 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P53819 from www.uniprot.org...
The NucPred score for your sequence is 0.83 (see score help below)
1 MEIENEQICTCIAQILHLLNSLIITFLDDDKTETGQSFVYIDGFLVKKHN 50
51 NQHTIVNFETYKNKMKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLF 100
101 ESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKIVVSVMVGKNVQK 150
151 FLTFVEDEPDFQGGPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLS 200
201 LFYLNRGYYNELSFRVLERCYEIASARPNDSSTMRTFTDFVSGTPIVRGL 250
251 QKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKRDL 300
301 CPRKPTEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGD 350
351 EEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSC 400
401 ACTARDLDIFDDNNGVAMWKWIKILYHEVAQETALKDSYRITLVPSSDGV 450
451 SVCGKLFNREYVRGFYFACKAQFDNLWEELNDCFYMPTVVDIASLILRNR 500
501 EVLFREPKRGIDEYLENDSFLQMIPVKYREIVLPKLRRDTNKMTAALKNK 550
551 VTVAIDELTVPLMWMIHFAVGYPYRYPELQLLAFAGPQRNVYVDDTTRRI 600
601 QLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFN 650
651 HDPWMDVVGFEDPDQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMR 700
701 ELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESATSELMANHSVQ 750
751 TGRNIYGVDSFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTR 800
801 NAEDVVAGEAASSDHHQKISRVTRKRPREPKSTNDILVAGQKLFGSSFEF 850
851 RDLHQLRLCHEIYMADTPSVAVQAPPGYGKTELFHLPLIALASKGDVKYV 900
901 SFLFVPYTVLLANCMIRLSRCGCLNVAPVRNFIEEGCDGVTDLYVGIYDD 950
951 LASTNFTDRIAAWENIVECTFRTNNVKLGYLIVDEFHNFETEVYRQSQFG 1000
1001 GITNLDFDAFEKAIFLSGTAPEAVADAALQRIGLTGLAKKSMDINELKRS 1050
1051 EDLSRGLSSYPTRMFNLIKEKSEVPLGHVHKIWKKVESQPEEALKLLLAL 1100
1101 FEIEPESKAIVVASTTNEVEELACSWRKYFRVVWIHGKLGAAEKVSRTKE 1150
1151 FVTDGSMRVLIGTKLVTEGIDIKQLMMVIMLDNRLNIIELIQGVGRLRDG 1200
1201 GLCYLLSRKNSWAARNRKGELPPIKEGCITEQVREFYGLESKKGKKGQHV 1250
1251 GCCGSRTDLSADTVELIERMDRLAEKQATASMSIVALPSSFQESNSSDRC 1300
1301 RKYCSSDEDSDTCIHGSANASTNATTNSSTNATTTASTNVRTSATTTASI 1350
1351 NVRTSATTTESTNSSTNATTTASTNVRTSATTTASINVRTSATTTESTNS 1400
1401 NTSATTTESTDSNTSATTTESTDSNTSATTTASTNSSTNATTTASTNSST 1450
1451 NATTTESTNASAKEDANKDGNAEDNRFHPVTDINKESYKRKGSQMVLLER 1500
1501 KKLKAQFPNTSENMNVLQFLGFRSDEIKHLFLYGIDVYFCPEGVFTQYGL 1550
1551 CKGCQKMFELCVCWAGQKVSYRRMAWEALAVERMLRNDEEYKEYLEDIEP 1600
1601 YHGDPVGYLKFFSVKRGEIYSQIQRNYAWYLAITRRRETISVLDSTRGKQ 1650
1651 GSQVFRMSGRQIKELYYKVWSNLRESKTEVLQYFLNWDEKKCREEWEAKD 1700
1701 DTVFVEALEKVGVFQRLRSMTSAGLQGPQYVKLQFSRHHRQLRSRYELSL 1750
1751 GMHLRDQLALGVTPSKVPHWTAFLSMLIGLFCNKTFRQKLEYLLEQISEV 1800
1801 WLLPHWLDLANVEVLAADNTRVPLYMLMVAVHKELDSDDVPDGRFDILLC 1850
1851 RDSSREVGE 1859
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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