 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P38742 from www.uniprot.org...
The NucPred score for your sequence is 0.87 (see score help below)
1 MDECLPNSCLLGVHLVISTHSGPQIVYHYPPSNTAFLTNNPTKHQHLYGN 50
51 HANLNKNTSTNKEEKLFNSGSTKTASQIALNESAKSYNTAITPSMTNTNT 100
101 NNVTLPPTRSHANTVGSQSSIPAATNGVGYRKTDIEDTSRTFQYQETESE 150
151 TSSSGLSDSELSTDYLDISSDSFSISSSLSSSSLSSSPSSSSSSSPPQDG 200
201 LSRTNSSFQSTDSMSPTSPQMIMENDSISVAESYLDSGTNNKSRAASKRS 250
251 QNFFHKLSTKKSTDSKTHSPVRKLKSKPSQSTKKGNKLLKNTSNETDGNA 300
301 FTGSCSISSKKSLSSTGEHNQELRNSSLNDTPGQSPHHYHHRYHHYHKNA 350
351 ATSQRNSHTQYDVEEEDMEVSAMLQDGKISMNEIFFEEENFQDINKILEF 400
401 DNDFVAEFCSPEREMCNTRFEFTVDNFCFLGLPIHVDSQGRWRKSKHKNK 450
451 TRSKRSSSTTTNISRKKSIASKISSLSENTLKKVNSGEADTVYDSNIGHE 500
501 ASTDTPNLRINTDVSGNEFEREKEDLGKNMNMFHVCFVMNPHLIEYNKRI 550
551 DDMYQFVVTRLSLLLRYVQSKTSYISSECHIILKEKERVLKHSKTYQSIR 600
601 GAGNKGKYLYQRILAKSSLARALTECVDKIQRNEIACLEINDDKVISLQI 650
651 PIQNEFEKMPNFKLQPVLRGSYLTSILNMKFLEKSSLRIESQNRQNDQAQ 700
701 FSDTNNNIYRFGNNINSTGHCGAANVDDGDDNESNYYCDDNDDLLNYALL 750
751 LLDEPNNIISSLETFSYQDDIGTIILKHLVRNIQPNIPLRSYRYLITDLL 800
801 DNPSSLDDLTTETNSLESSILRSCALHLMYWRHARIVIPLSSKYTYIVSP 850
851 LAPIQGYTIDDYKSTSQNDGNVKKMDDRENNKSGSDRVPLIYQNSMLFRS 900
901 KFPSLPSLPIFLSLLSTDKPQAYSNIIPSREHKPVYLNALAWLIQYGYVT 950
951 QLLTFINIRVDKHIKMAVDEDLEKEGFRKTNTARRPSMDYKKTDKKLDDE 1000
1001 DGQSRDANASEACSGKNEGMQSNDNNKDVDEKDNENDSRVDDRDDNEIAI 1050
1051 ADEEEILHFEYDDPEMQHDYTIILEPERATAIEKRWLYRCIYGQPSDIQI 1100
1101 LFNKLLKYFNGKVPMELVIIKEEISRHDLKKLLNALDKYLIEIHHW 1146
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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