 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q28247 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MKLRGVSLAAGWFLLALSLWGQPAEAAACYGCSPGSKCDCSGVKGEKGER 50
51 GFPGLEGHPGLPGFPGPEGPPGPRGQKGDDGIRGPPGPKGIRGPPGLPGF 100
101 PGTPGLPGMPGHDGAPGPQGIPGCNGTKGERGFPGSPGFPGLEGPPGPPG 150
151 IPGMKGEPGSIIMSSLPGPKGNPGYPGPPGIQGPAGPTGLPGPIGPPGPP 200
201 GLMGPPGPPGLPGPKGNMGLNFQGPKGEKGEQGLQGPPGPPGQISEQKRP 250
251 IDVEFQKGDQGLPGDRGPPGPPGIRGPPGPPGGMKGEKGEQGEPGKRGKP 300
301 GKDGENGQPGIPGLPGDPGYPGEPGRDGEKGQKGDIGSTGPPGLVIPRPG 350
351 TGVTVGEKGNMGLPGLPGEKGERGFPGIQGPPGLPGPPGTAVMGPPGPPG 400
401 FPGERGQKGDEGPPGISIPGFPGLDGQPGAPGLRGPPGPPGPHISPSDEI 450
451 CETGPPGPPGSPGDRGLQGEQGVKGDKGDTCFNCIGTGVSGPRGQPGLPG 500
501 LPGPPGSLGFPGQKGEKGHAGLTGPKGLTGIPGAPGPPGFPGSKGEPGDV 550
551 LTFPGMKGDKGELGYPGAPGLPGLPGTPGQDGLPGLPGPKGEPGGIAFKG 600
601 ERGPPGNPGLPGLPGNRGPMGPVGFGPPGPVGEKGIQGVAGNPGQPGIPG 650
651 PKGDPGQTITQPGKPGLPGNPGRHGEVGLPGDPGLPGPPGLPGIPGNKGE 700
701 PGIPGIGLPGPPGPKGFPGIQGPPGAPGTPGRIGLEGPSGPPGFPGPKGE 750
751 PGLGLPGPPGPPGLPGFKGTLGPKGDRGFPGPPGLPGRTGLDGLPGPKGD 800
801 VGPKGQPGPMGPPGLPGIGVQGPPGPPGIPGPVGEPGLHGIPGEKGDPGP 850
851 PGFDVLGPPGERGSPGIPGAPGPMGPPGTPGLPGKAGASGFPGAKGEMGM 900
901 MGPPGPPGPLGIPGRSGVPGLKGDNGLQGQPGPPGPEGEKGGKGEPGLPG 950
951 PPGPVDPDLLGSKGEKGDPGLPGIPGVSGPKGYQGLPGDPGQPGLSGQPG 1000
1001 LPGPSGPKGNPGLPGKPGLTGPPGLKGSIGDMGFPGPQGVKGSPGPPGVP 1050
1051 GQPGSPGLPGQKGEKGDPGVSGIGLPGLPGPKGEAGLPGYPGNPGIKGSM 1100
1101 GDTGLPGLPGTPGAKGQPGLPGFPGTPGLPGPKGINGPPGNPGLPGEPGP 1150
1151 VGGGGRPGPPGPPGEKGNPGQDGIPGPAGQKGEPGQPGFGIPGPPGLPGL 1200
1201 SGQKGDGGLPGIPGNPGLPGPKGEPGFQGFPGVQGPPGPPGSPGPALEGP 1250
1251 KGNPGPQGPPGRPGPTGFQGLPGPEGPRGLPGNGGIKGERGNPGQPGQPG 1300
1301 LPGLKGDQGPPGIQGNPGRPGLNGMKGDPGLPGVPGFPGMKGPSGVPGSA 1350
1351 GPEGDPGLVGPPGPPGLPGPSGQSIIIKGDVGPPGIPGQPGLKGLPGLPG 1400
1401 PQGLPGPIGPPGDPGRNGLPGFDGAGGRKGDPGLPGQPGTRGLDGPPGPD 1450
1451 GMQGPPGPPGTSSIAHGFLITRHSQTTDAPQCPHGTVQIYEGFSLLYVQG 1500
1501 NKRAHGQDLGTAGSCLRRFSTMPFMFCNINNVCNFASRNDYSYWLSTPEP 1550
1551 MPMSMEPLKGQSIQPFISRCAVCEAPAVVIAVHSQTIQIPHCPHGWDSLW 1600
1601 IGYSFMMHTSAGAEGSGQALASPGSCLEEFRSAPFIECHGRGTCNYYANS 1650
1651 YSFWLATVDVSDMFSKPQSETLKAGDLRTRISRCQVCMKRT 1691
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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