 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P56199 from www.uniprot.org...
The NucPred score for your sequence is 0.22 (see score help below)
1 MAPRPRARPGVAVACCWLLTVVLRCCVSFNVDVKNSMTFSGPVEDMFGYT 50
51 VQQYENEEGKWVLIGSPLVGQPKNRTGDVYKCPVGRGESLPCVKLDLPVN 100
101 TSIPNVTEVKENMTFGSTLVTNPNGGFLACGPLYAYRCGHLHYTTGICSD 150
151 VSPTFQVVNSIAPVQECSTQLDIVIVLDGSNSIYPWDSVTAFLNDLLERM 200
201 DIGPKQTQVGIVQYGENVTHEFNLNKYSSTEEVLVAAKKIVQRGGRQTMT 250
251 ALGIDTARKEAFTEARGARRGVKKVMVIVTDGESHDNHRLKKVIQDCEDE 300
301 NIQRFSIAILGSYNRGNLSTEKFVEEIKSIASEPTEKHFFNVSDELALVT 350
351 IVKTLGERIFALEATADQSAASFEMEMSQTGFSAHYSQDWVMLGAVGAYD 400
401 WNGTVVMQKASQIIIPRNTTFNVESTKKNEPLASYLGYTVNSATASSGDV 450
451 LYIAGQPRYNHTGQVIIYRMEDGNIKILQTLSGEQIGSYFGSILTTTDID 500
501 KDSNTDILLVGAPMYMGTEKEEQGKVYVYALNQTRFEYQMSLEPIKQTCC 550
551 SSRQHNSCTTENKNEPCGARFGTAIAAVKDLNLDGFNDIVIGAPLEDDHG 600
601 GAVYIYHGSGKTIRKEYAQRIPSGGDGKTLKFFGQSIHGEMDLNGDGLTD 650
651 VTIGGLGGAALFWSRDVAVVKVTMNFEPNKVNIQKKNCHMEGKETVCINA 700
701 TVCFDVKLKSKEDTIYEADLQYRVTLDSLRQISRSFFSGTQERKVQRNIT 750
751 VRKSECTKHSFYMLDKHDFQDSVRITLDFNLTDPENGPVLDDSLPNSVHE 800
801 YIPFAKDCGNKEKCISDLSLHVATTEKDLLIVRSQNDKFNVSLTVKNTKD 850
851 SAYNTRTIVHYSPNLVFSGIEAIQKDSCESNHNITCKVGYPFLRRGEMVT 900
901 FKILFQFNTSYLMENVTIYLSATSDSEEPPETLSDNVVNISIPVKYEVGL 950
951 QFYSSASEYHISIAANETVPEVINSTEDIGNEINIFYLIRKSGSFPMPEL 1000
1001 KLSISFPNMTSNGYPVLYPTGLSSSENANCRPHIFEDPFSINSGKKMTTS 1050
1051 TDHLKRGTILDCNTCKFATITCNLTSSDISQVNVSLILWKPTFIKSYFSS 1100
1101 LNLTIRGELRSENASLVLSSSNQKRELAIQISKDGLPGRVPLWVILLSAF 1150
1151 AGLLLLMLLILALWKIGFFKRPLKKKMEK 1179
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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