 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q864S8 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MPIGCKERPTFFEIFRTRCNKADLGPISLNWFEELCLEAPPYNSEPTEES 50
51 GYKISYEPNLFKTPQRKPCHQLASTPIIFKEQGLIPPIYQQSPLKELGKD 100
101 ITNSKHRSCCTMKSKMDQTNDVTSPPLNSCLSESPLLRSTHVTPQREKSV 150
151 VCGSLFHTPKLTKGQTPKRISESLGAEVDPDMSWSSSLATPPTLSSTVLI 200
201 VRDEEASAAVFPNDTTAIFKSYFCNHDESLKKNDRFIPSGPDSENKSQRE 250
251 AKSQGLGKMVGNSCDKVNSCKDPFGNSTLNVLEDGVRERVADVSEEDSFP 300
301 LCVPKCKTRNLQKIKTSKTRKNIFNETTDECKEAKKQMKENKHSFVSEME 350
351 ANASDPLDSNVTNQKPFGNGSDKISKEVVLSSASESCHLTLSGLNGTHME 400
401 KLPLLCISSCDQNNSEKDLITTEKECTNFIILEDSLPQISGVPKCTEKIL 450
451 NEEIVVNKIDEGQCLESHEDSILAVKQAVFETSLIASPLQGIRKSIFRIR 500
501 ESPEETFSAVFSNNITDPNFKEEHEASESVLEKHSICSQKEDSLSTSSLD 550
551 NGSWPATIKHTSVALKNSGLISTLKKKTKKFIYVVNDETSYQGLKTQKDQ 600
601 QSGLMNYSAQFEANVLEGPLTFANADSGLLHSSVKKTCLQNDSKEPILSL 650
651 TNSFGTLLRKVSNKGSSSPNNKIISQDLDYKEAKIKKEKLQSFISTETNC 700
701 LSSLQEKHCEDDTKSQRVADRKEEILPAVSQPSVPYSEVEDSGIHFQTLK 750
751 SFSSDPDKSSQLTPHPRDPPSNPVGLSRGRESYEVSETLKCKNHEAGFEL 800
801 TKTMENSQEIHVLNEHAKKAKLLSTEKYVTEASPSMKVPFNQNAHLTIIQ 850
851 KDQKETTLISKITMNPNSEELFPDGDNFVFKITKERNVPVLGSIKELQDS 900
901 DLCCVKEPVLENSTMVVYTDMDDKQAAKVSITKGFDSSNIDDLTEKDRNS 950
951 IKQQLRMTLDQDSKSDITLDSDMKSNGNNDYMDNWARLSDPILNHNFGNG 1000
1001 FRTASNKEIKLSEHNIKKSKMLFKDIEERYPTNLACIEIVNTPLESQEKL 1050
1051 SKPHILDPQSINTVSGCVQSSAYVSDSENRHTTPPTLSLKRDFDSNHNLT 1100
1101 PSQKAEITELSTILEESGSQFEFTQFRKPSHLKQKNPCEMPEKHLTISNT 1150
1151 TPEEQKDGHLRLTINALSISQGDSSKKFEGIIGGKQKLACLSKTSCNKSA 1200
1201 SGHLTGKNEVEFRGFYSARGTKLNVCSEALQKAKKLFSDLENISEETSVE 1250
1251 VDRSFSSSKCNGSVSMFKKENCNNEKKLNEKNNKYRLILQNNIEMTTGIF 1300
1301 VAEDTEGYKRNIENKANKYTDASRNVYNFREADGSDSSKNDTVYIHKEEN 1350
1351 GLPYIDQHDIDLKLSSQFIKEGNTQIKEGLSDLTCLEVVKAEETLHVNTS 1400
1401 NKEHLTANTMGRITKDFDIFDVSFQTASGKNIRVSRASLNKVTNLLDQKC 1450
1451 TEEELNNFADSLNSELLSGIDINKADISHHGEMEILKKRQMKESDLTGTE 1500
1501 NKSLTLQQRPEYEIKKIKEPTILGFHTASGKRIEIAKESLDKVKNLFDEQ 1550
1551 EQDKSEMTNFSHRGTKMSKGREECEGGLRLACKTIEITPASKEEEMQKPL 1600
1601 EKNLVSNEIVVVPRLLSDNLYKQTENLKIPNRASLKVKVHENTGKETAKK 1650
1651 PTTCTNQSTYSATENSALSFYTGHGRKISVSQSSILEVKKWLRGGELDDQ 1700
