 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9P7V5 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MQDASEPVINMPALPMNSVNEQHDRHEDLPEVGTTSVTKIINDSDNEDDE 50
51 NGMDLGRVASESLEGDAVVSIDINTEDSSLSPAKQENEKSPEGIEQKYQE 100
101 EDLKDDKKSNETIATPVPDAASRASLEKQKSGLTNLEKSTHVVLIKCIEQ 150
151 LIQIKVCMKNEKMKNLMEQLKPLLQTECQFDKFLIVELFQYCFESSQDEV 200
201 MNISLDTISKLASFAYFSSKDKTPASFGPPKSLLQCMVDMVCDSINDEVV 250
251 DGNLQLNVVKALSAFILCSEQDSMLHGAILLNSVRKLFNVFLLGDSDTIQ 300
301 SVAQASLTQAVTVVYERLRASHTQSNSTSALPEEDASVTENWVHDEDEPD 350
351 KKITLHSMASAGTSSLDHVKVDADDPAVTSVENSSIQDAFLVFRSMCRLA 400
401 VRQTSPDKVSNIRSQAMRAKLISLHLIYRILEKNSDLFMDPTLQFRGIPA 450
451 LKGMTLVHASRQYICLVLSRNAVSPVPQVFEVCCDIFYLMVFSLRAHFKQ 500
501 EIEVFFREVYFPMLDLKNTSYNQKLHTLLIIQRICLNPRALVELYINYDC 550
551 DRSSTTNVFEQLLFSISKVTTNGPSETISEDIEEILPSLESSERSSTPFL 600
601 NTNSASLKSEVVQLTTFSDFQLKLKTLQCVLDILQSLSNWAESGLYLSRR 650
651 GVSTDEQGFVGDYDALSRSDTPVTNPYYNGKQSFEANSHSSSSIALADPS 700
701 QFESNKQRKKLLRTCINKFNYKPTRGLKMLSENEYVDINDPKAIAEFLFR 750
751 ADGIDKTTLGDYLGEGDEKSISVMHEFIDCLSFINLKFVDALRRLLQCFR 800
801 LPGEAQKIDRIMLKFSERYMKENPSAFANADTAYILAYSIILLNTDLHSP 850
851 RIKNKMTKEDFIKNNRGINDGADLDEDYLGFVYDDILKNEIAMKDDQELA 900
901 AIAPLMNNFSTSSGFTTFTSNGRDLQRVACIQASEEMANKATSVLKKLLY 950
951 QQKHGSQKTNVYYNATHFEHIGPMLEATWMPILAALSNPLQNSDYVNELN 1000
1001 MCLDGFQLVVRIACLFDLDLIRDAFIKTLTNFTNLHSTSEIKLRNTMVIK 1050
1051 TLLRIASTEGNNLKDSWKDILTIISQLERVQLIGVGVDETEVPDVINARV 1100
1101 RRKNVNIGSSNSIRHVSGSTSRSTRTRSLSKPLSPEAVSELMSTEVVLSI 1150
1151 DRIFTQTSSLSGSAIVSFFKALCEVSWDEITSSSDLEQPRLYSLQKLVEI 1200
1201 SYYNMQRIRVEWSSIWNVLGRFFNMVGSDENRHVAVFALDSLRQLSMHFL 1250
1251 EIEELSLFSFQKEFLKPFEYVMASDTVVEVKELVLQCVKQMIQAKISKIK 1300
1301 SGWKTLFGVFTFAAKARSEILISMTFDTLVNLFSEHYDTLMQQNCLIDML 1350
1351 ISFTELCKNGTNQKISLQSLEIIREVYSSLSTMIKEGLSSKPSVNETFSK 1400
1401 YVFPVLFAYYDIIMSAEDLEVRSRALQNLFYIFLEESDDFTEETWEVVSR 1450
1451 KFIFPIFSIFGPEADEATVMLRDEEIRTWQSTTLVEALRSLVTLLTRRFD 1500
1501 KLHNLLKGYLWLFSNCICRDNITLSRIGTNCMQQLLSGNAYRFEVKDWNL 1550
1551 VADMFIELFKETTPHQLLLLETFSNGQGAPVYSENENTQLSHKRGGSLPE 1600
1601 TSRSISTSSISPEKQMEFRSMIRKCILQLLLISIVAELLDNEEVFNHIPH 1650
1651 EHVLKITVAIYDSWQFARKFNEDKSLRITLLNVGFMKQLPNLLRQETASA 1700
1701 LLYITLLFRLLKTRDPLGKTETDQKIHKLLFPVCAEMLDMYASLVVEKHT 1750
1751 RNHAAWQPVIATILDSILNLPLELFSENIHTLYFSCCSMIAKENLDDQLR 1800
1801 ELLKNYFNRVGHILLNLNAQQE 1822
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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