 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9VPQ6 from www.uniprot.org...
The NucPred score for your sequence is 0.82 (see score help below)
1 MLSSNTRGDCSDTAEEMTVDSRDSKDLSAQDIGEQKQQQMEDQLEDQLND 50
51 SRDPQNNNNNIDDDADEDAEFEEPEKANPQQDQDLGETEMEQEHDLQQED 100
101 LQQELPANSPSTPPRSPSSPQLIPKLEQPATPPSEPEASPCPSPSPCPTP 150
151 KYPKVRLNALLASDPALKPDAKELTLPDSRLLAPPPLVKPDTQAQPEVAE 200
201 PLLKPARFMCLPCGIAFSSPSTLEAHQAYYCSHRIKDTDEAGSDKSGAGG 250
251 SGATAGDAAGLTGGSTEPPAKMARTGKQYGCTQCSYSADKKVSLNRHMRM 300
301 HQTSPAAPTLAGLPSLLQNGIAPPGVTPNPMEDSSSQQTDRYCSHCDIRF 350
351 NNIKTYRAHKQHYCSSRRPEGQLTPKPDASPGAGSGPGSAGGSIGVSAQA 400
401 ATPGKLSPQARNKTPTPAMVAVAAAAAAAAASLQATPHSHPPFLALPTHP 450
451 IIIVPCSLIRAASFIPGPLPTPNSGIVNPETTCFTVDNGTIKPLATALVG 500
501 ATLEPERPSAPSSAAEATEAKSSPPEPKRKEAGLTRESAPLDLSLRRSPI 550
551 TLNSLSLRQRQLRNALLDVEEVLLAGVGTGKENVETPRGGGSVTPEQIVC 600
601 APSLPSSPSMSPSPKRRAISPRSSGAGSASSMSPPGLNVAVPHLLDMRSM 650
651 LPADFGLSESLLAKTNPELALKLAAAAAAAAVAGSSGAAAFPPASLPAQT 700
701 SSGNPGSGGSAGGAQQPQIYVKKGVSKCMECNIVFCKYENYLAHKQHYCS 750
751 ARSQEGASEVDVKSAVSPSIAGAGGLGAGAAEAASSVETTPVAYQQLICA 800
801 ACGIKYTSLDNLRAHQNYYCPKGGAVAAPAATPTDPGQLGMPKEKCGKCK 850
851 TLHEIGLPCPPPVANPLAAPTVNPQPATNSLNKCPVCGVVSPTAALAKKH 900
901 MEMHGTVKAYRCSICQYKGNTLRGMRTHIRTHFDKKTSDVNEELYMTCIF 950
951 EEDASALSQELVTPTGASTTTGHDSMDHPSQMFNCDYCNYVSTYKGNVLR 1000
1001 HMKLMHPHVAINSPSISPDTRDQDVTSNPTTNQHSNSDVSNGEAPSFHIK 1050
1051 SEPLDPPPTVNLVHENNNSPIATPHIKAEPIEVGADAAPGGLVPPMTSPL 1100
1101 GNSSSVAAAAAAAAEVMKKYCSTCDISFNYVKTYLAHKQFYCKNKPIRPE 1150
1151 ASDSPSPNHLGGGVAVGLGIGGLVGGHGQQKNKENLQEAAI 1191
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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