 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P52873 from www.uniprot.org...
The NucPred score for your sequence is 0.16 (see score help below)
1 MLKFQTVRGGLRLLGVRRSSTAPVASPNVRRLEYKPIKKVMVANRGEIAI 50
51 RVFRACTELGIRTVAVYSEQDTGQMHRQKADEAYLIGRGLAPVQAYLHIP 100
101 DIIKVAKENGVDAVHPGYGFLSERADFAQACQDAGVRFIGPSPEVVRKMG 150
151 DKVEARAIAIAAGVPVVPGTNSPINSLHEAHEFSNTYGFPIIFKAAYGGG 200
201 GRGMRVVHSYEELEENYTRAYSEALAAFGNGALFVEKFIEKPRHIEVQIL 250
251 GDQYGNILHLYERDCSIQRRHQKVVEIAPATHLDPQLRSRLTSDSVKLAK 300
301 QVGYENAGTVEFLVDKHGKHYFIEVNSRLQVEHTVTEEITDVDLVHAQIH 350
351 VSEGRSLPDLGLRQENIRINGCAIQCRVTTEDPARSFQPDTGRIEVFRSG 400
401 EGMGIRLDNASAFQGAVISPHYDSLLVKVIAHGKDHPTAATKMSRALAEF 450
451 RVRGVKTNIPFLQNVLNNQQFLAGIVDTQFIDENPELFQLRPAQNRAQKL 500
501 LHYLGHVMVNGPTTPIPVKVSPSPVDPIVPVVPIGPPPAGFRDILLREGP 550
551 EGFARAVRNHQGLLLMDTTFRDAHQSLLATRVRTHDLKKIAPYVAHNFNN 600
601 LFSIENWGGATFDVAMRFLYECPWRRLQELRELIPNIPFQMLLRGANAVG 650
651 YTNYPDNVVFKFCEVAKENGMDVFRIFDSLNYLPNMLLGMEAAGSAGGVV 700
701 EAAISYTGDVADPSRTKYSLEYYMGLAEELVRAGTHILCIKDMAGLLKPA 750
751 ACTMLVSSLRDRFPDLPLHIHTHDTSGSGVAAMLACAQAGADVVDVAVDS 800
801 MSGMTSQPSMGALVACTKGTPLDTEVPLERVFDYSEYWEGARGLYAAFDC 850
851 TATMKSGNSDVYENEIPGGQYTNLHFQAHSMGLGSKFKEVKKAYVEANQM 900
901 LGDLIKVTPSSKIVGDLAQFMVQNGLSRAEAEAQAEELSFPRSVVEFLQG 950
951 YIGIPHGGFPEPFRSKVLKDLPRIEGRPGASLPPLNLKELEKDLIDRHGE 1000
1001 EVTPEDVLSAAMYPDVFAQFKDFTATFGPLDSLNTRLFLQGPKIAEEFEV 1050
1051 ELERGKTLHIKALAVSDLNRAGQRQVFFELNGQLRSILVKDTQAMKEMHF 1100
1101 HPKALKDVKGQIGAPMPGKVIDVKVAAGAKVVKGQPLCVLSAMKMETVVT 1150
1151 SPMEGTIRKVHVTKDMTLEGDDLILEIE 1178
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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