 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8AYS8 from www.uniprot.org...
The NucPred score for your sequence is 0.48 (see score help below)
1 MSNNINANNLNTDSSSSPVNVPKMDALIIPVTMEVPCDSRGQRMWWAFLA 50
51 SSMVTFFGGLFIILLWRTLKYLWTVCCHCGVKNKEAQKINGGGDTQADGA 100
101 CKPTDEKEENVAAEVGWMTSVKDWAGVMISAQTLTGRVLVVLVFALSIGA 150
151 LVIYFIDSSNPIESCQNFYKDFTLQIDMAFNVFFLLYFGLRFIAANDKLW 200
201 FWLEVNSVVDFFTVPPVFVSVYLNRSWLGLRFLRALRLIQFSEILQFLNI 250
251 LKTSNSIKLVNLCSIFISTWLTAAGFIHLVENSGDPWENFQNNQQLTYWE 300
301 CVYLLMVTMSTVGYGDVYAKTTLGRLFMVFFILGGLAMFASYVPEIIELI 350
351 GNRKKYGGSYSAVSGRKHIVVCGHITLESVSNFLKDFLHKDRDDVNVEIV 400
401 FLHNISPNLELEALFKRHFTQVEFYQGSVLNPHDLARVKIESADACLILA 450
451 NKYCADPDAEDASNIMRVISIKNYHPKIRIITQMLQYHNKAHLLNIPSWN 500
501 WKEGDDAICLAELKLGFIAQSCLAPGLSTMLANLFSMRSFIKIEEDTWQK 550
551 YYLEGVANEMYTEYLSSAFVGLSFPAVCELVFAKLKLLMIAIEYKSEKRE 600
601 SSILINPGNHVKIQEGTLGFFIASDAKEVKRAFFYCKACHDDITDPKRIK 650
651 KCGCKRLEDEQPSTLSPKKKQRNGGMRNSPNSSPKLMRHDPLLIPGNEQI 700
701 DNMDANVKKYDSTGMFHWCPAKDIEKVILTRSEAAMTVLSGHVVVCIFGD 750
751 VKSALIGLRNLVMPLRASNFHYHELKHIVFVGSLEYLRREWETLHNFPKV 800
801 SILPGTPLSRADLRAVNINLCDMCVILSANQNNIDDASLQDKECILASLN 850
851 IKSMQFDDSIGVLQANSQGFTPPGMDRSSPDNSPVHGLLRQPSITTGANI 900
901 PIITELVNDSNVQFLDQDDDDDPDTELYLTQPFACGTAFAVSVLDSLMSA 950
951 TYFNDNILTLIRTLVTGGATPELEALIAEENALRGGYSTPQTLANRDRCR 1000
1001 VAQLALYDGPFADLGDGGCYGDLFCKALKTYNMLCFGIYRLRDAHLSTPS 1050
1051 QCTKRYVITNPPYEFELVPTDLIFCLMQFDHNAGQSRASLSHSSHSSYSS 1100
1101 SKKSSSVHSIPSTANRPNRTKTRDSREKQKYVQEDRL 1137
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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