 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O88457 from www.uniprot.org...
The NucPred score for your sequence is 0.68 (see score help below)
1 MEERYYPVIFPDERNFRPFTSDSLAAIEKRIAIQKERKKSKDKAAAEPQP 50
51 RPQLDLKASRKLPKLYGDIPPELVAKPLEDLDPFYKDHKTFMVLNKKRTI 100
101 YRFSAKRALFILGPFNPLRSLMIRISVHSVFSMFIICTVIINCMFMANSM 150
151 ERSFDNDIPEYVFIGIYILEAVIKILARGFIVDEFSFLRDPWNWLDFIVI 200
201 GTAIATCFPGSQVNLSALRTFRVFRALKAISVISGLKVIVGALLRSVKKL 250
251 VDVMVLTLFCLSIFALVGQQLFMGILNQKCIKHNCGPNPASNKDCFEKEK 300
301 DSEDFIMCGTWLGSRPCPNGSTCDKTTLNPDNNYTKFDNFGWSFLAMFRV 350
351 MTQDSWERLYRQILRTSGIYFVFFFVVVIFLGSFYLLNLTLAVVTMAYEE 400
401 QNRNVAAETEAKEKMFQEAQQLLREEKEALVAMGIDRSSLNSLQASSFSP 450
451 KKRKFFGSKTRKSFFMRGSKTAQASASDSEDDASKNPQLLEQTKRLSQNL 500
501 PVDLFDEHVDPLHRQRALSAVSILTITMQEQEKFQEPCFPCGKNLASKYL 550
551 VWDCSPQWLCIKKVLRTIMTDPFTELAITICIIINTVFLAVEHHNMDDNL 600
601 KTILKIGNWVFTGIFIAEMCLKIIALDPYHYFRHGWNVFDSIVALLSLAD 650
651 VLYNTLSDNNRSFLASLRVLRVFKLAKSWPTLNTLIKIIGHSVGALGNLT 700
701 VVLTIVVFIFSVVGMRLFGTKFNKTAYATQERPRRRWHMDNFYHSFLVVF 750
751 RILCGEWIENMWGCMQDMDGSPLCIIVFVLIMVIGKLVVLNLFIALLLNS 800
801 FSNEEKDGSLEGETRKTKVQLALDRFRRAFSFMLHALQSFCCKKCRRKNS 850
851 PKPKETTESFAGENKDSILPDARPWKEYDTDMALYTGQAGAPLAPLAEVE 900
901 DDVEYCGEGGALPTSQHSAGVQAGDLPPETKQLTSPDDQGVEMEVFSEED 950
951 LHLSIQSPRKKSDAVSMLSECSTIDLNDIFRNLQKTVSPKKQPDRCFPKG 1000
1001 LSCHFLCHKTDKRKSPWVLWWNIRKTCYQIVKHSWFESFIIFVILLSSGA 1050
1051 LIFEDVNLPSRPQVEKLLRCTDNIFTFIFLLEMILKWVAFGFRRYFTSAW 1100
1101 CWLDFLIVVVSVLSLMNLPSLKSFRTLRALRPLRALSQFEGMKVVVYALI 1150
1151 SAIPAILNVLLVCLIFWLVFCILGVNLFSGKFGRCINGTDINMYLDFTEV 1200
1201 PNRSQCNISNYSWKVPQVNFDNVGNAYLALLQVATYKGWLEIMNAAVDSR 1250
1251 EKDEQPDFEANLYAYLYFVVFIIFGSFFTLNLFIGVIIDNFNQQQKKLGG 1300
1301 QDIFMTEEQKKYYNAMKKLGTKKPQKPIPRPLNKCQAFVFDLVTSQVFDV 1350
1351 IILGLIVLNMIIMMAESADQPKDVKKTFDILNIAFVVIFTIECLIKVFAL 1400
1401 RQHYFTNGWNLFDCVVVVLSIISTLVSRLEDSDISFPPTLFRVVRLARIG 1450
1451 RILRLVRAARGIRTLLFALMMSLPSLFNIGLLLFLVMFIYAIFGMSWFSK 1500
1501 VKKGSGIDDIFNFETFTGSMLCLFQITTSAGWDTLLNPMLEAKEHCNSSS 1550
1551 QDSCQQPQIAVVYFVSYIIISFLIVVNMYIAVILENFNTATEESEDPLGE 1600
1601 DDFEIFYEVWEKFDPEASQFIQYSALSDFADALPEPLRVAKPNKFQFLVM 1650
1651 DLPMVMGDRLHCMDVLFAFTTRVLGDSSGLDTMKTMMEEKFMEANPFKKL 1700
1701 YEPIVTTTKRKEEEQGAAVIQRAYRKHMEKMVKLRLKDRSSSSHQVFCNG 1750
1751 DLSSLDVAKVKVHND 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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