 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9R053 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MEERYYPVIFPDERNFRPFTFDSLAAIEKRITIQKEKKKSKDKAATEPQP 50
51 RPQLDLKASRKLPKLYGDVPPDLIAKPLEDLDPFYKDHKTFMVLNKKRTI 100
101 YRFSAKRALFILGPFNPIRSFMIRISVHSVFSMFIICTVIINCMFMANNS 150
151 SVDSRPSSNIPEYVFIGIYVLEAVIKILARGFIVDEFSYLRDPWNWLDFI 200
201 VIGTAIAPCFLGNKVNNLSTLRTFRVLRALKAISVISGLKVIVGALLRSV 250
251 KKLVDVMVLTLFCLSIFALVGQQLFMGILSQKCIKDDCGPNAFSNKDCFV 300
301 KENDSEDFIMCGNWLGRRSCPDGSTCNKTTFNPDYNYTNFDSFGWSFLAM 350
351 FRVMTQDSWEKLYRQILRTSGIYFVFFFVVVIFLGSFYLLNLTLAVVTMA 400
401 YEEQNRNVAAETEAKEKMFQEAQQLLREEKEALVAMGIDRTSLNSLQASS 450
451 FSPKKRKFFGSKTRKSFFMRGSKTARASASDSEDDASKNPQLLEQTKRLS 500
501 QNLPVELFDEHVDPLHRQRALSAVSILTITMQEQEKSQEPCFPCGKNLAS 550
551 KYLVWECSPPWLCIKKVLQTIMTDPFTELAITICIIVNTVFLAMEHHNMD 600
601 NSLKDILKIGNWVFTGIFIAEMCLKIIALDPYHYFRHGWNIFDSIVALVS 650
651 LADVLFHKLSKNLSFLASLRVLRVFKLAKSWPTLNTLIKIIGHSVGALGN 700
701 LTVVLTIVVFIFSVVGMRLFGAKFNKTCSTSPESLRRWHMGDFYHSFLVV 750
751 FRILCGEWIENMWECMQEMEGSPLCVIVFVLIMVVGKLVVLNLFIALLLN 800
801 SFSNEEKDGNPEGETRKTKVQLALDRFSRAFYFMARALQNFCCKRCRRQN 850
851 SPKPNEATESFAGESRDTATLDTRSWKEYDSEMTLYTGQAGAPLAPLAKE 900
901 EDDMECCGECDASPTSQPSEEAQACDLPLKTKRLPSPDDHGVEMEVFSEE 950
951 DPNLTIQSARKKSDAASMLSECSTIDLNDIFRNLQKTVSPQKQPDRCFPK 1000
1001 GLSCIFLCCKTIKKKSPWVLWWNLRKTCYQIVKHSWFESFIIFVILLSSG 1050
1051 ALIFEDVNLPSRPQVEKLLKCTDNIFTFIFLLEMILKWVAFGFRKYFTSA 1100
1101 WCWLDFLIVVVSGLSLTNLPNLKSFRNLRALRPLRALSQFEGMKVVVNAL 1150
1151 MSAIPAILNVLLVCLIFWLIFCILGVNFFSGKFGRCINGTDINKYFNASN 1200
1201 VPNQSQCLVSNYTWKVPNVNFDNVGNAYLALLQVATYKGWLDIMNAAVDS 1250
1251 RGKDEQPAFEANLYAYLYFVVFIIFGSFFTLNLFIGVIIDNFNQQQKKLG 1300
1301 GQDIFMTEEQKKYYNAMKKLGTKKPQKPIPRPLNKCQAFVFDLVTSQVFD 1350
1351 VIILGLIVTNMIIMMAESEGQPNEVKKIFDILNIVFVVIFTVECLIKVFA 1400
1401 LRQHYFTNGWNLFDCVVVVLSIISTLVSGLENSNVFPPTLFRIVRLARIG 1450
1451 RILRLVRAARGIRTLLFALMMSLPSLFNIGLLLFLVMFIYAIFGMNWFSK 1500
1501 VKRGSGIDDIFNFDTFSGSMLCLFQITTSAGWDALLNPMLESKASCNSSS 1550
1551 QESCQQPQIAIVYFVSYIIISFLIVVNMYIAVILENFNTATEESEDPLGE 1600
1601 DDFEIFYEIWEKFDPEATQFIQYSSLSDFADALPEPLRVAKPNRFQFLMM 1650
1651 DLPMVMGDRLHCMDVLFAFTTRVLGNSSGLDTMKAMMEEKFMEANPFKKL 1700
1701 YEPIVTTTKRKEEEECAAVIQRAYRRHMEKMIKLKLKGRSSSSLQVFCNG 1750
1751 DLSSLDVPKIKVHCD 1765
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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