 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P58751 from www.uniprot.org...
The NucPred score for your sequence is 0.57 (see score help below)
1 MERGCWAPRTLVLAVLLLLLATLRARAATGYYPRFSPFFFLCTHHGELEG 50
51 DGEQGEVLISLHIAGNPTYYVPGQEYHVTISTSTFFDGLLVTGLYTSTSI 100
101 QSSQSIGGSSAFGFGIMSDHQFGNQFMCSVVASHVSHLPTTNLSFVWIAP 150
151 PAGTGCVNFMATATHRGQVIFKDALAQQLCEQGAPTEATAYSHLAEIHSD 200
201 SVILRDDFDSYHQLELNPNIWAECSNCDTGEQCGTIMHGNAVTFCEPYGP 250
251 RELTTTYLNTTTASVLQFSIGSGSCRFSYSDPSIIVSYAKNNTADWIQLE 300
301 KIRAPSNVSTIIHILYLPEDAKGENVQFQWKQDSLHVGEVYEACWALDNI 350
351 LVINSAHRQVILEDSLDPVDTGNWLFFPGATVKHSCQSDGNAIYFHGNEG 400
401 SQLNFATTRDVDLSTEDIQEQWSEEFESQPTGWDILGAVVGSECGTIESG 450
451 LSLVFLKDGERKLCTPYMDTTGYGNLRFYFAMGGTCDPGDSHENDVILYA 500
501 KIEGKKEHIALDTLSYSSYKVPTLVSVVINPELQTPATKFCLRQKNHQGH 550
551 NQNVWAVDFFHVLPILPSTMSHMIQFSINLGCGTHQPGNSVSLEFSTNHG 600
601 RSWSLLHTECLPEICAGPHLPHSTIYSSENYSGWNRVTIPLPNAALTRDT 650
651 RIRWRQTGPILGNMWAIDNVYIGPSCLKFCSGRGQCTRHGCKCDPGFSGP 700
701 ACEMASQTFPMFISESFGSSRLSSYHNFYSIRGAEVSFGCGVLASGKALV 750
751 FNKDGRRQLITSFLDSSQSRFLQFTLRLGSKSVLSTCRAPDQPGEGVLLH 800
801 YSYDNGITWKLLEHYSYLNYHEPRIISVELPDDAKQFGIQFRWWQPYHSS 850
851 QGEDVWAIDEILMTSVLFNSISLDFTNLVEVTQSLGFYLGNIQPYCGHDW 900
901 TLCFTGDSKLASSMRYVETQSMQIGASYMIQFSLVMGCGQKYTPHMDNQV 950
951 KLEYSTNHGLTWHLVQDECLPSMPSCQEFTSASIYHASEFTQWRRVTVIL 1000
1001 PQKTWSGATRFRWSQSYYTAQDEWALDDIYIGQQCPNMCSGHGSCDHGVC 1050
1051 RCDQGYQGTECHPEAALPSTIMSDFENPSSWDSDWQEVIGGEVVKPEQGC 1100
1101 GVVSSGSSLYFSKAGKRQLVSWDLDTSWVDFVQFYIQIGGESAACNKPDS 1150
1151 REEGVLLQYSNNGGIQWHLLAEMYFSDFGKPRFVYLELPAAAKTPCTRFR 1200
1201 WWQPVFSGEDYDQWAVDDIIILSEKQKQVIPVVNPTLPQNFYEKPAFDYP 1250
1251 INQMSVWLMLANEGMAKNDSFCATTPSAMVFGKSDGDRFAVTRDLTLKPG 1300
1301 YVLQFKLNIGCASQFSSTAPVLLQYSHDAGMSWFLVKEGCFPASAGKGCE 1350
1351 GNSRELSEPTVYYTGDFEEWTRVTIAIPRSLASSKTRFRWIQESSSQKNV 1400
1401 PPFGLDGVYISEPCPSYCSGHGDCISGVCFCDLGYTAAQGTCVSNIPNHS 1450
1451 EMFDRFEGKLSPLWYKISGGQVGTGCGTLSDGRSLYFNGLGKREARTVPL 1500
1501 DTRNIRLVQFYIQIGSKTSGITCIKPRARNEGLVVQYSNDNGILWHLLRE 1550
1551 LDFLSFLEPQIISIDLPREAKTPATAFRWWQPQHGKHSAQWALDDVLIGV 1600
1601 NDSSQTGFQDKFDGSIDLQANWYRIQGGQVDIDCLSMDTALIFTENIGKP 1650
1651 RYAETWDFHVSASSFLQFDMSMGCSKPFSATHSVQLQYSLNNGKDWHPVT 1700
1701 EECVPPTIGCVHYTESSTYTSERFQNWRRVTVYLPLATNSPRTRFRWIQA 1750
