 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6ZPJ0 from www.uniprot.org...
The NucPred score for your sequence is 0.68 (see score help below)
1 MTSLNGRHAEKTIDMPKPSAPKVHVQRSVSRDTIAIHFSASGEEEEEEEE 50
51 EFRGYLEEGLDDQSIVTGLEAKEDLYLESQGGHDPAGPVSTAPADGLSVS 100
101 ESPAILPVSENTVKLLESPAPALQVLSPVPLALSPGSSSSGPLASSPSVS 150
151 SLSEQKTSSSSPLSSPSKSPVLSSSASSSALSSAKPFMSLVKSLSTEVEP 200
201 KESPHPPRHRHLMKTLVKSLSTDTSRQESDTVSYKPPDSKLNLHLFKQFT 250
251 QPRNTGGDSKTAPSSPLTSPSDTRSFFKVPEMEAKIEDTKRRLSEVIYEP 300
301 FQLLSKIIGEESGSHRPKALSASASELSSLSGLNGHLESNNYSIKEEEGD 350
351 SEGEGYGSDSNTSRSDHLKPTEDASKEVEPKGSQASSLKDLGLKTSSLVL 400
401 EKCSLSALVSKEDEEFCELYTEDFELETEGEGRLDKTLDLPLKPEVLASD 450
451 GVALESEDEEDSATEHQELPVKTLGFFIMCVYAYLILPLPYYMSGLFLGV 500
501 GLGFMTAVCMIWFFTPPSAHKHHKSLKALRHQSTRSLDIKEPEILKGWMN 550
551 EIYNYDPETYHATLTHSVFVRLEGGTLRLSKPNKNISRRASYNETKPEVT 600
601 YISQKIYDLSDSKIYLVPKSLARKRIWNKKYPICIELGRQDDFMSKAQSD 650
651 KEATEEKPPPEKELPSEDLKKPPQPQEGTKSSQRDPILYLFGRTGREKEE 700
701 WFRRFILASRLKSELRKPAGVSGSKSGLLPAHSRHSSPSGHLSHSRSSSK 750
751 GSVEEMMSQPKQKELVGSVRQKMLLDYSVYMGRCVPQDNRSPHRSPVQSA 800
801 ESSPTASKKLPEAPPSEEEEQEAWVNALLGRIFWDFLGEKYWSDVVSKKI 850
851 QMKLSKIKLPYFMNELTLTELDMGVAVPKILQAFKPYVDHQGLWIDLEMS 900
901 YNGSFLMTLETKMNLTKLGKEPLVEALKVGEIGKEGCRPRAYCLADSDEE 950
951 SSSAGSSEEDDPPEPTAGDKQPLPGAEGYVGGHRTSKIMRFVDKITKSKY 1000
1001 FQKATETEFIKKKIEEVSNTPLLLTVEVQECRGTLAVNIPPPPTDRIWYG 1050
1051 FRKPPYVELKARPKLGEREVTLVHVTEWIEKKLEQELQKVFVMPNMDDVY 1100
1101 IPIMHSAMDPRSTSCLLKEPPVETSDQL 1128
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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