 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O00936 from www.uniprot.org...
The NucPred score for your sequence is 0.68 (see score help below)
1 MERKQTQMILGRRLAKDSPEVKHFQRKSSVVPFGRDGRAATNFTCWTADC 50
51 PAVKADPTLVFAKCIVVGGSMDTQLELEQVDPPARGTFTVAPTDVFNANE 100
101 LIEPETVDDIGYLPHTNVACVLDVLKSRFLRSIIYTTAEPLLVAINPFKD 150
151 LGNTTDAWISTYRNASKPEMLPPHVFKTARAALEDLEGYKKNQSIIVSGE 200
201 SGAGKTEATKQIMRFFASASSEVRTTIQDTIMAGNPILEAFGNAKTIRNN 250
251 NSSRFGRFMMLDVSSHRGIQHGSISNFLLEKVRVVSQEANERSYHIFYQL 300
301 LKGATSEMRAKYHLRSLKEYAYLNGKNGGCYDVPGIDDKADFEEVLQSLD 350
351 AMQITGSKRHSVFSILSGLLLIGNVSIEGKDAQGVPDAAYISPQSEEILE 400
401 EACQLLSVDDAALKKEILVKSTKVGPQVIEGVRTKDEAKTSVLSLSKNVY 450
451 DKLFDWLVRQLNSLIDAPDGMPNFIGILDIFGFEVLEVNSLEQVLINITN 500
501 EYLQKHFIDVVFDMETKLYQAEGVPTEALEYTDNLALVGALCGKNDSFFA 550
551 LLEDACLGIRSTDEGFCGTILRRLEPSGFFLESRRDKRLKFIIRHTIADI 600
601 EYTCEGMLEKNKDFLRKEVMDVMKASTDPVTKALFEGIEIEAGKIGKGTL 650
651 IASRFLKNLEEMIGIVAQTEAHFIRCLKPNEEKKPLGWNGSKVLNQLFSL 700
701 SILEALQLRQVGYAYRRNFSEFCSHFRWLDLGLVNSDRDRKEVAQLLLEQ 750
751 SGIPESSWVIGKTMVFVKPDAAKELSILQREKLMCFQPLIAVLGPMWRKV 800
801 LLRKKMARVIHFLTRLESNARRHLEPDSINISPEEREALLSGMERPRNPC 850
851 VVVKKRVEPERAPPTKVLSLSRARLSLSKELPRNYAASNEALDVDDTMSV 900
901 DTDAFLRLKMKRSPNENYLRQTALARLKERRPSHVCMEEAYHVWRSVELL 950
951 FREPLSDKRLQNICTVIRNDMDQHYGFFWQVIINRTPNFGMAATHIHGSL 1000
1001 HVVEQEGMYRDGRQFLFHLIMYKTRKPRKEEIRLHERAAEKTYGICRKRD 1050
1051 FSGIVRVMNSKVPPYMQKDVSYLIGMLFQRYQYTRDWTNFATCIQSYLIG 1100
1101 RYSEPFGGAWNVVAQEGAFFLSRLWTKHSRFLRVEIDFPALAEQASSEPC 1150
1151 PGCPTPVLTVVCFEACAPDRP 1171
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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