1751 NYTMGADAWAIDNVLLASGCPWLCSGRGICDSGRCVCDRGFGGPFCVPVV 1800
1801 PLPSILKDDFNGNLHPDLWPEVYGAERGNLNGETIKSGTSLIFKGEGLRM 1850
1851 LISRDLDCTNTMYVQFSLRFIAKGTPERSHSILLQSSINGGVTWRLMDEF 1900
1901 YFPQTTSILFINVPLPYSAQTNATRFRLWQPYNNGKKEEIWIIDDFIIDG 1950
1951 DNLNNPVMLLDTFDFGPREDNWFFYPGGNIGLYCPYSSKGAPEEDSAMVF 2000
2001 VSNEIGEHSITTRDLSVNENTIIQFEINVGCSTDSSSADPVRLEFSRDFG 2050
2051 ATWHLLLPLCYHSSSLVSSLCSTEHHPSSTYYAGTTQGWRREVVHFGKLH 2100
2101 LCGSVRFRWYQGFYPAGSQPVTWAIDNVYIGPQCEEMCCGHGSCVNGTKC 2150
2151 ICDPGYSGPTCKISTKNPDFLKDDFEGQLESDRFLLMSGGKPSRKCGILS 2200
2201 SGNNLFFNEDGLRMLVTRDLDLSHARFVQFFMRLGCGKGVPDPRSQPVLL 2250
2251 QYSLNGGLSWSLLQEFLFSNSSNVGRYIALEMPLKARSGSTRLRWWQPSE 2300
2301 NGHFYSPWVIDQILIGGNISGNTVLEDDFSTLDSRKWLLHPGGTKMPVCG 2350
2351 STGDALVFIEKASTRYVVTTDIAVNEDSFLQIDFAASCSVTDSCYAIELE 2400
2401 YSVDLGLSWHPLVRDCLPTNVECSRYHLQRILVSDTFNKWTRITLPLPAY 2450
2451 TRSQATRFRWHQPAPFDKQQTWAIDNVYIGDGCLDMCSGHGRCIQGSCVC 2500
2501 DEQWGGLYCDEPETSLPTQLKDNFNRAPSNQNWLTVNGGKLSTVCGAVAS 2550
2551 GLALHFSGGCSRLLVTVDLNLTNAEFIQFYFMYGCLITPSNRNQGVLLEY 2600
2601 SVNGGITWTLLMEIFYDQYSKPGFVNILLPPDAKEIGTRFRWWQPRHDGL 2650
2651 DQNDWAIDNVLISGSADQRTVMLDTFSSAPVPQHERSPADAGPVGRIAFD 2700
2701 MFMEDKTSVNENWVFHDDCTVERFCDSPDGVMLCGSHDGREVYAVTHDLT 2750
2751 PTENWIMQFKISVGCKVPEKIAQNQIHVQFSTDFGVSWSYLVPQCLPADP 2800
2801 KCSGTVSQPSVFFPTKGWKRITYPLPESLMGNPVRFRFYQKYSDVQWAID 2850
2851 NFYLGPGCLDNCGGHGDCLKEQCICDPGYSGPHCYLTHTLKTFLKERFDS 2900
2901 EEIKPDLWMSLEGGSTCTECGILAENTALYFGGSTVRQAITQDLDLRGAK 2950
2951 FLQYWGRIGSENNMTSCHRPVCRKEGVLLDYSKDGGITWTLLHEMDFQKY 3000
3001 ISVRHDYILLPEGALTNTTRLRWWQPFVISNGLVVSGVERAQWALDNILI 3050
3051 GGAEINPSQLVDTFDDEGSSHEENWSFYPNAVRTAGFCGNPSFHLYWPNK 3100
3101 KKDKTHNALSSRELIIQPGYMMQFKIVVGCEATSCGDLHSVMLEYTKDAR 3150
3151 SDSWQLVQTQCLPSSSNSIGCSPFQFHEATIYNAVNSSSWKRITIQLPDH 3200
3201 VSSSATQFRWIQKGEETEKQSWAIDHVYIGEACPRLCSGHGYCTTGAVCI 3250
3251 CDESFQGDDCSVFSHELPSYIKDNFESARVTEANWETIQGGAIGSGCGQL 3300
3301 APYAHGDSLYFNGCQIRQAATKPLDLTRASKIMFVLQIGSTAQTDSCNSD 3350
3351 LSGPHTVDKAVLLQYSVNNGITWHVIAQHQPKDFTQAQRVSYNVPLEARM 3400
3401 KGVLLRWWQPRHNGTGHDQWALDHVEVVLVSTRKQNYMMNFSRQHGLRHF 3450
3451 YNRRRRSLRRYP 3462
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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