 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P23739 from www.uniprot.org...
The NucPred score for your sequence is 0.42 (see score help below)
1 MAKKKFSALEISLIVLFIIVTAIAIALVTVLATKVPAVEEIKSPTPTSNS 50
51 TPTSTPTSTSTPTSTSTPSPGKCPPEQGEPINERINCIPEQHPTKAICEE 100
101 RGCCWRPWNNTVIPWCFFADNHGYNAESITNENAGLKATLNRIPSPTLFG 150
151 EDIKSVILTTQTQTGNRFRFKITDPNNKRYEVPHQFVKEETGIPAADTLY 200
201 DVQVSENPFSIKVIRKSNNKVLCDTSVGPLLYSNQYLQISTRLPSEYIYG 250
251 FGGHIHKRFRHDLYWKTWPIFTRDEIPGDNNHNLYGHQTFFMGIGDTSGK 300
301 SYGVFLMNSNAMEVFIQPTPIITYRVTGGILDFYIFLGDTPEQVVQQYQE 350
351 VHWRPAMPAYWNLGFQLSRWNYGSLDTVSEVVRRNREAGIPYDAQVTDID 400
401 YMEDHKEFTYDRVKFNGLPEFAQDLHNHGKYIIILDPAISINKRANGAEY 450
451 QTYVRGNEKNVWVNESDGTTPLIGEVWPGLTVYPDFTNPQTIEWWANECN 500
501 LFHQQVEYDGLWIDMNEVSSFIQGSLNLKGVLLIVLNYPPFTPGILDKVM 550
551 YSKTLCMDAVQHWGKQYDVHSLYGYSMAIATEQAVERVFPNKRSFILTRS 600
601 TFGGSGRHANHWLGDNTASWEQMEWSITGMLEFGIFGMPLVGATSCGFLA 650
651 DTTEELCRRWMQLGAFYPFSRNHNAEGYMEQDPAYFGQDSSRHYLTIRYT 700
701 LLPFLYTLFYRAHMFGETVARPFLYEFYDDTNSWIEDTQFLWGPALLITP 750
751 VLRPGVENVSAYIPNATWYDYETGIKRPWRKERINMYLPGDKIGLHLRGG 800
801 YIIPTQEPDVTTTASRKNPLGLIVALDDNQAAKGELFWDDGESKDSIEKK 850
851 MYILYTFSVSNNELVLNCTHSSYAEGTSLAFKTIKVLGLREDVRSITVGE 900
901 NDQQMATHTNFTFDSANKILSITALNFNLAGSFIVRWCRTFSDNEKFTCY 950
951 PDVGTATEGTCTQRGCLWQPVSGLSNVPPYYFPPENNPYTLTSIQPLPTG 1000
1001 ITAELQLNPPNARIKLPSNPISTLRVGVKYHPNDMLQFKIYDAQHKRYEV 1050
1051 PVPLNIPDTPTSSNERLYDVEIKENPFGIQVRRRSSGKLIWDSRLPGFGF 1100
1101 NDQFIQISTRLPSNYLYGFGEVEHTAFKRDLNWHTWGMFTRDQPPGYKLN 1150
1151 SYGFHPYYMALENEGNAHGVLLLNSNGMDVTFQPTPALTYRTIGGILDFY 1200
1201 MFLGPTPEIATRQYHEVIGFPVMPPYWALGFQLCRYGYRNTSEIEQLYND 1250
1251 MVAANIPYDVQYTDINYMERQLDFTIGERFKTLPEFVDRIRKDGMKYIVI 1300
1301 LAPAISGNETQPYPAFERGIQKDVFVKWPNTNDICWPKVWPDLPNVTIDE 1350
1351 TITEDEAVNASRAHVAFPDFFRNSTLEWWAREIYDFYNEKMKFDGLWIDM 1400
1401 NEPSSFGIQMGGKVLNECRRMMTLNYPPVFSPELRVKEGEGASISEAMCM 1450
1451 ETEHILIDGSSVLQYDVHNLYGWSQVKPTLDALQNTTGLRGIVISRSTYP 1500
1501 TTGRWGGHWLGDNYTTWDNLEKSLIGMLELNLFGIPYIGADICGVFHDSG 1550
1551 YPSLYFVGIQVGAFYPYPRESPTINFTRSQDPVSWMKLLLQMSKKVLEIR 1600
1601 YTLLPYFYTQMHEAHAHGGTVIRPLMHEFFDDKETWEIYKQFLWGPAFMV 1650
1651 TPVVEPFRTSVTGYVPKARWFDYHTGADIKLKGILHTFSAPFDTINLHVR 1700
1701 GGYILPCQEPARNTHLSRQNYMKLIVAADDNQMAQGTLFGDDGESIDTYE 1750
1751 RGQYTSIQFNLNQTTLTSTVLANGYKNKQEMRLGSIHIWGKGTLRISNAN 1800
1801 LVYGGRKHQPPFTQEEAKETLIFDLKNMNVTLDEPIQITWS 1841
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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