 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q25490 from www.uniprot.org...
The NucPred score for your sequence is 0.36 (see score help below)
1 MGKSNRLLSVLFVISVLWKAAYGNGKCQIACKGSSSPSFAAGQKYNYGVE 50
51 GTVSVYLTGADNQETSLKMLGQASVSAISNCELELSVHNMVLSGPDGKKY 100
101 PCPQGIEKPVRFSYQDGRVGPEICAAEDDSRRSLNIKRAIISLLQAEQKP 150
151 SVQVDVFGVCPTEVSSSQEGGAVLLHRSRDLSRCAHREQGRNDFVNSIAN 200
201 PDAGIKDLQVLQSMLNVESKVNNGVPEKVSAIEEYLYKPFSVGENGARAK 250
251 VHTKLTLSGKGGAGGGNAHCTESRSIIFDVPHGTSSASGNLNSVISAVKE 300
301 TARTVANDASSKSAGQFAQLVRIMRTSSKDDLMRIYSQVKAHQLEKRVYL 350
351 DALLRAGTGESIEASIQILKSKDLSQLEQHLVFLSLGNARHVNNPALKAA 400
401 AGLLDMPNLPKEVYLGAGALGGAYCREHDCHNVKPEGIVALSNKLGSKLQ 450
451 NCRPKNKPDEDVVVAILKGIRNIRHLEDSLIDKLVHCAVDNNVKARVRAV 500
501 ALEAFHADPCSAKIHKTAMDIMKNRQLDSEIRIKAYLAVIECPCSHSASE 550
551 IKNLLDSEPVHQVGNFITSSLRHIRSSSNPDKQLAKKHYGQIRTPNKFKV 600
601 DERKYSFYREMSYKLDALGAGGSVDQTVIYSQTSFLPRSVNFNLTVDLFG 650
651 QSYNVMELGGRQGNLDRVVEHFLGPKSFLRTEDPQALYDNLVKRFQESKK 700
701 KVEDSLSRGRRSIKSEIDVFDKNLKAESAPYNNELDLDIYVKLFGTDAVF 750
751 LSFGDDKGFDFNKMLDQILGGCNSGINKAKHFQQEIRSHLLFMDAELAYP 800
801 TSVGLPLRLNLIGAATARLDVATNIDIRQIFQSPQNAKADIKFVPSTDFE 850
851 ISGAFIIDADAFSTGIKVITNLHSSTGVHVNAKVLENGRGIDLQIGLPVD 900
901 KQELIAASSDLVFVTAEKGQKEKQKVIKMEKGENEYSACFDQLSGPLGLT 950
951 MCYDMVLPFPIVNRNDKLDSIAKAMGKWPLSGSAKFKLFLEKNDLRGYHI 1000
1001 KAVVKEDKDAGRRSFELLLDTEGAKTRRSQLTGEAVYNENEVGVKLGLEA 1050
1051 VGKVIYGHIWAHKKPNELVASVKGKLDDIEYSGKLGFSVQGNEHRAVYKP 1100
1101 IFEYSLPDGSSPGSKKYEVKIDGQVIRECDGRVTKYTFDGVHVNLQNAEK 1150
1151 PLEICGSVSTVAQPREVEFDVEVKHYASLKGSWKGSDVVLAFNNQLNPKI 1200
1201 NFDLKGKFENTDSMHNELDIHYGPNRGDNNARITFSQILKYHVENSKNFN 1250
1251 VITKNNLEIRAVPFKLVANADVDPKKIDIDIEGQLQDKSAGFNLDARTHI 1300
1301 KKEGDYSIKVKANLNNANLEAFSRRDIVNAEKSNVENYIDMKGVGRYELS 1350
1351 GFVLHKTKPNDVNVGFIGHLKINGGGKNEDFKINIGHIETPAVFSSHATI 1400
1401 SGSRGDIIDYLLKIMRTANPNGNFKLVIKDSIAANGQYKVTDADGKGNGL 1450
1451 IIIDFKKINRKIKGDVRFTAKEPVFNADIDLFLNFEKDNSDKVHFSTYNK 1500
1501 KTDKVMDTKNKLEYAGKRTEVNIHQDGILAVTGKAHTVAELVLPTERCLS 1550
1551 LKIDHDGAFKDGLYNGHMDMTISDAPKRGSGASTISYKGKVSNSNLDQEI 1600
1601 IDYEGQINFKLKDGKNLQSTFSLKNNPDGDKFKYEFKSDVNGNLIPKPAN 1650
