 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P54210 from www.uniprot.org...
The NucPred score for your sequence is 0.08 (see score help below)
1 MSGKERTEENGAVKQDTKEQVKKSADNGDKGVDEVDFAKIGLEDAFKYLN 50
51 CSEHGLSGAEAEARLKQHGPNKLPDNSRNPVLVYFGYMWNPLAWAMEAAA 100
101 IIAIALVDGADFALIVGLLIINATISFVEESNADKAIKALSAALAPKAMA 150
151 LRNGAMVTIDAVDLVPGDVILIRIGNVVPADVKLLPEHGADDYETPVQID 200
201 QAALTGESLPAKKFTGNVAFSGSTVKQGERHAVVYATGVNTFFGRAAALI 250
251 SGTHNVANIQRVMNRIGGLCLITIGVWVVIEVPVQFAHYKHSCVAGKEGC 300
301 PTLLNMLVILVGAIPIAMPTVLSVTLALGAYKLAREGAIVTRMSAVEEMA 350
351 GLDVLCSDKTGTLTLNKLSIDPSNVFPVGTMDIPEVMKFGALSANIITEE 400
401 PIDMVLWESYPEREKLKSEYKHTKYFPFNPNDKITIATVLEIATGRVFRV 450
451 LKGSPQVVLAKAWNAQALDGPVNEKIKEYAGRGFRSLGIAMAEGDGKDGT 500
501 KWEMLAVLPMFDPPRHDTKETIERCMKQGIAVKMVTGDHLLIGKETAKML 550
551 GMGTEMYPSEVLIKARNGDVEAPHGYKNYVAMVEACNGFAQVFPEHKFEI 600
601 VEILQEAHHRVGMTGDGVNDAPALKKAHVGVAVADATDAARGAADIVLTE 650
651 PGLSTIVTAVIGARKIFKRMTTYAKYTISVTFRIAFTFGLLTVIYDWYFP 700
701 TILIVILAVFNDGAMIALSKDRVVASVLPSTWNLATIFVPGFVYAMWLTL 750
751 SSWALYQVATHSTFFERMTPLPSLNTQHATLISWCEDEISSKLGVNPQDS 800
801 LCTYPSYADQLNECKGSVSLSSQVPGVPTILDQCVTEQRYIRDALTRALI 850
851 YTHLSVSGQAVVFVVRTSGFSLKEVAGVSTYVAFALAQFGATMFGIFGLG 900
901 GYNKPRQNFDNCQFCDYSTHNRVLFFNSEVEPRAGTESVYTASVIGCGGY 950
951 VIVAWIWAALFYTALDPLKWGLMWIMNDDGFRDRHAWRKSNHEAMERRSR 1000
1001 EQLDNKEFAGPSGMVPANFSNPLGRASMSKPVSALLDRKSASLVAINRSS 1050
1051 MTVSHDPNHALNIGRRSMIGRPSGPLGRNSNTGQSNPLNSSSVEIKPDAP 1100
1101 NKV 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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