 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P14410 from www.uniprot.org...
The NucPred score for your sequence is 0.51 (see score help below)
1 MARKKFSGLEISLIVLFVIVTIIAIALIVVLATKTPAVDEISDSTSTPAT 50
51 TRVTTNPSDSGKCPNVLNDPVNVRINCIPEQFPTEGICAQRGCCWRPWND 100
101 SLIPWCFFVDNHGYNVQDMTTTSIGVEAKLNRIPSPTLFGNDINSVLFTT 150
151 QNQTPNRFRFKITDPNNRRYEVPHQYVKEFTGPTVSDTLYDVKVAQNPFS 200
201 IQVIRKSNGKTLFDTSIGPLVYSDQYLQISTRLPSDYIYGIGEQVHKRFR 250
251 HDLSWKTWPIFTRDQLPGDNNNNLYGHQTFFMCIEDTSGKSFGVFLMNSN 300
301 AMEIFIQPTPIVTYRVTGGILDFYILLGDTPEQVVQQYQQLVGLPAMPAY 350
351 WNLGFQLSRWNYKSLDVVKEVVRRNREAGIPFDTQVTDIDYMEDKKDFTY 400
401 DQVAFNGLPQFVQDLHDHGQKYVIILDPAISIGRRANGTTYATYERGNTQ 450
451 HVWINESDGSTPIIGEVWPGLTVYPDFTNPNCIDWWANECSIFHQEVQYD 500
501 GLWIDMNEVSSFIQGSTKGCNVNKLNYPPFTPDILDKLMYSKTICMDAVQ 550
551 NWGKQYDVHSLYGYSMAIATEQAVQKVFPNKRSFILTRSTFAGSGRHAAH 600
601 WLGDNTASWEQMEWSITGMLEFSLFGIPLVGADICGFVAETTEELCRRWM 650
651 QLGAFYPFSRNHNSDGYEHQDPAFFGQNSLLVKSSRQYLTIRYTLLPFLY 700
701 TLFYKAHVFGETVARPVLHEFYEDTNSWIEDTEFLWGPALLITPVLKQGA 750
751 DTVSAYIPDAIWYDYESGAKRPWRKQRVDMYLPADKIGLHLRGGYIIPIQ 800
801 EPDVTTTASRKNPLGLIVALGENNTAKGDFFWDDGETKDTIQNGNYILYT 850
851 FSVSNNTLDIVCTHSSYQEGTTLAFQTVKILGLTDSVTEVRVAENNQPMN 900
901 AHSNFTYDASNQVLLIADLKLNLGRNFSVQWNQIFSENERFNCYPDADLA 950
951 TEQKCTQRGCVWRTGSSLSKAPECYFPRQDNSYSVNSARYSSMGITADLQ 1000
1001 LNTANARIKLPSDPISTLRVEVKYHKNDMLQFKIYDPQKKRYEVPVPLNI 1050
1051 PTTPISTYEDRLYDVEIKENPFGIQIRRRSSGRVIWDSWLPGFAFNDQFI 1100
1101 QISTRLPSEYIYGFGEVEHTAFKRDLNWNTWGMFTRDQPPGYKLNSYGFH 1150
1151 PYYMALEEEGNAHGVFLLNSNAMDVTFQPTPALTYRTVGGILDFYMFLGP 1200
1201 TPEVATKQYHEVIGHPVMPAYWALGFQLCRYGYANTSEVRELYDAMVAAN 1250
1251 IPYDVQYTDIDYMERQLDFTIGEAFQDLPQFVDKIRGEGMRYIIILDPAI 1300
1301 SGNETKTYPAFERGQQNDVFVKWPNTNDICWAKVWPDLPNITIDKTLTED 1350
1351 EAVNASRAHVAFPDFFRTSTAEWWAREIVDFYNEKMKFDGLWIDMNEPSS 1400
1401 FVNGTTTNQCRNDELNYPPYFPELTKRTDGLHFRTICMEAEQILSDGTSV 1450
1451 LHYDVHNLYGWSQMKPTHDALQKTTGKRGIVISRSTYPTSGRWGGHWLGD 1500
1501 NYARWDNMDKSIIGMMEFSLFGMSYTGADICGFFNNSEYHLCTRWMQLGA 1550
1551 FYPYSRNHNIANTRRQDPASWNETFAEMSRNILNIRYTLLPYFYTQMHEI 1600
1601 HANGGTVIRPLLHEFFDEKPTWDIFKQFLWGPAFMVTPVLEPYVQTVNAY 1650
1651 VPNARWFDYHTGKDIGVRGQFQTFNASYDTINLHVRGGHILPCQEPAQNT 1700
1701 FYSRQKHMKLIVAADDNQMAQGSLFWDDGESIDTYERDLYLSVQFNLNQT 1750
1751 TLTSTILKRGYINKSETRLGSLHVWGKGTTPVNAVTLTYNGNKNSLPFNE 1800
1801 DTTNMILRIDLTTHNVTLEEPIEINWS 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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