 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q70PP2 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MKNAIHPENCNGAGTEEEASSAFHAEIDRVLLNNNGNHGDSSNEGGGGNG 50
51 SGRGGATGSGNIAGLGGSESMWSPGGGKSHDVAQAFANALLLRNMNHVVG 100
101 KGQPVVQNHRKAYQCKGDTINPMANGEDLRLSKIIRRLINENNPTVSLEL 150
151 CSKLDQAVRTPINMGYMTCSFVWILDNMLTLYKQCPPPVLEECSKTLGLI 200
201 GFINRKSYPIYEEFIVKNYKSSKRMQKYMIMALRATLSCDTKCELHMYAD 250
251 KIMLLLKDFLENAESADIFIVVSNTLVQFAASYAETFECHFTDVVDIVIG 300
301 WQLEAGQPTDLKTHCAQVLEQLTPFFSKQIDFSYGLLDQFVEDITTLEEG 350
351 EPANTAERVGAFVGAFNTLLKCLARMQIFVGMPTCECIVKMAVDHLIKIM 400
401 PTLHLNTEALVNINELICICLLNNFTGLDPILLEQVLLDQVKRMISLTEL 450
451 QRQSVLYLLLCTVRRLRARLTPSLVHFIFQSNPYMTKVRLRSPGETSYKL 500
501 LLRTCQETLLIRNVPLLQQAYKYLVDDIDACLEKLLITAPRSKARKASVL 550
551 LVFHLSALAALAKQTSSIIGMYACKPSILELLLTNCRAHELKFWSKYPAA 600
601 QQAIFGLLVVHCQANHNFRTNSSLLRDQELSAENTSPTANSFASILRFLD 650
651 SVLGQAHQLAPQNLRVLLQWIQMLLRECREKIDLLMEQENFRGICRNIAA 700
701 TASKLVPLESAACIQTVLDYGLERLEKYPKLLILYRDTALQQLQMLSTNY 750
751 HAPYFQIYAQLPLHLTLTGGESSMPGMASRRVSVWQQRISQYSAVRDNVF 800
801 RDFFDRVQKPEQDSLIHCLRELFVRSCQVAPQDERQMNLSQCTKRCQRLA 850
851 IAWLQFEAARYCVDQRLRTTVGKPQETFLGFEAIIMRHARLLSGCAKEIE 900
901 RSALDDLSLEELLSMQSNLSLLLGFLDALEKLIYNAAEGSAFALRPPEKQ 950
951 VAAFFRLNNPTCQSWFNRIRIGVVIIAMHVQQPELVIRYAQQILVNSKTQ 1000
1001 DPTYSQAIVYMAWSLVSCQEADSLRGLRLWARGKSCKSYKWLKYAADQAA 1050
1051 GKRESALAGYRTILAEKELQSELEPHTRQFVVSQMMQCLQDLGQWSQLVE 1100
1101 LKQQQMTRPEDRELNPFLQRSNVEVNALERLLAKSEESCSSMDALGGVFQ 1150
1151 QLSLWPSNWDESVSSSGLSERASFSSIHMRQRTEDIVLHKLLEDRCVPDQ 1200
1201 AKNLLDTQWRDSLLNPSFDQRSCKELTLLRHIVQGVSGGQELSLLPVSSG 1250
1251 RCQNRSKFISSAILMRCLAWTQLLRQHCAPGSWETLCLDAAAAAREEGNL 1300
1301 QLAETLLTQFFGQPIGEIAALFSLEQGVQTDNPEMLRGYSELVKCLHLQQ 1350
1351 QQSQTHSGDLSSSIDVCAALCLNIQKSNNQPAAGADLLLNLADWIAVRTC 1400
1401 NGLTTNQSPVLIQLLDQLPECPLTCDSSQPLAIPQAERMVARLVHSCLQQ 1450
1451 RPNYAEALIAYGNWCYRWGKKVADSCCVLTQADATAISQALDIPQPLESE 1500
1501 KLDELLQALSTEQPPANCVEVCPDAARARDDEAAKNRLRRLTFLADKTPE 1550
1551 ALDAILQIWRRAIANTYDYYKDAARSYFQYLSFKSGSGPEKPEGEGVVSQ 1600
1601 RERLHVDDSNLVTTTLRLLRLIVKHASGLQEVLEQGLHTTPIAPWKVIIP 1650
