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Output format



DESCRIPTION


Prediction without filtering

The output from NetPhosK contains four columns: Column 1: position of the residue being analyzed Column 2: the residue Column 3: the predicted kinase (above the threshold ,default 0.500) Column 4: output score (value in the range [0.000-1.000])

Prediction with ESS filtering

Here the prediction is refined by a filtering process based on the evolutionary information obtained from sequence similarity and taxonomy. The Evolutionary Stable Sites (ESS) filter tests the same kinase prediction on homologues sites obtained from other homologues from higher eukaryotes. As most phosphorylation sites should be conserved in this taxonomic group, sites which are not positively predicted in all or a greater subset of the homologues can be filtered out. The output from NetPhosK with ESS filtering contains six columns: Column 1: position of the residue being analyzed Column 2: the residue Column 3: the predicted kinase (above the threshold ,default 0.500) Column 4: output score (value in the range [0.000-1.000]) Column 5: number of homologues with positive prediction Column 6: number of homologues with negative prediction

Kinase Landscapes

Consider the kinase specificity prediction along a amino acid sequence only at STY sites - if the prediction for different kinase are represented by different letters with heights equally to the prediction values and plotted along the sequence position, one can recognize kinase landscape - a potential region of the sliding kinase motif. Prediction of kinase specificity along an amino acid sequence is normally done at serine, threonine, or tyrosine residues. However, if kinase specificity is additionally predicted at non-STY sites, one can notice that the individual kinase predictions tend to cluster.




EXAMPLE OUTPUT

Example: Prediction without filtering

Method: NetPhosK without ESS filtering:
Name: q_EFTU_HUMAN
Pos AA Kinase Score:
5 T: PKC 0.777000
5 T: cdc2 0.520333
14 S: cdc2 0.547333
50 T: PKC 0.699500
72 T: PKC 0.655750
74 T: PKC 0.638000
107 T: PKA 0.579000
116 S: PKC 0.691750
141 T: PKC 0.536250
189 S: CKII 0.554000
273 T: PKA 0.624750
296 S: PKA 0.589000
296 S: cdc2 0.515000
304 T: CKII 0.532667
304 T: PKG 0.501000
345 S: PKC 0.874500
360 S: CKII 0.566333
423 T: PKC 0.724750
433 T: PKC 0.725750
443 T: CKII 0.678333
Highest Score: 0.874500 PKC at position 345

Example: Prediction with ESS filtering

Method: NetPhosK with ESS filtering
Name: SYN1_HUMAN
Pos: AA: Kinase: Score: Positiv: Negative:
==========================================
9 S cdc2 0.530333333333 1 6
9 S PKA 0.88575 1 6
9 S PKG 0.554 1 6
55 S cdc2 0.563666666667 1 6
55 S PKA 0.62825 1 6
78 S cdc2 0.524666666667 6 1
79 S cdc2 0.526666666667 6 1
81 S PKC 0.68325 1 6
124 T CKII 0.523666666667 6 1
177 S PKC 0.5165 1 6
177 S PKG 0.565 1 6
191 S PKC 0.738 2 5
218 S PKC 0.53725 5 2
260 S PKA 0.52575 1 6
293 S PKC 0.5485 6 1
298 T PKC 0.76775 6 1
330 S PKC 0.62725 6 1
332 S PKC 0.79425 6 1
350 S PKC 0.77475 7 0
359 T CKII 0.511666666667 6 1
359 T PKG 0.531 6 1
432 S PKC 0.63475 6 1
432 S PKA 0.595 5 2
448 T PKA 0.5395 5 2
494 S cdc2 0.579666666667 6 1
502 S cdc2 0.523666666667 6 1
510 S PKG 0.547 6 1
513 S cdc2 0.508333333333 5 2
533 S PKC 0.52075 6 1
567 T PKA 0.5075 4 3
567 T PKG 0.506 4 3
568 S CaM-II 0.5325 6 1
570 S PKC 0.7195 7 0
605 S CaM-II 0.518 6 1
624 S PKA 0.52875 7 0
663 S cdc2 0.524666666667 6 1
667 T PKC 0.68 5 2
681 S PKA 0.7365 1 6
683 S CKII 0.524333333333 1 6
694 S PKC 0.827 2 5
698 S PKA 0.64075 1 6

Example: Kinase Landscapes




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