 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O61309 from www.uniprot.org...
The NucPred score for your sequence is 0.75 (see score help below)
1 MGSLSQQAHGPPDAPRSKEELLIHAKDFINQYFTSFQMNKTRAHFHRLGE 50
51 INDLIEKSGTYDLTMAELTFGAKHAWRNAPGCIGRSQWSKLQVFDAREIG 100
101 TPREMFEALCSHIRYATNEGKIRSTITIFPQRKEGRPDFRVWNTQLISYA 150
151 GYKLGDGKVIGDPANVEFTEMCVEMGWKPKHGMFDLLPLVLSAAENSPEY 200
201 FELPTELVLEVTLKHPEYPWFAEMGLKWYALPTDSGMLLDCGGLEFPSCP 250
251 FNGWFMGTMIGSRNLCDPHRYNMLEPIGLKMGLNTETASSLWKDRVLIEV 300
301 NVAVLYSFESANVTIVNHHDASTDFISHMDKEIKLRGGCPSDWVRMVPPM 350
351 SGSTLEVFHQEMLLYNLHPAFVRQDVKPWKKHVWKSDQSVPINSCNPKRK 400
401 LGFKALARAVEFSASLMSKALSSRVKCSIFYATETGRSERFARRLSEIFK 450
451 PVFHSRVVCMDDYAVETLEHESLVMVITSTFGNGEPPENGKQFAQSLLDM 500
501 KRKYDCDLGFLESCSSISTCIKSSILTEGPLAADVIGDRQSLAMGTGPLC 550
551 NVRFAVFGLGSKAYPYYAAYGKYIYLMLQELGAERLVNYCAGDALYGQEQ 600
601 SFRAWSEEVFKASCEAFCLDNRNDAPGPQTKGDCSKVRIVPVENCQEPDL 650
651 CQVLRNIHGKEVMPLILAERIQLQAKDSDQQTILIKLDAHNATDLKYAPG 700
701 DHVAIFPANSPEIVDAILVRLDTSKGPSPDQVVKTEISTQLGTNDTWRSH 750
751 LPICTSRTAFSFLLDVTTPPSQEILQVLATQASSDMDKHKLEQLASNSEA 800
801 YEKWRLDLSPNILEILDEFPSLKIPPSLLLTQLPLLQPRYYSISSSQQKN 850
851 PNEVHATIAVVRFKTQDGDGPVHEGVCSSWLNRSPIGTVVPCFLRSAPHF 900
901 HLPEDPSLPIIMIGPGSGIAPFRSFWQQRLGEIENTMPSCENTMLSCETT 950
951 IPSCENSMPSCENTMPSCENTMPSCENTIPSCENTIPSCENTMPSCENTI 1000
1001 PSWERTMQPCQIILPSQTKKHFGEMVLYTGCRTAKHMIYAAELEEMKRLG 1050
1051 VLSNYHVALSREAALPKMYVQDIIIKNAAAVYEIVMKKGGHFYVSGDVSM 1100
1101 AHDVTRALELVLCQQGGREASQQVMSLRDENLFHEDIFGSFVRKAGGQRS 1150
1151 EDE 1153
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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