 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P13828 from www.uniprot.org...
The NucPred score for your sequence is 0.83 (see score help below)
1 MKVIGLLFSFVFFAIKCKSETIEVYNDLIQKLEKLESLSVDGLELFQKSQ 50
51 VIINATQPTETIDPFTNHNFAQQVQDFVTKFEGLGFTEQTELVNLIKALT 100
101 PNRYGVKYLIESKEEFNGLMHAINFYYDVLRDKLNDMCANNYCEIPEHLK 150
151 ISEEETEMLKKVILGYRKPIENIQDDIEKLEIYIERNKETVAALNALIAE 200
201 ETKKIQPEGNEDCNDASCDSDKYNKKKPIYQAMYNVIFYKKQLAEIQKVV 250
251 EVLEKRVSTLKKNDAIKPLWQQIEVLNAAPVVTAETQIVTGGQSSTEPGS 300
301 GGSSASGTSSSGQASAGTGVEQANTVASVTVTPSVGQNGEASTNPQTAQV 350
351 QPVPTLTLEEKQKKIAGLYAQIKEIAKTIKFNLEGIFVDPIELEYFKKEK 400
401 KKESCNLSTSSCKKNKASETIIPLTIRYPNGISYPLPENDVYNKIANNAA 450
451 ETTYGDLTHPDNTPLTGDLATNEQARKDLIKAIKKKIKAEEKKLETLKTN 500
501 YDNKLTEFNQQKTPFKEAAKEFYESKFRNKLTSEIFEKFKTKRDEYMTKK 550
551 TELNTCEYGNTKELINKLNKQLNYLQDYSLRKDIISNEIEYFSNKKKELQ 600
601 YNINRLAEAVQAKQNVLVASKDVPLSTLVELQIQKSLLTKQIEQLNKTEV 650
651 SLNKAQLKDKLYVPKTYGNEGKPEPYYLIAVKKEVDRLAQFIPKIESMIA 700
701 KEKERMEQGPAITGESEEVPSGPSAESSTDRSTQSSTSSSSSSSSTPAAA 750
751 ESSSATLPEAPAPAEAASPSTEASEETTIPPTTQETQPSQAASSTTPAKP 800
801 VMTKLYYLEKLQKFLVFSYSCHKYVLLQNSTINKDALSKYALTSEEDKIR 850
851 TLKRCSELDVLLAIQNNMPTMYSLYESIVDGLQNIYTELYEKEMMYHIYK 900
901 LKDENPSIKSLLVKAGVIEPEPVAAPTPVTPAATEQQQQQATPDVQSDAP 950
951 APSDVSQQPETPVTSTTPEVTTSTEASSSAPGEGTPSGEAGASGTEGATA 1000
1001 SNAATPAGTSASGSAASNASTTSDVTPPAAAAAVPSTSTPAPAQPPAANS 1050
1051 QSGNPDSGIRSRAESEEDMPADDFELDNLYKSYLQQIDGNNTEFINFIKS 1100
1101 KKELIKALTPEKVNQLYLEIAHLKELSEHYYDRYSTYKLKLERLYNKHEQ 1150
1151 IQLTNRQIRDLSILKARLLKRKQTLNGVFYILNGYVNFFNKRREAEKQYV 1200
1201 DNALKNTDMLLKYYKARTKYFTSEAVPLKTLSKASLDRESNYLKIEKFRA 1250
1251 YSRLELRLKKNINLGKERISYVSGGLHHVFEEFKELIKDKDYTGKKNPDN 1300
1301 APEVTNAFEQYKELLPKGVTVSTPAVAVTTTLAADAPATPEGAVPGAVPG 1350
1351 AVPGAVPGAVPGAVPGSGTDTRVAGSSVDDNEDDDIYQIASGQSEDAPEK 1400
1401 DILSEFTNESLYVYTKRLGSTYKSLKKHMLREFSTIKEDMTNGLNNKSQK 1450
1451 RNDFLEVLSHELDLFKDLSTNKYVIRNPYQLLDNDKKDKQIVNLKYATKG 1500
1501 INEDIETTTDGIKFFNKMVELYNTQLAAVKEQIATIEAETNDTNKEEKKK 1550
1551 YIPILEDLKGLYETVIGQAEEYSEELQNRLDNYKNEKAEFEILTKNLEKY 1600
1601 IQIDEKLDEFVEHAENNKHIASIALNNLNKSGLVGEGESKKILAKMLNMD 1650
1651 GMDLLGVDPKHVCVDTRDIPKNAGCFRDDNGTEEWRCLLGYKKGEGNTCV 1700
1701 ENNNPTCDINNGGCDPTASCQNAESTENSKKIICTCKEPTPNAYYEGVFC 1750
1751 SSSSFMGLSILLIITLIVFNIF 1772
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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