 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P56729 from www.uniprot.org...
The NucPred score for your sequence is 0.51 (see score help below)
1 MARKKFSGLEISLIVLFAIVTIIAIALVVVLATKTPAVEEISDSTSTPAT 50
51 TRTTTPYPGSGKCPSELNDPINERINCIPDQFPTQTLCATRGCCWKPWND 100
101 SIIPWCFFVDNHGYNVEGTTSTNTGLEAKLNRIPSPTLFGDDINSVLLTT 150
151 QSQTPNRFRFKITDPNNRRYEVPHQFVKEFSGTAASDTLYDVQVIENPFS 200
201 IKIIRKSNNRILFDTSIGPLVYSDQYLQLSARLSSEYIYGIGEHIHKRFR 250
251 HDLYWKTWPIFTRDQLPGDNNNNLYGHQTFFMCIEDTSGKSFGVFLMNSN 300
301 AMEIFIQPTPIVTYRIIGGILDFYIFLGDTPEQVVQQYQELIGRPAMPAY 350
351 WSLGFQLSRWDYKSLDVVKEVVQRNREAGIPFDTQVTDIDYMEDKKDFTY 400
401 DHVAFNGLPEFVQDLHDHGQKYVIILDPAISIQKLASGAAYETYDRGTAQ 450
451 NVWINESDGTTAIIGEVWPGLTVYPDFTSPNCIEWWANECSIFHQVVNYD 500
501 GLWIDMNEVSSFVHGSEKGCSDNKLNYPPFTPGILDKLLYAKTVCMDSVQ 550
551 YWGKQYDVHSLYGYSMAIATEKAVEKVFRNKRSFILTRSTFAGSGSHAAH 600
601 WLGDNTASWEQMEWSITGMLEFSLFGIPLVGADICGFVVETTEELCRRWM 650
651 QLGAFYPFSRNHNAQGYEHQDPAFFGQNSLLVNSSKHYLNIRYTLLPFLY 700
701 TLFYKAHMFGETVVRPILHEFYEDTNSWIEDTQFLWGPSLLITPVLKQGA 750
751 STVSAYIPNATWYDYETGAKRPWRKQRVDMYLPADKIGLHLRGGYIIPIQ 800
801 QPAVTTTASRKNPLGLIVALDEYNTAEGDFFWDDGETKDTIQNGSYIFYT 850
851 FSVSNNKLDIMCTHSSYQEGTTLAFETIKILGLIDAVTEVSVVEGDQPMS 900
901 AHYNFTYDASNQYLLIYNLKFNLGRNFTVQWDQTFLDAEKFTCYPDTDTI 950
951 TKELCEERGCLWQESHLESKAPECFFPKEDNSYLVQSTQYSSLGVTADLQ 1000
1001 LNTASTRIKLPSDPIPTLRVEVKYHKDYMLQFKIYDPQNKRYEVPVPLNI 1050
1051 PTTPTSTYENRLYDVEIKENPFGIQIRRRSTGKVIWDSRLPGFAFNNQFI 1100
1101 QISTRLPSEYIYGFGEVEHTAFKRDLNWNTWGMFTRDQPPGYKLNSYGFH 1150
1151 PYYMALEDESHAHGVLLLNSNAMDVTFQPTPALTYRIIGGILDFYMFLGP 1200
1201 SPEVATKQYHEVIGRPVMPPYWALGFQLCRYGYRNTSQVEEVYNDMVTAQ 1250
1251 IPYDVQYTDIDYMERQLDFTIGEEFRELPKFVDKIRNEGMRYIIILDPAI 1300
1301 SGNETEPYPAFDRGQEKDVFVKWPNTSDICWAKVWPDLPNVTIDESLTED 1350
1351 EAVNASRAHVAFPDFFRNSTAEWWAREIIDFYNNQMKFDGLWIDMNEPSS 1400
1401 FVNGTTTNQCRNTELNYPPYFPELTKRTEGLHFRTMCMETEQILSDGSSV 1450
1451 LHYDVHNLYGWSQVKPTYDALQKATGKRGIVISRSTYPTAGRWAGHWLGD 1500
1501 NYAKWDNLDKSIIGMMEFSLFGISYTGADICGFFNNSEYQLCARWMQLGA 1550
1551 FYPYSRNHNIAFTRRQDPASWNETFSEMSRNILNIRYTLLPYFYTQMHEI 1600
1601 HAYGGTVIRPLLHEFFNEKPTWDIFKQFLWGPAFLVTPVLEPFADTVQGY 1650
1651 VPNARWFDYHTGKDIGIRETFHIFSAPLYEINLHVRGGHILPCQEPAQNT 1700
1701 FFSRQNFMKLIVAADDNQTAQGNLFWDDGETIDTYERDLYFLVQFNFNKS 1750
1751 ILTSTILKNSYINRNEMRLGYVLIWGKEKTSVNEVNLIYNGNKETVPFVE 1800
1801 DLNQEILNIDLRGLNVTLDEPIEISWS 1827
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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