 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P59328 from www.uniprot.org...
The NucPred score for your sequence is 0.68 (see score help below)
1 MPATQKPMRYGHTEGHTEVCFDDSGSYIVTCGSDGDVRMWEDLDDDDPKS 50
51 VNVGEKAFSCALKNGKLVTAVSNNTVQVYTFPEGVPDGILTRFTTNANHV 100
101 VFNGAGNKIAAGSSDFLVKVVDVMDNSQQQTFRGHDAPVLSLSFDPKDIF 150
151 LASASCDGTVRVWNISDQTCAVSWPVLQKSNDVVNAKSICRLAWQPKAGK 200
201 LLAVPVEKSVKLYRRETWSNPFDLSDSSISQTLNIVTWSPCGQYLAAGAI 250
251 NGLIVVWNVETKDCMERVKHEKGYAICGLAWHPTCSRICYTDVEGNLGVL 300
301 ENVCDLSGKVSSNKVSSRVEKDYNDLFDGDDTSSAGDFLNDNAVEIPSFS 350
351 KGIINEDDDNDDIMLAADHDLGDDENSVDVTMLKADLSHKEEGDDDQARS 400
401 IHNLPLIRPQRPFYDGPMPTPRQKPFQSSSTPLHLSHRFMVWNSVGIIRC 450
451 YNDDQDSAIDVEFHDTSIHHATHLLNAFNYTMGTLSHEAILLACESADEL 500
501 ASKLHCLHFSSWDSSKEWMVDMPQNEDIEAICLGLGWAAAATTALLLRLF 550
551 TIGGVQKEVFCLPGPVVSMAGHGEQLCIVYHRGTGFDGDQCLGVQLLELG 600
601 RKKNQVLHGDPLPLTRKSYLTWLGFSAEGTPCYVDSEGCVRMLNRGLGNT 650
651 WTPVCNIREHCKGKSDHYWVVGIHENPQQLRCIPCKGSRFPPTLPRPAVA 700
701 ILSFKLPYCQTSTEKGQMEEQFWHSVLFHNYLDYLAKNGYDYEESIKNQA 750
751 VKEQQELLMKMLALSCKLEREFRCVELADLMTQNAVHLAIKYASRSRKLI 800
801 LAQKLSELAAEKAAELAETQSEEEKEEDFREKLNAGYSHTTTEWSRPRVR 850
851 SQVEDAEDREDTVSEEKPESHNHGQNLFQSANSSDTPALKSGAVFSSSQG 900
901 WVNPFKVVVSSKEPAVSANSTRSANILDSMNKSSRKSTSLNRMENNEKSP 950
951 VIKPLTPKPRSKQASAASYFQKRTPQADKTEEVKENPKSSSSDAPAVCLQ 1000
1001 NSENQRPKTGFQMWLEENRSQILSDNPDISDETDIIKEGMIRFRVLSAEE 1050
1051 RKAWTNKAKGETASDGAEAKKRKRVVSEICETENQEETVKENLDLSKKQK 1100
1101 ALNLPANQKLSAFAFKQ 1117
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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