 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P42527 from www.uniprot.org...
The NucPred score for your sequence is 0.88 (see score help below)
1 MFNIKKRKESITGIPPINVNSPQSVPLSGTLQSPLITPNSPNFVSRQCPF 50
51 KKFGCSSFLVSKAEFDNHLKDDAQFHLQLAVEKFDHQFDLHTQLMAHFTE 100
101 QMEDQLEKTMKVVRNHTDSLGGNVQTKLDEGIEKCMAFAKKVEQQQQQLA 150
151 KRLITQQIQEKKSTSSPLVKGGISGGGGSGGDDSFDGANISSMSTSKQEL 200
201 QQELQSLSIKMKKELTELSDELSQKLERSTGNIDIKIKRIEGEVNEKIDK 250
251 RQLVSTIDDSIGKKTDSIGYTLESSIIKKVEEKEKKKSEQNQLLFDSKIE 300
301 SLKDKIKIIETQQLDTSSEVRKLKLESTSSGNLMAGLNGTSGRPSSSSHF 350
351 IPSSVSAAANNINKNEIMEEVKKVEEKLQKKIREEIDNTKSELSKVERSV 400
401 KDNRSEIEGLEKDCKNQFDKQDNKIKQVEDDLKKSDSLLLLMQNNLKKYN 450
451 EFVDRERDRESERLKLQDSIKRLEQNQKKIEAEIQEGNEQVERVLREEAS 500
501 ISPISSVPKSPITTKRSSIILNSPPMTSQQSSPKIQDLLSSSGSSSVSGI 550
551 NISSETGEMGILWEFDPIINKWIRLSMKLKVERKPFAEGALREAYHTVSL 600
601 GVGTDENYPLGTTTKLFPPIEMISPISKNNEAMTQLKNGTKFVLKLYKKE 650
651 AEQQASRELYFEDVKMQMVCRDWGNKFNQKKPPKKIEFLMSWVVELIDRS 700
701 PSSNGQPILCSIEPLLVGEFKKNNSNYGAVLTNRSTPQAFSHFTYELSNK 750
751 QMIVVDIQGVDDLYTDPQIHTPDGKGFGLGNLGKAGINKFITTHKCNAVC 800
801 ALLDLDVKLGGVLSGNNKKQLQQGTMVMPDILPELMPSDNTIKVGAKQLP 850
851 KAEFSKKDLKCVSTIQSFRERVNSIAFFDNQKLLCAGYGDGTYRVFDVND 900
901 NWKCLYTVNGHRKSIESIACNSNYIFTSSPDNTIKVHIIRSGNTKCIETL 950
951 VGHTGEVNCVVANEKYLFSCSYDKTIKVWDLSTFKEIKSFEGVHTKYIKT 1000
1001 LALSGRYLFSGGNDQIIYVWDTETLSMLFNMQGHEDWVLSLHCTASYLFS 1050
1051 TSKDNVIKIWDLSNFSCIDTLKGHWNSVSSCVVKDRYLYSGSEDNSIKVW 1100
1101 DLDTLECVYTIPKSHSLGVKCLMVFNNQIISAAFDGSIKVWEWQSK 1146
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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