| Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8CBD1 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MTHGEELGSDVHQDSIVLTYLEGLLMHQAAGGSGTAINKKSAGHKEEDQN 50
51 FNLSGSAFPSCQSNGPTVSTQTYQGSGMLHLKKARLLQSSEDWNAAKRKR 100
101 LSDSIVNLNVKKEALLAGMVDSVPKGKQDSTLLASLLQSFSSRLQTVALS 150
151 QQIRQSLKEQGYALSHESLKVEKDLRCYGVASSHLKTLLKKSKTKDQKSG 200
201 PTLPDVTPNLIRDSFVESSHPAVGQSGTKVMSEPLSCAARLQAVASMVEK 250
251 RASPAASPKPSVACSQLALLLSSEAHLQQYSREHALKTQNAHQVASERLA 300
301 AMARLQENGQKDVGSSQLSKGVSGHLNGQARALPASKLVANKNNAATFQS 350
351 PMGVVPSSPKNTSYKNSLERNNLKQAANNSLLLHLLKSQTIPTPMNGHSQ 400
401 NERASSFESSTPTTIDEYSDNNPSFTDDSSGDESSYSNCVPIDLSCKHRI 450
451 EKPEAERPVSLENLTQSLLNTWDPKIPGVDIKEDQDTSTNSKLNSHQKVT 500
501 LLQLLLGHKSEETVERNASPQDIHSDGTKFSPQNYTRTSVIESPSTNRTT 550
551 PVSTPPLYTASQAESPINLSQHSLVIKWNSPPYACSTPASKLTNTAPSHL 600
601 MDLTKGKESQAEKPAPSEGAQNSATFSASKLLQNLAQCGLQSSGPGEEQR 650
651 PCKQLLSGNPDKPLGLIDRLNSPLLSNKTNAAEESKAFSSQPAGPEPGLP 700
701 GCEIENLLERRTVLQLLLGNSSKGKNEKKEKTPARDEAPQEHSERAANEQ 750
751 ILMVKIKSEPCDDFQTHNTNLPLNHDAKSAPFLGVTPAIHRSTAALPVSE 800
801 DFKSEPASPQDFSFSKNGLLSRLLRQNQESYPADEQDKSHRNSELPTLES 850
851 KNICMVPKKRKLYTEPLENPFKKMKNTAVDTANHHSGPEVLYGSLLHQEE 900
901 LKFSRNELDYKYPAGHSSASDGDHRSWARESKSFNVLKQLLLSENCVRDL 950
951 SPHRSDSVPDTKKKGHKNNAPGSKPEFGISSLNGLMYSSPQPGSCVTDHR 1000
1001 TFSYPGMVKTPLSPPFPEHLGCVGSRPEPGLLNGCSVPGEKGPIKWVIAD 1050
1051 MDKNEYEKDSPRLTKTNPILYYMLQKGGGNSVTTQETQDKDIWREPASAE 1100
1101 SLSQVTVKEELLPAAETKASFFNLRSPYNSHMGNNASRPHSTNGEVYGLL 1150
1151 GNALTIKKESE 1161
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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