 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q08217 from www.uniprot.org...
The NucPred score for your sequence is 0.68 (see score help below)
1 MTYPVSAAAPADISYSKNTPLVGLSKPPCLYQHASSSVDSFSSTFSDDDR 50
51 SDLVAVPNESPHAFSYNPISPNSLGVRLTILRRSLEIMVNSPDILHELKK 100
101 KAPVIAYPPSLRHTRNLTETATLSASRDPLNGSLISPLVSNMPSPASRPV 150
151 IQRATSLMVLPDNDTASKLNPAKSELENLLFLLNLALENNSFERASDLHM 200
201 LSLLNIKKINFDSDIQKSETLKKVLLDSLAEPFFENYKKFPHKDLGSKSQ 250
251 YNEYEEKHDDIVSLADIKPQQDYSRILHPFTSAKNSGPEAIFTCSQQYPW 300
301 NFKAANDLACLTFGISKNVIKALTLLDLIHTDSRNFVLEKIMNAEDDNQE 350
351 IVFTGETIPIVQPNSTSNNNVPNLIWASLWAKRKNGLLVCVFEKTPCDYI 400
401 DVMLNLRDFSVDSIIDTTHFLENFDKKKQQESTSPMTEKKTVKFANEIHD 450
451 IGSVSHSLSKLIDDVRFGKVFSADDDLLPLSIRVANHVNEERYFTLNCLS 500
501 ENIPCAVTTSVLENEIKLKIHSLPYQAGLFIVDSHTLSLLSFNKSVAKNM 550
551 FGLRLHELAGSSVTKLVPSLADMISYINKTYPMLNITLPENKGLVLTEHF 600
601 FRKIEAEMHHDKDSFYTSIGLDGCHKDGNLIKVDVQLRVLNTNAVLLWIT 650
651 HSRDVVIENYTTVPSQLPMLKENEIDVVGSRGSSSASSKKSSEKIPVNTL 700
701 KAMADLSISSAETISNSDDEVDLNQVNEKLRETSCGKVRGIESNDNNNYD 750
751 DDMTMVDDPELKHKIELTKMYTQDKSKFVKDDNFKVDEKFIMRIIEPING 800
801 EEIKKETNELDKRNSTLKATYLTTPEANIGSQKRIKKFSDFTILQVMGEG 850
851 AYGKVNLCIHNREHYIVVIKMIFKERILVDTWVRDRKLGTIPSEIQIMAT 900
901 LNKNSQENILKLLDFFEDDDYYYIETPVHGETGSIDLFDVIEFKKDMVEH 950
951 EAKLVFKQVVASIKHLHDQGIVHRDIKDENVIVDSHGFVKLIDFGSAAYI 1000
1001 KSGPFDVFVGTMDYAAPEVLGGSSYKGKPQDIWALGVLLYTIIYKENPYY 1050
1051 NIDEILEGELRFDKSEHVSEECISLIKRILTREVDKRPTIDEIYEDKWLK 1100
1101 I 1101
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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