 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P13590 from www.uniprot.org...
The NucPred score for your sequence is 0.22 (see score help below)
1 MLPAAALPWTLFFLGAAASLQVDIVPSQGEISVGESKFFLCQVAGEAKYK 50
51 DISWFSPNGEKLTPNQQRISVVRNDDFSSTLTIYNANIDDAGIYKCVVSS 100
101 VEEGDSEATVNVKIFQKLMFKNAPTPQEFKEGDDAVIVCDVVSSLPPTII 150
151 WKHKGRDVMLKKDVRFIVLSNNYLQIRGIKKTDEGTYRCEGRILARGEIN 200
201 FKDIQVIVNVPPSVRARQSTMNATANLSQSVTLACDADGFPEPTMTWTKD 250
251 GEPIEQEDNEEKYSFNYDGSELIIKKVDKSDEAEYICIAENKAGEQDATI 300
301 HLKVFAKPKITYVENKTAMELEDQITLTCEASGDPIPSITWKTSTRNISN 350
351 EEKTLDGRIVVRSHARVSSLTLKEIQYTDAGEYVCTASNTIGQDSQAMYL 400
401 EVQYAPKLQGPVAVYTWEGNQVNITCEVFAYPSAVISWFRDGQLLPSSNY 450
451 SNIKIYNTPSASYLEVTPDSENDFGNYNCTAVNRIGQESSEFILVQADTP 500
501 SSPSIDRVEPYSSTARVEFDEPEATGGVPILKYKAEWRALGEGEWHSRLY 550
551 DAKEANVEGTITISGLKPETTYSVRLSAVNGKGVGEISLPSDFKTQPVRE 600
601 PSAPKLEGQMGEDGNSIKVNVIKQDDGGSPIRHYLIKYKAKHSSEWKPEI 650
651 RLPSGIDHVMLKSLDWNAEYEVYVIAENQQGKSKPAHYAFRTSAQPTVIP 700
701 ASTSPTSGLGTAAIVGILIVIFVLLLVAVDVTCYFLNKCGLLMCIAVNLC 750
751 GKSGPGAKGKDMEEGKAAFSKDESKEPIVEVRTEEERTPNHDGGKHTEPN 800
801 ETTPLTEPEHTADTAATVEDMLPSVTTGTTNSDTITETFATAQNSPTSET 850
851 TTLTSSIAPPATAIPDSNAMSPGQATPAKAGASPVSPPPPSSTPKVAPLV 900
901 DLSDTPSSAPATNNLSSSVLSNQGAVLSPSTVANMAETSKAAAGNKSAAP 950
951 TPANLTSPPAPSEPKQEVSSTKSPEKEAAQPSTVKSPTETAKNPSNPKSE 1000
1001 AASGGTTNPSQNEDFKMDEGTFKTPDIDLAKDVFAALGTTTPASVASGQA 1050
1051 RELASSTADSSVPAAPAKTEKTPVEDKSEVQATEIRHLQQK 1091
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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