 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O18866 from www.uniprot.org...
The NucPred score for your sequence is 0.52 (see score help below)
1 MSSNIHANHLSLDASSSSSSSSSSSSSSSSSSSSVHEPKMDALIIPVTME 50
51 VPCDSRGQRMWWAFLASSMVTFFGGLFIILLWRTLKYLWTVCCHCGGKTK 100
101 EAQKINNGASQADGTLKPVDEKEEVVAAEVGWMTSVKDWAGVMISAQTLT 150
151 GRVLVVLVFALSIGALVIYFIDSSNPIESCQNFYKDFTLQIDMAFNVFFL 200
201 LYFGLRFIAANDKLWFWLEVNSVVDFFTVPPVFVSVYLNRSWLGLRFLRA 250
251 LRLIQFSEILQFLNILKTSNSIKLVNLLSIFISTWLTAAGFIHLVENSGD 300
301 PWENFQNNQALTYWECVYLLMVTMSTVGYGDVYAKTTLGRLFMVFFILGG 350
351 LAMFASYVPEIIELIGNRKKYGGSYSAVSGRKHIVVCGHITLESVSNFLK 400
401 DFLHKDRDDVNVEIVFLHNISPNLELEALFKRHFTQVEFYQGSVLNPHDL 450
451 ARVKIESADACLILANKYCADPDAEDASNIMRVISIKNYHPKIRIITQML 500
501 QYHNKAHLLNIPSWNWKEGDDAICLAELKLGFIAQSCLAQGLSTMLANLF 550
551 SMRSFIKIEEDTWQKYYLEGVSNEMYTEYLSSAFVGLSFPTVCELCFVKL 600
601 KLLMIAIEYKSANRESRILINPGNHLKIQEGTLGFFIASDAKEVKRAFFY 650
651 CKACHDDITDPKRIKKCGCKRLEDEQPSTLSPKKKQRNGGMRNSPSSSPK 700
701 LMRHDPLLIPGNDQIDNMDSNVKKYDSTGMFHWCAPKEIEKVILTRSEAA 750
751 MTVLSGHVVVCIFGDVSSALIGLRNLVMPLRASNFHYHELKHIVFVGSIE 800
801 YLKREWETLHNFPKVSILPGTPLSRADLRAVNINLCDMCVILSANQNNID 850
851 DTSLQDKECILASLNIKSMQFDDSIGVLQANSQGFTPPGMDRSSPDNSPV 900
901 HGMLRQPSITTGVNIPIITELVNDTNVQFLDQDDDDDPDTELYLTQPFAC 950
951 GTAFAVSVLDSLMSATYFNDNILTLIRTLVTGGATPELEALIAEENALRG 1000
1001 GYSTPQTLANRDRCRVAQLALLDGPFADLGDGGCYGDLFCKALKTYNMLC 1050
1051 FGIYRLRDAHLSTPSQCTKRYVITNPPYEFELVPTDLIFCLMQFDHNAGQ 1100
1101 SRASLSHSSHSSQSSSKKSSSVHSIPSTANRQNRPKSRESRDKQKYVQEE 1150
1151 RL 1152
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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