 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9D217 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MEEFLQRAKSKLDRSKQLEQVHAVIGPKSCDLDSLISAFTYAYFLDKVSP 50
51 PGVLCLPVLNIPRTEFNYFTETRFILEELNIPESFHIFRDEINLHQLNDE 100
101 GKLSITLVGSHVLGSEDRTLESAVVRVINPGEQSDGELGFPETSSSLVLK 150
151 ELLREAPELITQQLAHLLRGSILFTWMSMDPELPEKQEEILSILEEQFPN 200
201 LPPRDDIINVLQESQLSAQGLSLEQTMLKDLKELSDGEIKVAISTVNMTL 250
251 EDYLLHGNITSDLKAFTDKFGFDVLILISSFTWEEQQRQQIAVYSQNLEL 300
301 CSQICCELEESQNPCLELEPFECGCDEILVYQQEDPSVTSDQVFLLLKEV 350
351 INRRCAEMVSNSRTSSTEAVAGSAPLSQGSSGIMELYGSDIEPQPSSVNF 400
401 IENPPELNDSNQAQADGNIDLVSPDSGLATIRSSRSSKESSVFLSDDSPV 450
451 GDGGAPHHSLLPGFDSYSPIPEGIVAEEHAHSGEHSEHFDLFNFDSAPIA 500
501 SEQSQPSSHSADYSPEDDFPNSDSSEGNLSAGPKGLGEMGINMSNYSSSS 550
551 LLSEAGKDSLVEFDEEFIQRQESSGDNSERNLSLTCFAGEEPSSPERLKN 600
601 PGKMIPPTPMNSFVEISPSNEEPTPLYPEDIIQNAIDTGHLGPPQARARC 650
651 RSWWGGLEIDSKNVVDTWNASEQESVFQSPEPWKDPKPEPVERRTSDSTF 700
701 QPKSLEFSKSDPWESEFGQPELGNKEAQDQKEESLQYQHLPTVRPHLTDA 750
751 SPHGTNHLIEDFAALWHSGHSPTTMPEPWGNPTDAGEAAVTMSFPTWGAF 800
801 DKEEDNADTLKNTWNLHPTNNETPSGQEPSEWAMGQSGFSFPAADLLDNP 850
851 LIEVNKDAAPEIWGKNNSSKDTSLTSGSPTSDLGQTWNNSKLPGEDQNGL 900
901 VDPKATGKVYEKEGSWSLFEESAKKRGADVLAPWEDSFLSYKCSDYSASN 950
951 IGEDSVPSPLDTNYSTSDSYTSPTYAGDEKEIANKPVDKDNGFEAKDAEF 1000
1001 PAEGLEVLATSSQQSQRNRIGSGPGNLDMWALPHTEDKPEGNDAHHPDSD 1050
1051 ALKTEHAEDKNASMEDDVRESSPSSYDDPSMMRLYEANRQLTLLHSNTNS 1100
1101 RQAAPDSLDTWNRVTLEDTQSTATISDMDNDLDWDDCSGGVAISGDGQAE 1150
1151 GYIAANSEPETRFSVKQLEPWGTEHQEANQVDWDLSASAEPTGDPGPSEY 1200
1201 QTLNEKTGQLIANSIWDSVMGDKNMPSFRLPSPPNTVDMEHGTWPSESPR 1250
1251 HSNGKDSHMLEASRLSESGGLTSQPVNQDTWGDSQGDTASSVTGLASPEH 1300
1301 FAQSDPWTGHTYGQSESEIEGLVASDCEHLDKEAALGSGVNGAPWAFGKK 1350
1351 PRDQEFSSSDAFEHQDISSASGKISSLSVTSSPQSEEPGEALEVEKEPFI 1400
1401 LDSLAVQTETFTWDLQSKDTHEESLVDHRNLGEANTTLDRINPMKNRPLS 1450
1451 GMELEKTEACTILKPERANGKLLYESSQDFGVWDGPMDSDVWDSHISYET 1500
1501 AMNSTGQRTEERSLEALSPGNYDRDSLSSGCTHSSASSPDLHDSSVALSS 1550
