X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;ds=sidebyside;f=help%2Fhtml%2FwebServices%2FproteinDisorder.html;h=1c35bf3ca3c252916581e668906d02e79a1d90e4;hb=1f84a89eddaa39d8277b0d520c2cfd6356cff020;hp=5b88c594a5b5f6d29166d3dc7a551bf88c4b78f6;hpb=a8f483d04205bb8273ee311c12968b7e86d205fa;p=jalview.git diff --git a/help/html/webServices/proteinDisorder.html b/help/html/webServices/proteinDisorder.html index 5b88c59..1c35bf3 100644 --- a/help/html/webServices/proteinDisorder.html +++ b/help/html/webServices/proteinDisorder.html @@ -1,243 +1,250 @@ + -->
- 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 2.0 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. - |
-
+ 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 2.0 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. -
++ [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. -
+
+ 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. +