1701 PEKTVYNISEYLPKSKVDNSGIEPVVRNVGERENTSVSEIMFTVREADTD 1750
1751 PQSVNEDICVQRLVTNFSCKKENTAIKVTVSDSNNFDSTQKLNSDSNDAV 1800
1801 PVYTTASSERVLVAHETKVAEGFTENCSMAIKQTTKSKPGKIVAGYRKAP 1850
1851 DDSEDTICPNSLDGAECSSPSHKDFAETQSEQTPQLNQSISGFKKRSEIP 1900
1901 PHQINLKTSDICKLSTGKRLQSISYTNACGIFSTASGKCVQVSDAALQKA 1950
1951 RQVFSKVEDSAKQPFSKVSFKHNEDHSDKFTREENTMIHTPQNLLSSAFS 2000
2001 GFSTASGKQVPVSESALCKVKGILEEFDVMRTECGPQRSPTSRQDVSKMP 2050
2051 PPSCVENKTPKHSVNSKLEKAYNKEFKLSSNSKIENGSSENHSVQVSPYP 2100
2101 SQFKQDKQLIQGNKASLVENIHLLEKEQALPKNIKWKLETEAFPNLPLKT 2150
2151 DTAIHSTDSKDPENYFETETVEIAKAFMEDGELTDADLLSHARHFLPTCQ 2200
2201 HSEETLVSNSRRGKRRGVLVSVGEPPIKRNLLNEFDRIIKNQEKSLKASK 2250
2251 STPDGIIKDRSLFMHHISLEPVTCGPFSTTKKRQEIQNPNFTAPGQKFLS 2300
2301 KSHFYEHLALEKSSSNVSISGQPFCTVPATRSEKRGHSITPSKPVKVFVP 2350
2351 PFKTKSRFLQDEQHISKNTHVEENKQKPNNIDEHSSGDSKNNINNSEIHQ 2400
2401 LNKNNSSQAATMVFTKCEKEPLDLIASLQNARDIQDMRIREKRKQHIFPQ 2450
2451 PGSLFLAKTSTVPRISLRVAVEGRVPSACSHKQLYMYGVSKHCVKINSKN 2500
2501 AESFQFHTQDYFGKEVQWAKEGIQLADGGWLIPSNDGKAGKEEFYRALCD 2550
2551 TPGVDPNLISRIWVYNHYRWIIWKLAAMEFAFPKEFANRCLSPERVLLQL 2600
2601 KYRYDMEIDRSKRSAIKKIMERDDTAAKTLVLCISETISSSTDLSETSGS 2650
2651 KTSGVGTKNVGIVELTDGWYAIKAQLDPPLLALVKKGRLTVGHKIIIHGA 2700
2701 ELAGSPDACTPLEAPESLILKISANSTRPACWYAKLGFFPDPRPFPLPLS 2750
2751 SLFSDGGNVGCVDVVIQRTYPIQWMEKTPSGLCIFRNEREEEREATKYAE 2800
2801 AQQKKLEVLFNKIQAEFEKHDENITKRCVPLRALTRQQVCALQDGAELYE 2850
2851 AVKNAPDPASLEAYFSEEQIRALNNHRQMLNDKKQAQIQLEFRKAMESAE 2900
2901 QGEQMLPRDVTTVWKMRIISYGKKEKDSVTLSIWRPSSDLYSLLTEGKRY 2950
2951 RIYHLATSQSKSKSERAHIQLTATKKTQYQQLPASDELLFQVYQPREPLY 3000
3001 FNKLLDPDFQPPCSEVDLIGFVVSVVKKIGFAPLVYLSDECHNLLAIKVW 3050
3051 TDLNEDIVKPHTLIAASNLQWRPESKSGIPTLFAGDFSRFSASPKEGHFQ 3100
3101 ETFHKMKNTIENVETFCNDAENKLVHILNANSPKVSTPMKDYASEPHTIQ 3150
3151 TVLGLGNKLSMSSPNSEMNYQSPLSLCKPKAKSVPTPGSAQMTSKSCYKG 3200
3201 ERELDDPKTCKKRKALDFLSRLPLPPPVSPICTFVSPAAQKAFQPPRSCG 3250
3251 TKYETPIKKRELNSPQMTPLKFNDTSLVESDSIADEELALINTQALLSGL 3300
3301 AGEDQLMSLNDSPRTAPTSSKDYVRPKSYPTAPGIRDCENPQASTEGGEP 3350
3351 DVQDTDTVKRSSMRLQRRQQQT 3372
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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