1651 LVATGTYSNSENEIDETYRLKGSYGSDIGFELAGVGTIKFLDAGDKKYLD 1700
1701 DYTLTVRLPFEKAHDIKWVSTVLFLQPQGQEMTEYTLVESVQINADVYKI 1750
1751 DANGKVGPKNGYGAVKVLVPHVEPFVLDYNYKSSHEGEKNNNYVELKTKY 1800
1801 GKGKSASMVVDSSYAPHYSTLKVKANTPNNDKFKKLDVTVHSKNPSPDAY 1850
1851 SNSVVVDADGRVYKIDSSIVLSKAHPVLDIQYHSPSSDKIRRLYLQGSSL 1900
1901 SSTQGKLEVKVDNINDICLDAVSEANVQKDNVAFKVVANAKELGWKNYGI 1950
1951 DISSKDSGSGKRLEFHATNDNKNVLSGSTSFISKQEGQKTIIEGSGSVKV 2000
2001 KEEQKSANFKYIRTVFTDSNEKGVETFFNVALGERSYVAESRVTNYEYKN 2050
2051 SYVYCEEKKQCAHAEIQSKIDMSTPGMIVNVINAGLDLRKLGVAPELGLQ 2100
2101 MRDEVSDRRPPRFTLDLHINKEDRKYHLHAYNTPENGHYASGVTVRLPSR 2150
2151 VMALEYTLTHPTSQDLPFPIKGEACLDLDKNRPGHKTSARFLVDYSNSGS 2200
2201 EDKAVAEIGFFHPKIEKEAVIRLNAFMKRPENGCFKIESSASLCHSALGT 2250
2251 DRVAKVMFETTPNSVKFLADTPFVKAIDVEGSFNVNQQQRTQQCLFRICL 2300
2301 LEGKPVQMSALVKDYQYYEFTTEESNRKLSYVGHLIPEKRVDISTDIILS 2350
2351 GDKKNIAHGALFLQDNLVKSDYGLSKENFNYFLNALKKDLDTLEDRIKNV 2400
2401 GEKASKDVEAVTQRAAPYFKKVEDNFRAEWNRFYQEIADDKVFKEISHVF 2450
2451 NEIVQYIAKFIDEILQGTKRSWTPSCRPTLSHPRNREMYKKQIEPQVKQL 2500
2501 YDTLGALMKEYLDGVIDVVAHFAAIVTDFFEKHKAELQELTNVFTEIFKD 2550
2551 LTRLVVAQLKELPPKIAQIYNDIVSQITNMPFVVVLQEKWKEFNFAERAV 2600
2601 QLVSQAYEAFSKILPTDELKEFAKALNAYLLKKIKEEKMEESKELPRAVR 2650
2651 EAGQRVLLITSIPALAVRRPRLRRWTWHHLKLAVGAGASAPSLGAASWSA 2700
2701 LRQLAAGDGPPALAPRGLPTAQLDPLDEVPNKLRAVVVNGQHIFTFDGRH 2750
2751 LTFPGTCRYVLIHDHVDRNFTVLMQLANGQPKALVLEDKSGTIIELKDNG 2800
2801 QVILNCQSHGFPVVEQDVFAFRQTSGRIGLCSKYGLMAFCTSKFEVCYFE 2850
2851 VNGFYLGKLPGLLGDGNNEPYDDFRMPNGKICSSESEFGNSYRLSRSCPA 2900
2901 ANAPAHDHHQMHAPLPKPCERVFSGTSPLRPLSLMLDIAPFRQACIHAVT 2950
2951 GADADKDLQQACDLARGYRRSRSRGCCPPRCPTPACAARTATGPGSWATP 3000
3001 TSTNCPTDSLISSSPLRPLRTTPAHYKNMVVPLVSQLVDMLKGKHCTDIK 3050
3051 VFLVGHTSKHPYPILYDTDLKLKNAKVSFDDKSRYDRIPFVKTGHEKFDS 3100
3101 YSKTVVDFLNYIKIELGITNIEASQGQIFDLPLRPGAVKHVIFVTGGPTI 3150
3151 SQFFLLETVRALRNKVIIDEMAMSASLVTSTPGLKIGGGKNAAQIVGYEK 3200
3201 HGVLLLGEKKQSKDSEAVRATLEVEDDPFSDAVEFANGVVFSASNYAALP 3250
3251 AGQQKQFIQTAAHNIIQRMWREQIVQQCTCVFVDPFRVRSVCFNKARTEV 3300
3301 ARRRK 3305
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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