 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P32528 from www.uniprot.org...
The NucPred score for your sequence is 0.22 (see score help below)
1 MTVSSDTTAEISLGWSIQDWIDFHKSSSSQASLRLLESLLDSQNVAPVDN 50
51 AWISLISKENLLHQFQILKSRENKETLPLYGVPIAVKDNIDVRGLPTTAA 100
101 CPSFAYEPSKDSKVVELLRNAGAIIVGKTNLDQFATGLVGTRSPYGKTPC 150
151 AFSKEHVSGGSSAGSASVVARGIVPIALGTDTAGSGRVPAALNNLIGLKP 200
201 TKGVFSCQGVVPACKSLDCVSIFALNLSDAERCFRIMCQPDPDNDEYSRP 250
251 YVSNPLKKFSSNVTIAIPKNIPWYGETKNPVLFSNAVENLSRTGANVIEI 300
301 DFEPLLELARCLYEGTWVAERYQAIQSFLDSKPPKESLDPTVISIIEGAK 350
351 KYSAVDCFSFEYKRQGILQKVRRLLESVDVLCVPTCPLNPTMQQVADEPV 400
401 LVNSRQGTWTNFVNLADLAALAVPAGFRDDGLPNGITLIGKKFTDYALLE 450
451 LANRYFQNIFPNGSRTYGTFTSSSVKPANDQLVGPDYDPSTSIKLAVVGA 500
501 HLKGLPLHWQLEKVNATYLCTTKTSKAYQLFALPKNGPVLKPGLRRVQDS 550
551 NGSQIELEVYSVPKELFGAFISMVPEPLGIGSVELESGEWIKSFICEESG 600
601 YKAKGTVDITKYGGFRAYFEMLKKKESQKKKLFDTVLIANRGEIAVRIIK 650
651 TLKKLGIRSVAVYSDPDKYSQHVTDADVSVPLHGTTAAQTYLDMNKIIDA 700
701 AKQTNAQAIIPGYGFLSENADFSDACTSAGITFVGPSGDIIRGLGLKHSA 750
751 RQIAQKAGVPLVPGSLLITSVEEAKKVAAELEYPVMVKSTAGGGGIGLQK 800
801 VDSEEDIEHIFETVKHQGETFFGDAGVFLERFIENARHVEVQLMGDGFGK 850
851 AIALGERDCSLQRRNQKVIEETPAPNLPEKTRLALRKAAESLGSLLNYKC 900
901 AGTVEFIYDEKKDEFYFLEVNTRLQVEHPITEMVTGLDLVEWMIRIAAND 950
951 APDFDSTKVEVNGVSMEARLYAENPLKNFRPSPGLLVDVKFPDWARVDTW 1000
1001 VKKGTNISPEYDPTLAKIIVHGKDRDDAISKLNQALEETKVYGCITNIDY 1050
1051 LKSIITSDFFAKAKVSTNILNSYQYEPTAIEITLPGAHTSIQDYPGRVGY 1100
1101 WRIGVPPSGPMDAYSFRLANRIVGNDYRTPAIEVTLTGPSIVFHCETVIA 1150
1151 ITGGTALCTLDGQEIPQHKPVEVKRGSTLSIGKLTSGCRAYLGIRGGIDV 1200
1201 PKYLGSYSTFTLGNVGGYNGRVLKLGDVLFLPSNEENKSVECLPQNIPQS 1250
1251 LIPQISETKEWRIGVTCGPHGSPDFFKPESIEEFFSEKWKVHYNSNRFGV 1300
1301 RLIGPKPKWARSNGGEGGMHPSNTHDYVYSLGAINFTGDEPVIITCDGPS 1350
1351 LGGFVCQAVVPEAELWKVGQVKPGDSIQFVPLSYESSRSLKESQDVAIKS 1400
1401 LDGTKLRRLDSVSILPSFETPILAQMEKVNELSPKVVYRQAGDRYVLVEY 1450
1451 GDNEMNFNISYRIECLISLVKKNKTIGIVEMSQGVRSVLIEFDGYKVTQK 1500
1501 ELLKVLVAYETEIQFDENWKITSNIIRLPMAFEDSKTLACVQRYQETIRS 1550
1551 SAPWLPNNVDFIANVNGISRNEVYDMLYSARFMVLGLGDVFLGSPCAVPL 1600
1601 DPRHRFLGSKYNPSRTYTERGAVGIGGMYMCIYAANSPGGYQLVGRTIPI 1650
1651 WDKLCLAASSEVPWLMNPFDQVEFYPVSEEDLDKMTEDCDNGVYKVNIEK 1700
1701 SVFDHQEYLRWINANKDSITAFQEGQLGERAEEFAKLIQNANSELKESVT 1750
1751 VKPDEEEDFPEGAEIVYSEYSGRFWKSIASVGDVIEAGQGLLIIEAMKAE 1800
1801 MIISAPKSGKIIKICHGNGDMVDSGDIVAVIETLA 1835
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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