 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P02563 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MTDAQMADFGAARYLRKSEKERLEAQTRPFDIRTECFVPDDKEEYVKAKI 50
51 VSREGGKVTAETENGKTVTVKEDQVMQQNPPKFDKIEDMAMLTFLHEPAV 100
101 LYNLKERYAAWMIYTYSGLFCVTVNPYKWLPVYNAEVVAAYRGKKRSEAP 150
151 PHIFSISDNAYQYMLTDRENQSILITGESGAGKTVNTKRVIQYFASIAAI 200
201 GDRSKKDNPNANKGTLEDQIIQANPALEAFGNAKTVRNDNSSRFGKFIRI 250
251 HFGATGKLASADIETYLLEKSRVIFQLKAERNYHIFYQILSNKKPELLDM 300
301 LLVTNNPYDYAFVSQGEVSVASIDDSEELLATDSAFDVLGFTAEEKAGVY 350
351 KLTGAIMHYGNMKFKQKQREEQAEPDGTEDADKSAYLMGLNSADLLKGLC 400
401 HPRVKVGNEYVTKGQSVQQVYYSIGALAKSVYEKMFNWMVTRINATLETK 450
451 QPRQYFIGVLDIAGFEIFDFNSFEQLCINFTNEKLQQFFNHHMFVLEQEE 500
501 YKKEGIEWEFIDFGMDLQACIDLIEKPMGIMSILEEECMFPKATDMTFKA 550
551 KLYDNHLGKSNNFQKPRNVKGKQEAHFSLVHYAGTVDYNILGWLEKNKDP 600
601 LNETVVGLYQKSSLKLMATLFSTYASADTGDSGKGKGGKKKGSSFQTVSA 650
651 LHRENLNKLMTNLRTTHPHFVRCIIPNERKAPGVMDNPLVMHQLRCNGVL 700
701 EGIRICRKGFPNRILYGDFRQRYRILNPAAIPEGQFIDSGKGAEKLLGSL 750
751 DIDHNQYKFGHTKVFFKAGLLGLLEEMRDERLSRIITRIQAQARGQLMRI 800
801 EFKKMVERRDALLVIQWNIRAFMGVKNWPWMKLYFKIKPLLKSAETEKEM 850
851 ANMKEEFGRVKDALEKSEARRKELEEKMVSLLQEKNDLQLQVQAEQDNLA 900
901 DAEERCDQLIKNKIQLEAKVKEMTERLEDEEEMNAELTAKKRKLEDECSE 950
951 LKKDIDDLELTLAKVEKEKHATENKVKNLTEEMAGLDEIIAKLTKEKKAL 1000
1001 QEAHQQALDDLQAEEDKVNTLTKSKVKLEQQVDDLEGSLEQEKKVRMDLE 1050
1051 RAKRKLEGDLKLTQESIMDLENDKLQLEEKLKKKEFDISQQNSKIEDEQA 1100
1101 LALQLQKKLKENQARIEELEEELEAERTARAKVEKLRSDLTRELEEISER 1150
1151 LEEAGGATSVQIEMNKKREAEFQKMRRDLEEATLQHEATAAALRKKHADS 1200
1201 VAELGEQIDNLQRVKQKLEKEKSEFKLELDDVTSHMEQIIKAKANLEKVS 1250
1251 RTLEDQANEYRVKLEEAQRSLNDFTTQRAKLQTENGELARQLEEKEALIW 1300
1301 QLTRGKLSYTQQMEDLKRQLEEEGKAKNALAHALQSARHDCDLLREQYEE 1350
1351 EMEAKAELQRVLSKANSEVAQWRTKYETDAIQRTEELEEAKKKLAQRLQD 1400
1401 AEEAVEAVNAKCSSLEKTKHRLQNEIEDLMVDVERSNAAAAALDKKQRNF 1450
1451 DKILAEWKQKYEESQSELESSQKEARSLSTELFKLKNAYEESLEHLETFK 1500
1501 RENKNLQEEISDLTEQLGEGGKNVHELEKIRKQLEVEKLELQSALEEAEA 1550
1551 SLEHEEGKILRAQLEFNQIKAEIERKLAEKDEEMEQAKRNHLRVVDSLQT 1600
1601 SLDAETRSRNEALRVKKKMEGDLNEMEIQLSQANRIASEAQKHLKNAQAH 1650
1651 LKDTQLQLDDAVRANDDLKENIAIVERRNTLLQAELEELRAVVEQTERSR 1700
1701 KLAEQELIETSERVQLLHSQNTSLINQKKKMDADLSQLQTEVEEAVQECR 1750
1751 NAEEKAKKAITDAAMMAEELKKEQDTSAHLERMKKNMEQTIKDLQHRLDE 1800
1801 AEQIALKGGKKQLQKLEARVRELENELEAEQKRNAESVKGMRKSERRIKE 1850
1851 LTYQTEEDKKNLVRLQDLVDKLQLKVKAYKRQAEEAEEQANTNLSKFRKV 1900
1901 QHELDEAEERADIAESQVNKLRAKSRDIGAKQKMHDEE 1938
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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