 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q60841 from www.uniprot.org...
The NucPred score for your sequence is 0.59 (see score help below)
1 MERGCWAPRALVLAVLLLLATLRARAATGYYPRFSPFFFLCTHHGELEGD 50
51 GEQGEVLISLHIAGNPTYYVPGQEYHVTISTSTFFDGLLVTGLYTSTSIQ 100
101 SSQSIGGSSAFGFGIMSDHQFGNQFMCSVVASHVSHLPTTNLSFVWIAPP 150
151 AGTGCVNFMATATHRGQVIFKDALAQQLCEQGAPTEATAYSHLAEIHSDS 200
201 VILRDDFDSYQQLELNPNIWVECSNCEMGEQCGTIMHGNAVTFCEPYGPR 250
251 ELTTTCLNTTTASVLQFSIGSGSCRFSYSDPSITVSYAKNNTADWIQLEK 300
301 IRAPSNVSTVIHILYLPEEAKGESVQFQWKQDSLRVGEVYEACWALDNIL 350
351 VINSAHREVVLEDNLDPVDTGNWLFFPGATVKHSCQSDGNSIYFHGNEGS 400
401 EFNFATTRDVDLSTEDIQEQWSEEFESQPTGWDILGAVVGADCGTVESGL 450
451 SLVFLKDGERKLCTPYMDTTGYGNLRFYFVMGGICDPGVSHENDIILYAK 500
501 IEGRKEHIALDTLTYSSYKVPSLVSVVINPELQTPATKFCLRQKSHQGYN 550
551 RNVWAVDFFHVLPVLPSTMSHMIQFSINLGCGTHQPGNSVSLEFSTNHGR 600
601 SWSLLHTECLPEICAGPHLPHSTVYSSENYSGWNRITIPLPNAALTRDTR 650
651 IRWRQTGPILGNMWAIDNVYIGPSCLKFCSGRGQCTRHGCKCDPGFSGPA 700
701 CEMASQTFPMFISESFGSARLSSYHNFYSIRGAEVSFGCGVLASGKALVF 750
751 NKDGRRQLITSFLDSSQSRFLQFTLRLGSKSVLSTCRAPDQPGEGVLLHY 800
801 SYDNGITWKLLEHYSYVNYHEPRIISVELPDDARQFGIQFRWWQPYHSSQ 850
851 GEDVWAIDEIVMTSVLFNSISLDFTNLVEVTQSLGFYLGNVQPYCGHDWT 900
901 LCFTGDSKLASSMRYVETQSMQIGASYMIQFSLVMGCGQKYTPHMDNQVK 950
951 LEYSANHGLTWHLVQEECLPSMPSCQEFTSASIYHASEFTQWRRVTVVLP 1000
1001 QKTWSGATRFRWSQSYYTAQDEWALDNIYIGQQCPNMCSGHGSCDHGVCR 1050
1051 CDQGYQGTECHPEAALPSTIMSDFENPSSWESDWQEVIGGEVVKPEQGCG 1100
1101 VVSSGSSLYFSKAGKRQLVSWDLDTSWVDFVQFYIQIGGESAACNKPDSR 1150
1151 EEGILLQYSNNGGIQWHLLAEMYFSDFSKPRFVYLELPAAAKTPCTRFRW 1200
1201 WQPVFSGEDYDQWAVDDIIILSEKQKQVIPVVNPTLPQNFYEKPAFDYPM 1250
1251 NQMSVWLMLANEGMAKNDSFCATTPSAMVFGKSDGDRFAVTRDLTLKPGY 1300
1301 VLQFKLNIGCTSQFSSTAPVLLQYSHDAGMSWFLVKEGCFPASAGKGCEG 1350
1351 NSRELSEPTVYYTGDFEEWTRITIAIPRSLASSKTRFRWIQESSSQKNVP 1400
1401 PFGLDGVYISEPCPSYCSGHGDCISGVCFCDLGYTAAQGTCVSNTPNHSE 1450
1451 MFDRFEGKLSPLWYKITGGQVGTGCGTLNDGRSLYFNGLGKREARTVPLD 1500
1501 TRNIRLVQFYIQIGSKTSGITCIKPRARNEGLVVQYSNDNGILWHLLREL 1550
1551 DFMSFLEPQIISIDLPREAKTPATAFRWWQPQHGKHSAQWALDDVLIGVN 1600
1601 DSSQTGFQDKFDGSIDLQANWYRIQGGQVDIDCLSMDTALIFTENIGKPR 1650
1651 YAETWDFHVSASSFLQFEMNMGCSKPFSGAHGIQLQYSLNNGKDWQLVTE 1700
1701 ECVPPTIGCVHYTESSTYTSERFQNWRRVTVYLPLATNSPRTRFRWIQTN 1750
