 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10287 from www.uniprot.org...
The NucPred score for your sequence is 0.44 (see score help below)
1 MDQYWREQEGRGLFEDDANSYVSDDDTMSSLTRMIYDKNSSRDALHSDYD 50
51 SSSFNVDSSSVAYPAWNQAGEEAPVTMEGVQEILLDLTNKLGFQKDNMRN 100
101 IFDYVMVLLDSRASRMSPSSALLTIHADVIGGEHANFSKWYFASHFNDGH 150
151 AIGFHDMSSPIVETMTLKEAEQAWRDQMAAFSPHRMMVQVCLYFLCWGEA 200
201 NNVRFVPECLCFIFECAYDYYISSEAKDVDAALPKEFYLDSVITPIYRFI 250
251 HAQLFEILDGKYVRRERDHSQIIGYDDINQLFWSYKGLQEIMCADKTPLL 300
301 DLPPFMRYRHLSDVEWKSCFYKSYYEYRSWFHNVTNFSRIWVMHISAYWY 350
351 YSAYNSPNLYTKNYHIRLNNKPPASCRWTACGLAGAIASFITLAAVVFEY 400
401 IHVPRRYHSARRLWPSMLLLISTLLLNIAPVVFIFASSTKEQHYASRLVV 450
451 GIVHFFFSLVCVVYYSITPLRNLVGFTTKRSGKNLANRFFTANFTPTSKT 500
501 GAFVSWCLWITVLVAKFLESYFFLTLNLADSIRFLGAMRPYDCRDYILGA 550
551 GLCKAQPKILLSLLYLTDLSLFFLDTYLWYILISTIYSLAYAFCLGISVW 600
601 TPWRELFYRVPRRIYTKLLYTDDMEIVFKPKVLISQVWNAIIISMYREHL 650
651 ISRTQIQELLYHQVPSEKAGYHTLRAPNFFYSQQVKHYKQDLFPANSEAA 700
701 RRISFFAQSLAESIPKTSSIDAMPTFTVLVPHYSEKILLSLREIIREEDQ 750
751 LSRVTLLEYLKQLYPVEWRNFVDDTKLLADENDSVIGSIDNEKNGVNKAY 800
801 DLPFYCVGFKSATPEYTLRTRIWASLRTQTLYRTINGFSNYSRAIKLLYR 850
851 TETPELVEWTNGDPVRLDEELDLMANRKFRFCVSMQRYAKFTKEEAENAE 900
901 FLLRAYPDLQIAYMDEDPQSRHNDERHLYSVLIDGHCPIMENGKRRPKYR 950
951 IRLSGNPILGDGKSDNQNMSIPYIRGEYVQMIDANQDNYLEECLKIRSIL 1000
1001 AEFEQLTPPLHSPYSVNAKAADNHPVAILGAREYIFSENTGMLGDVAAGK 1050
1051 EQTFGTLFARILSLIGGKLHYGHPDFINVLFMITRGGVSKAQKGLHVNED 1100
1101 IYAGMIALQRGGRIKHCDYYQCGKGRDLGFGSILNFTTKIGTGMAEQMLS 1150
1151 REYFNLGTQLPFDRFLSFFYAHAGFHVNNMVIMFSLQLLMLVIINLGAMY 1200
1201 TVVPVCRYRQFDSLTASLYPEGCYQLKPVLEWLKRCILSIFIVFGIAFVP 1250
1251 LAVCELGERGAIRMVIRLAKQIFSLSPIFEIFTCQIYAQSLIANLTFGGA 1300
1301 RYIGTSRGFATVRVPFSLLYSRFSGPSLYFGSRLMYMLLFGSITAWLPHY 1350
1351 IYFWITLTALCISPFLYNPHQFAWTDFFVDYREFMRWLFRENSRNQANSW 1400
1401 IGNCQLCRTRVTGYKRKIYGKKADKIAMDSPRARITTMFYGEILGPLGTL 1450
1451 FFTCIPFLFINSQPGNDDETQSTNAFIRLIIMSVAPLVLSAIIAFFFFCL 1500
1501 GIMLRPILGDRSKTYGVYLAGVAHFLFVCVDVVVFEVLGYLEGWSFSKTL 1550
1551 LGFVAIISIHRFAHKFFIICFLSREFRHDGANLAWWSGRWNGQGFGYMVL 1600
1601 TQPWREFVCKTTELNMFAGDFLLSHLLLFLQAPVILIPYIDKLHSIILFW 1650
1651 LVPSRQIRPPIYTIRQNKLRRQIVLRYATLYFSLFIAFFVLLILPFVFGK 1700
1701 SAAGTSMDKFNLIQPATKIVYSSTKNSSV 1729
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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