 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6QNF3 from www.uniprot.org...
The NucPred score for your sequence is 0.43 (see score help below)
1 MQVPGTMPAPVLKGQALWLPLLLMLSPQASGGLVITPPGPELVLNISSTF 50
51 VLTCSGPAPVVWERLSQEPLQKMARTQDGTFSSTLTLTNVTGLDTGEYFC 100
101 TYKGSHGLEPDGRKRLYIFVPDPTMGFLPVDPEELFIFLTEITEITIPCR 150
151 VTDPRLVVTLHEKKVDIPLPIPYDHQRGFSGTFEDKTYVCKTTIGDKEVD 200
201 SEAYYVYSLQVSSINVSVNAVQTVVRQGENITIMCIVTGNEVVNFEWTYP 250
251 RMESGRLVEPVTDFLFNVPSHIRSILHIPSAELGDSGTYICNVSESVNDH 300
301 RDEKSINVTVVESGYVRLLGELDAVQFAELHRSRALQVVFEAYPPPTVVW 350
351 FKDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGYYTMRAFH 400
401 EDAEAQLSFQLQVNVPVRVLELSESHPASGEQTVRCRGRGMPQPHLTWST 450
451 CSDLKRCPRELPPTPLGNSSEEESQLETNVTYWPEDQEFEVVSTLRLRRV 500
501 DQPLSVRCTLHNLLGHDMQEVTVVPHSLPFKVVVISAILALVVLTIISLI 550
551 ILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQL 600
601 VLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMS 650
651 ELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTF 700
701 LQLCSDKRRPPSAELYSNALPAGLPLPSHVSLPGESDGGYMDMSKDESVD 750
751 YVPMLDMKGGVKYADIESSSYMAPYDNYVPTAPERTCRATLINESPVLSY 800
801 TDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARD 850
851 IMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLG 900
901 GTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPP 950
951 FSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAVLRSQARLPGFPGLRS 1000
1001 PLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADGPLESSPSLASSTLNE 1050
1051 VNTSSTISCDSPLEPQEEPEPEPEPQPEPQVVPEPPLDSSCPGPRAEAED 1100
1101 SFL 1103
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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