1751 YTVGADSWAIDNVILASGCPWMCSGRGICDSGRCVCDRGFGGPFCVPVVP 1800
1801 LPSILKDDFNGNLHPDLWPEVYGAERGNLNGETIKSGTCLIFKGEGLRML 1850
1851 ISRDLDCTNTMYVQFSLRFIAKGTPERSHSILLQFSVSGGVTWHLMDEFY 1900
1901 FPQTTSILFINVPLPYGAQTNATRFRLWQPYNNGKKEEIWIIDDFIIDGN 1950
1951 NLNNPVLLLDTFDFGPREDNWFFYPGGNIGLYCPYSSKGAPEEDSAMVFV 2000
2001 SNEVGEHSITTRDLSVNENTIIQFEINVGCSTDSSSADPVRLEFSRDFGA 2050
2051 TWHLLLPLCYHSSSLVSSLCSTEHHPSSTYYAGTTQGWRREVVHFGKLHL 2100
2101 CGSVRFRWYQGFYPAGSQPVTWAIDNVYIGPQCEEMCYGHGSCINGTKCI 2150
2151 CDPGYSGPTCKISTKNPDFLKDDFEGQLESDRFLLMSGGKPSRKCGILSS 2200
2201 GNNLFFNEDGLRMLVTRDLDLSHARFVQFFMRLGCGKGVPDPRSQPVLLQ 2250
2251 YSLNGGLSWSLLQEFLFSNSSNVGRYIALEMPLKARSGSTRLRWWQPSEN 2300
2301 GHFYSPWVIDQILIGGNISGNTVLEDDFSTLDSRKWLLHPGGTKMPVCGS 2350
2351 TGDALVFIEKASTRYVVTTDIAVNEDSFLQIDFAASCSVTDSCYAIELEY 2400
2401 SVDLGLSWHPLVRDCLPTNVECSRYHLQRILVSDTFNKWTRITLPLPSYT 2450
2451 RSQATRFRWHQPAPFDKQQTWAIDNVYIGDGCLDMCSGHGRCVQGSCVCD 2500
2501 EQWGGLYCDEPETSLPTQLKDNFNRAPSNQNWLTVSGGKLSTVCGAVASG 2550
2551 LALHFSGGCSRLLVTVDLNLTNAEFIQFYFMYGCLITPSNRNQGVLLEYS 2600
2601 VNGGITWNLLMEIFYDQYSKPGFVNILLPPDAKEIATRFRWWQPRHDGLD 2650
2651 QNDWAIDNVLISGSADQRTVMLDTFSSAPVPQHERSPADAGPVGRIAFEM 2700
2701 FLEDKTSVNENWLFHDDCTVERFCDSPDGVMLCGSHDGREVYAVTHDLTP 2750
2751 TENWIMQFKISVGCKVPEKIAQNQIHVQFSTDFGVSWSYLVPQCLPADPK 2800
2801 CSGSVSQPSVFFPTEGWKRITYPLPESLTGNPVRFRFYQKYSDVQWAIDN 2850
2851 FYLGPGCLDNCGGHGDCLKEQCICDPGYSGPNCYLTHSLKTFLKERFDSE 2900
2901 EIKPDLWMSLEGGSTCTECGVLAENTALYFGGSTVRQAITQDLDLRGAKF 2950
2951 LQYWGRIGSENNMTSCHRPVCRKEGVLLDFSTDGGITWTLLHEMDFQKYI 3000
3001 SVRHDYILLPEGALTNTTRLRWWQPFVISNGLVVSGVERAQWALDNILIG 3050
3051 GAEINPSQLVDTFDDEGSSHEENWSFYPNAVRTAGFCGNPSFHLYWPNKK 3100
3101 KDKTHNALSSRELIIQPGYMMQFKIVVGCEATSCGDLHSVMLEYTKDARS 3150
3151 DSWQLVQTQCLPSSSNSIGCSPFQFHEATIYNAVNSSSWKRITIQLPDHV 3200
3201 SSSATQFRWIQKGEETEKQSWAIDHVYIGEACPKLCSGHGYCTTGAVCIC 3250
3251 DESFQGDDCSVFSHELPSYIKDNFESARVTEANWETIQGGVIGSGCGQLA 3300
3301 PYAHGDSLYFNGCQIRQAATKPLDLTRASKIMFVLQIGSPAQTDSCNSDL 3350
3351 SGPHTVDKAVLLQYSVNNGITWHVIAQHQPKDFTQAQRVSYNVPLEARMK 3400
3401 GVLLRWWQPRHNGTGHDQWALDHVEVVLVSTRKQNYMMNFSRQHGLRHFY 3450
3451 NRRRRSLRRYP 3461
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.) |
Go back to the NucPred Home Page.