediction types offered, each
using different parameters optimized for slightly different
applications. It provides raw scores based on two models for
predicting regions of 'long disorder' and 'short disorder'. A third
predictor identifies regions likely to form structured domains.
| Name | Annotation type | Description |
| Long disorder | Annotation Row | Prediction of context-independent global disorder that
encompasses at least 30 consecutive residues of predicted
disorder. Employs a 100 residue window for calculation. Values above 0.5 indicates the residue is intrinsically disordered. |
| Short disorder | Annotation Row | Predictor for short, (and probably) context-dependent,
disordered regions, such as missing residues in the X-ray
structure of an otherwise globular protein. Employs a 25 residue
window for calculation, and includes adjustment parameter for
chain termini which favors disorder prediction at the ends. Values above 0.5 indicate short-range disorder. |
| Structured domains | Sequence Feature | Features highlighting likely globular domains useful for
structure genomics investigation. Post-analysis of disordered region profile to find continuous regions confidently predicted to be ordered. Neighbouring regions close to each other are merged, while regions shorter than the minimal domain size of at least 30 residues are ignored. |
GLOBPLOT
Defines
regions of globularity or natively unstructured regions based on a
running sum of the propensity of residues to be structured or
unstructured. The propensity is calculated based on the probability
of each amino acid being observed within well defined regions of
secondary structure or within regions of random coil. The initial
signal is smoothed with a Savitzky-Golay filter, and its first order
derivative computed. Residues for which the first order derivative
is positive are designated as natively unstructured, whereas those
with negative values are structured.
| Name | Annotation type | Description |
| Disordered Region | Sequence Feature | Sequence features marking range(s) of residues with positive dydx values (correspond to the #Disorder column from JABAWS results) |
| Globular Domain | Sequence Feature | Putative globular domains |
| Dydx | Annotation row | First order derivative of smoothed score. Values above 0 indicates residue is disordered. |
| Smoothed Score Raw Score |
Annotation Row | The smoothed and raw scores used to create the
differential signal that indicates the presence of unstructured
regions. These are hidden by default, but can be shown by right-clicking on the alignment annotation panel and selecting Show hidden annotation |
Documentation and thresholds for the JABAWS Disorder predictors adapted from a personal communication by Nancy Giang, 2012.