1651 QLFSRLNHHEPYVRKSVCDLLCRLAKSRPQLVIFPAVVGANREQQDATAP 1700
1701 PATARPTTEDACCYGYLLGELSKQAPEAVQHVKLMVKELRRVCLLWDEYW 1750
1751 IHSLAHIYNTYVSRVSALATDFRPDDHEGKNNRFNVWRPQLLADLEALVA 1800
1801 VTSRPPETTYERSFRKRFDAPIRLTVDALRHRRYPEAWDKLKQLYHILQS 1850
1851 NMIRGSGSTLKMQSISPVLCGIGRMRISMPGLDAHGPDGDQVYIESVESS 1900
1901 VCVLPTKTKPKKVAFYGSNGQRYTFLFKGMEDLHLDERIMQFLSISNAIM 1950
1951 ACRSDAPGNGCYRAHHYSVIPLGPQSGLISWVDGVTPVFALYKKWQQRRS 2000
2001 QVAGNAGAGAVANVPRRFTDLFYNKLSPLLAKHNMQVSDPRRQWPISVLL 2050
2051 QVLDELSQETPNDLLARELWCQAGNAAEWRQSVRRFVRCMSVMSMIGYVI 2100
2101 GLGDRHLDNVLINLGSGDIVHIDYNVCFEKGRTLRIPEKVPFRLTQNLVQ 2150
2151 AMGITGIEGPFRLGCEYVLKVMRKERETLLTLLEAFVYDPLVDWTTNDDA 2200
2201 QALRRSLNAKLQESADGGGAGGLGVGDLKYHKKDKNKGKPLDSDVKRQPF 2250
2251 LSKLGMLQKYWSTNKTELMPQLEEMEQEVGNLQAAQAKQVVAEEELVKLN 2300
2301 QRSALIAEIKSLGTAIESHSFNTASLRNAVRRGHSEALALLSTERLPDFG 2350
2351 RVQCILRSYGQCLQLYHLLDLQGQLVKLQMESNSENAREFSALTEALQLS 2400
2401 GLDSMRSQLNELLGRMDMVAQKSSKHLQEYAGVMNFYPEQSHRQNLFVRF 2450
2451 HDSFATYIQNGYTADSTTNTNSPSSSIICKADVVGVAEAMEYSWERLGCQ 2500
2501 LHEASKLYAANQAQALTLGAPTTALLSMIVQSGCSQLLLKASLVRTLDRA 2550
2551 GGAFAAYEQVALASHDDGLLHHQLLFIHLVRTMLQGVLVMTKEEDQHLAQ 2600
2601 LESLLSALSHLKKMFEYDLPANLYRLLLLQPNLGKLSALCHLSASSLAQL 2650
2651 FLEATMENGHKPPDQFPVERRFLLTLQPVYDQFLLASTSLDSLVSSMQSM 2700
2701 LEDVHDVQTQQIMELGLMRSCHTELNDECFFGLVSEALESSRTCDVREMA 2750
2751 RPMLGFIHRLQVEKLAGLLPILTRNFYTAVGPQCLPTASCGDPAQADHLC 2800
2801 ESLFISLQSDGALLQQQAEIALLSQQVDLHTLAASAQYWAYSEALGSQLR 2850
2851 CGPHIVSRPKLTAAIGECWLELDQKLTALQQLQAGLESQLSQLQTQRSNW 2900
2901 NRNHIDNLLRMEQCNKQRTMSHVALLQKMTDGAGAVARLEQNAIVVGEEG 2950
2951 QALVDHLEQWLAAHGQWQASSSRISAVEQSMVELLDPEGAIDHYWLENVQ 3000
3001 GLLEEQTCKVHREIAAIEGEQQSKHRFICTLLKETLRLLENMPRFHVQSL 3050
3051 CSEAQAQGQGKMEYANVQLLSDHLREGQGLMQSLYMRLQELRKDICSDRR 3100
3101 VLQPSMLQNWRHQLEMILTLAKQEVNEFFKGLEDFMQHAGETDSYEIFTH 3150
3151 AKGSGNVHEQKRNAYGVSVWKKIRMKLEGRDPDSNQRSTVAEQVDYVIRE 3200
3201 ACNPENLAVLYEGWTPWV 3218
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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