1551 WTYGPSAEHQKENHDDANKQIHQESELFTTEAHVDVITEMKDFVENREDG 1600
1601 FGKMSNQEDPQFPEIPNDPFNSGPSSSSSSLGADKYSEYSHAYQEGDLTT 1650
1651 KRQEENELGFLEIVEPEGTRIISTSSGSGNDSGGDEELLEKELHLATVAQ 1700
1701 SEAGACTSLHEPAFSATEHSKPEFSVFVGSLESIEKENKSSPFSDSQQSS 1750
1751 PGQWILSPLMQADTQDTCKEETRAAETGTMDTTWHGSASTEAKNGDPDKL 1800
1801 EMLGFSADSTEWWNAGAQEGRANAGMSAEELSNSEGELEPTSPVFQNAGP 1850
1851 WSLPIQNDSEPVDTGSTNPFRGKLKSPVLDSHGDKSQEKLWNIQPKQLDS 1900
1901 DANQLSQLVILDQMKDKDSGQQTAMSPAAGDLPAETLTQGQGRESMLSVW 1950
1951 DRAEPALTHRDENGCVSTGVSPTECQQENQWEPEKPYLSHVTHSSTPTEN 2000
2001 ALESNAPTQLMRKLDSDWNSPSPSEPQHNFVPDILHGNFEEGGQLASASP 2050
2051 DLWMDVKQPFSFKVDSENPDILTHCDHDSNSQASSSPDVCHDSEGEQKME 2100
2101 KHTAVYLGLEVEPSEFSLTEPNMNDEPTWEPEQESLPHNSELHSEHAMPL 2150
2151 PPIDSQNDINNSSKPASSRSSPEPSDMRGDNNTSVTSMEEDTNPEVEAVD 2200
2201 SVTIPGHFPRSEDADTFEAHQEVSVEVDDSWVSKDLCPESQTGTRALLDC 2250
2251 EQPFASESPAVLTDIFLTSDTCLDVSEAALDHSFSDASGLNTSTGTIDDM 2300
2301 SKLTLSEGHPETPVDGDAGKQDVCSSEASWGDFEYDAMGQNIDEELMREP 2350
2351 EHFLYGGDLPSEESALKQSLTPYTPPFDLSYLTEPTGCTETAQGAESPGD 2400
2401 ESLGSDAAEMLLSALPDHREEDKAETNIRKPRYQMTVLHIHEDPEALSSP 2450
2451 VGGTGSNNESSPSNIDWEIETDNSDSPAGGDMKPPNGKEILELEEDEKVI 2500
2501 PTKGPKQTELEYKEEKQPEQSEDHQVLAVDYILVSHEKDSPLKPEAREAR 2550
2551 ENIPELEQLSIGSRETGLPETQLSGTPDTCQSEFLNDVKVHSAERMSSSS 2600
2601 NHESASLENPAQDQSWMVLSHSEVGDPPTETRDSGPESPGRTPEPFLSLS 2650
2651 LDKGPKSQVLERNKPLNSLALEEVAGLSSQSRNIERQGQAGLDAVPTQAA 2700
2701 THDNEWEMLSPQLSRKNRNPPQEMEEETQCPEPGPRKPRLKGPPSEDEGM 2750
2751 DIHFEEGVLSPSAADMRPEPPNSLDLNGSHPRRIKLTAPNINLSLDQSEG 2800
2801 SILSDDNLDSPDEIDINVDELDTPDEADSFEYTNHEDPTANKSSGQESES 2850
2851 IPEYTAEEEREDNRLWRTVVIGEQEQRIDMKVIEPYRRVISHGGYYGDGL 2900
2901 NAIIVFAACFLPDSSRADYHYVMENLFLYVISTLELMVAEDYMIVYLNGA 2950
2951 TPRRKMPGLGWMKKCYQMIDRRLRKNLKSFIIVHPSWFIRTILAVTRPFI 3000
3001 SSKFSSKIKYVTSLSELSGLIPMDCIHIPESIIKYDEEKSFKRSVRLDEE 3050
3051 LREASEAAKTSCLYNDPEMSSMEKDIDMKLKEKP 3084
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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