| Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q08773 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MTTQQEEQRSDTKNSKSESPSEVLVDTLDSKSNGSSDDDNIGQSEELSDK 50
51 EIYTVEDRPPEYWAQRKKKFVLDVDPKYAKQKDKSDTYKRFKYLLGVTDL 100
101 FRHFIGIKAKHDKNIQKLLKQLDSDANKLSKSHSTVSSSSRHHRKTEKEE 150
151 DAELMADEEEEIVDTYQEDIFVSESPSFVKSGKLRDYQVQGLNWLISLHE 200
201 NKLSGILADEMGLGKTLQTISFLGYLRYVKQIEGPFLIIVPKSTLDNWRR 250
251 EFLKWTPNVNVLVLHGDKDTRADIVRNIILEARFDVLITSYEMVIREKNA 300
301 LKRLAWQYIVIDEAHRIKNEQSALSQIIRLFYSKNRLLITGTPLQNNLHE 350
351 LWALLNFLLPDIFGDSELFDEWFEQNNSEQDQEIVIQQLHSVLNPFLLRR 400
401 VKADVEKSLLPKIETNVYVGMTDMQIQWYKSLLEKDIDAVNGAVGKREGK 450
451 TRLLNIVMQLRKCCNHPYLFEGAEPGPPYTTDEHLIFNSGKMIILDKLLK 500
501 RLKEKGSRVLIFSQMSRLLDILEDYCYFRDFEYCRIDGSTSHEERIEAID 550
551 EYNKPNSEKFVFLLTTRAGGLGINLVTADTVILFDSDWNPQADLQAMDRA 600
601 HRIGQKKQVHVYRFVTENAIEEKVIERAAQKLRLDQLVIQQGTGKKTASL 650
651 GNSKDDLLDMIQFGAKNMFEKKASKVTVDADIDDILKKGEQKTQELNAKY 700
701 QSLGLDDLQKFNGIENQSAYEWNGKSFQKKSNDKVVEWINPSRRERRREQ 750
751 TTYSVDDYYKEIIGGGSKSASKQTPQPKAPRAPKVIHGQDFQFFPKELDA 800
801 LQEKEQLYFKKKVNYKVTSYDITGDIRNEGSDAEEEEGEYKNAANTEGHK 850
851 GHEELKRRIEEEQEKINSAPDFTQEDELRKQELISKAFTNWNKRDFMAFI 900
901 NACAKYGRDDMENIKKSIDSKTPEEVEVYAKIFWERLKEINGWEKYLHNV 950
951 ELGEKKNEKLKFQETLLRQKIEQCKHPLHELIIQYPPNNARRTYNTLEDK 1000
1001 FLLLAVNKYGLRADKLYEKLKQEIMMSDLFTFDWFIKTRTVHELSKRVHT 1050
1051 LLTLIVREYEQPDANKKKRSRTSATREDTPLSQNESTRASTVPNLPTTMV 1100
1101 TNQKDTNDHVDKRTKIDQEA 1120
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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