 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Z1T1 from www.uniprot.org...
The NucPred score for your sequence is 0.87 (see score help below)
1 MSSNSFAYNEQSGGGEAAELGQEATSTISPSGAFGLFSSDWKKNEDLKQM 50
51 LESNKDSAKLDAMKRIVGMIAKGKNASELFPAVVKNVASKNIEIKKLVYV 100
101 YLVRYAEEQQDLALLSISTFQRALKDPNQLIRASALRVLSSIRVPIIVPV 150
151 MMLAIKEASADLSPYVRKNAAHAIQKLYSLDPEQKEMLIEVIEKLLKDKS 200
201 TLVAGSVVMAFEEVCPDRIDLIHRNYRKLCNLLVDVEEWGQVVIIHMLTR 250
251 YARTQFVSPWREDGGLEDNEKNFYESEEEEEEKEKSSRKKSYAMDPDHRL 300
301 LIRNTKPLLQSRNAAVVMAVAQLYWHISPKSEAGVISKSLVRLLRSNREV 350
351 QYIVLQNIATMSIERKGMFEPYLKSFYVRSTDPTMIKTLKLEILTNLANE 400
401 ANISTLLREFQTYVRSQDKQFAAATIQTIGRCATSISEVTDTCLNGLVCL 450
451 LSNRDEIVVAESVVVIKKLLQMQPAQHGEIIRHMAKLLDSITVPVARASI 500
501 LWLIGENCERVPKIAPDVLRKMAKSFTSEDDLVKLQILNLAAKLYLTNSK 550
551 QTKLLTQYILNLGKYDQNYDIRDRTRFIRQLIVPNEKSGALSKYAKKIFL 600
601 APKPAPLLESPFKDRDRFQLGTLSHTLNIKASGYLELSNWPEVAPDPSVR 650
651 NVEVIESAKEWTPLGKTKKEKPMKKFYSESEEEEDEDEDEDEEEEEKEDE 700
701 DENPSDSSSDSESGSGSESGDTGTEDSSEDSSSGQDSETGSQAEAERQKV 750
751 AKRNSKTKRKSDSENREKKNENSKASESSSEESSSMEDSSSESESESGSD 800
801 SEPAPRNVAPAKERKPQQERHPPSKDVFLLDLDDFNPVSTPVALPTPALS 850
851 PSLIADLEGLNLSTSSSVINVSTPVFVPTKTHELLHRMHGKGLAAHYCFP 900
901 RQPCIFSDKMVSVQITLTNTSDRKIENIHIGGKGLPVGMQMHAFHPIDSL 950
951 EPKGSVTVSVGIDFCDSTQTASFQLCTKDDCFNVTLQPPVGELLSPVAMS 1000
1001 EKDFKKEQGTLTGMNETSATLIAAPQNFTPSMILQKVVNVANLGAVPSSQ 1050
1051 DNVHRFAARTVHSGSLMLVTVELKEGSTAQLIINTEKTVIGSVLLRELKP 1100
1101 VLSQG 1105
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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