 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8NEN9 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MGLLLMILASAVLGSFLTLLAQFFLLYRRQPEPPADEAARAGEGFRYIKP 50
51 VPGLLLREYLYGGGRDEEPSGAAPEGGATPTAAPETPAPPTRETCYFLNA 100
101 TILFLFRELRDTALTRRWVTKKIKVEFEELLQTKTAGRLLEGLSLRDVFL 150
151 GETVPFIKTIRLVRPVVPSATGEPDGPEGEALPAACPEELAFEAEVEYNG 200
201 GFHLAIDVDLVFGKSAYLFVKLSRVVGRLRLVFTRVPFTHWFFSFVEDPL 250
251 IDFEVRSQFEGRPMPQLTSIIVNQLKKIIKRKHTLPNYKIRFKPFFPYQT 300
301 LQGFEEDEEHIHIQQWALTEGRLKVTLLECSRLLIFGSYDREANVHCTLE 350
351 LSSSVWEEKQRSSIKTVELIKGNLQSVGLTLRLVQSTDGYAGHVIIETVA 400
401 PNSPAAIADLQRGDRLIAIGGVKITSTLQVLKLIKQAGDRVLVYYERPVG 450
451 QSNQGAVLQDNFGQLEENFLSSSCQSGYEEEAAGLTVDTESRELDSEFED 500
501 LASDVRAQNEFKDEAQSLSHSPKRVPTTLSIKPLGAISPVLNRKLAVGSH 550
551 PLPPKIQSKDGNKPPPLKTSEITDPAQVSKPTQGSAFKPPVPPRPQAKVP 600
601 LPSADAPNQAEPDVLVEKPEKVVPPPLVDKSAEKQAKNVDAIDDAAAPKQ 650
651 FLAKQEVAKDVTSETSCPTKDSSDDRQTWESSEILYRNKLGKWTRTRASC 700
701 LFDIEACHRYLNIALWCRDPFKLGGLICLGHVSLKLEDVALGCLATSNTE 750
751 YLSKLRLEAPSPKAIVTRTALRNLSMQKGFNDKFCYGDITIHFKYLKEGE 800
801 SDHHVVTNVEKEKEPHLVEEVSVLPKEEQFVGQMGLTENKHSFQDTQFQN 850
851 PTWCDYCKKKVWTKAASQCMFCAYVCHKKCQEKCLAETSVCGATDRRIDR 900
901 TLKNLRLEGQETLLGLPPRVDAEASKSVNKTTGLTRHIINTSSRLLNLRQ 950
951 VSKTRLSEPGTDLVEPSPKHTPNTSDNEGSDTEVCGPNSPSKRGNSTGIK 1000
1001 LVRKEGGLDDSVFIAVKEIGRDLYRGLPTEERIQKLEFMLDKLQNEIDQE 1050
1051 LEHNNSLVREEKETTDTRKKSLLSAALAKSGERLQALTLLMIHYRAGIED 1100
1101 IETLESLSLDQHSKKISKYTDDTEEDLDNEISQLIDSQPFSSISDDLFGP 1150
1151 SESV 1154
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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