 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P48119 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MTQLTDTSLIPFDLLEIQKASFKWFFEEGLIEEIENFSPVIDLRGNFEVH 50
51 FYLKNYELEAPTFTLEEAKQRDLTYAAQLYIPVELRDLETGIVKQDRIFF 100
101 GEIPLMTDRCTFLINGVERVIINQIIRSPGIYYSVNTDEQGTRTYDVTII 150
151 SNRGAWLKFEVDKDDLIWMRIDKTHRLPCHIFLKALNLSESEILSALTHP 200
201 EFLQKTIYEHGDCTEEEALIEFHRQLRPGEPPNSIQGRYLLYSRFFDPKR 250
251 YDLGFVGRYKLNKKLNLNVSPNIRILTTQDILEILNYLINLQFGMGQIDD 300
301 IDHLGNRRVKAVGELLQSQLRIGLNRLERLIHDRLSMLTTKSFKKRKKLT 350
351 TLINAKPINECLKEFFGSSQLSQFMDQTNPLAELIHKRRLTILGPGGLSR 400
401 DRAGCAVRDIHPSHYGRICPIETPEGQNAGLVGSLTTHAHLNQYGFIETP 450
451 FYKVEKGYIQKELGIIYLTADEEDQYRIAAADVLINETNKIIGEDISVRY 500
501 RQEFITTKIDQIDFIAISPLQTFSVSTSLIPFLEHDDANRALMGSNMQRQ 550
551 AVPLLHAEKPLVGTGLEFQVAKDSRRVLINKSEGLVKRVTGDHICIQTDT 600
601 GKEINYTLIKYQRSNQDTCITQRPIVFEGERVKKGQILADDTATDKGELA 650
651 LGQNLLVAYMPWEGYNYEDAILINERLVYEDVYTSIHIEKYETETRQTKL 700
701 GIEEITREIPNVNEYSLRNLDEKGIVSVGSWIKGGDILVGKVAPKAESDQ 750
751 PPEGKLLKAIFGEKNRDVRDTSLRMPSGEKGRIVDVRIFIREFGEMLYPG 800
801 ANTANTIVRIYIAQKRKIQVGDKMAGRHGNKGIISKILPRQDMPYLPDGT 850
851 PIDIILNPLGVPSRMNVGQVYECLLGWAAEHLGVRFKLIPFDERFGKQAS 900
901 RTFIHEKLKEAKELTNKNWLFNSNHPGKIQLFDGRTGESFDNPIMVGKAY 950
951 MLKLVHLVDDKIHARSTGPYSLITQQPLGGKAQQGGQRFGEMEVWALEAF 1000
1001 GAAYTLQEILTIKSDDMLGRNEALKAIVKGKAIPRPGIPESFKVLMRELQ 1050
1051 ALCLDVVAYKIQNGDQDECEALAIDLSGSLATPIDNIIKEVIQQEELGEE 1100
1101 LTS 1103
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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