 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P87498 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MRGLISALVLTLVGSQHLNYQPDFGENKVYTYNYESILFSGIPEKGLART 50
51 GIRIRSEVEISGIGPKLCLIRIHSIEAAEYNGIWPTSSFSRSLKLTQALT 100
101 GQLSIPIKFEYSNGHVGNLMAPDSVSDDGLNIYRGILNILELSLKKMQHS 150
151 YSIQEAGIGGICNTTYAIQENKKANLVDVTKSKDLNSCEEKVQVVTGSAY 200
201 TQPCQTCQQRNKNSRATATYNYKIKYTHNEAVITQAEVEEVHQFTPFHEI 250
251 TGGNAIVEARQKLALIEVQKQVAEVPPKEFQKRGSLQYQFGSELLQLPVH 300
301 LFKIKDVERQIEERLQDLVETTYEQLPSDAPAKALKLMHLLRAANEENYE 350
351 SVWKQFSSRPAYRRYLLDLLPAAASHRSLRFLRHKMERQELTNWEIAQTV 400
401 LVALHSSSPTQEVMEEATLIVKKHCPRSSSVLRKVCLLSYASLCHKRCSS 450
451 PYSCSECLQVFHVFAGEALGKSNIEEVLLALKALGNVGHPASIKHIKKFL 500
501 PGYAAGASELPLKVHETAVMALKSIGMRDPQMVQAITLEIFLNHKIHPRI 550
551 RMLAAVVLLETKPGLPILMILVDAVLKEPSMQVASFIYSHLRALGRSTAP 600
601 DLQMMASACRMAVRALSPKFDRSGYQFSKVFRFSMFKEFLMSGLAAKYFV 650
651 LNNAGSLIPTMAVSQLRTHFLGRVADPIEVGIAAEGLQEMFVRGYSPDKD 700
701 WETNYDFREILKKLSDWKALPRDKPFASGYLKMFGQELLFGRLDKDTLQN 750
751 VLQVWYGPDEKIPSIRRLISSLQTGIGRQWTKALLLSEIRCIVPTCVGFP 800
801 METSFYYSSVTKVAGNVQAQITPSPRSDFRLTELLNSNVRLRSKMSLSMA 850
851 KHMTFVIGINTNMIQAGLEAHTKVNAHVPVNVVATIQMKEKSIKAEIPPC 900
901 KEETNLIIVSSKTFAVTRNIEDLAASKMTPVLLPEAVPDIMKMSFDSDSA 950
951 SGETDNIRDRQSVEDVSSGNSFSFGHPSSGKEPFIQSMCSNASTFGVQVC 1000
1001 IEKKSVHAAFIRNVPLYNAIGEHALRMSFKPVYSDVPIEKIQVTIQAGDQ 1050
1051 APTKMVRLVTFEDPERQESSRKEVMKRVKKILDDTDNQATRNSRSSSSSA 1100
1101 SSISESSESTTSTPSSSDSDNRASQGDPQINLKSRQSKANEKKFYPFGDS 1150
1151 SSSGSSSSSSSSSSSSSDSSSSSRSSSSSDSSSSSSSSSSSSSSKSKSSS 1200
1201 RSSKSNRSSSSSNSKDSSSSSSKSNSKGSSSSSSKASGTRQKAKKQSKTT 1250
1251 SFPHASAAEGERSVHEQKQETQSSSSSSSRASSNSRSTSSSTSSSSESSG 1300
1301 VSHRQWKQDREAETKRVKSQFNSHSSYDIPNEWETYLPKVYRLRFRSAHT 1350
1351 HWHSGHRTSSSSSSSSSESGSSHSNSSSSDSSSRRSHMSDSSSSSSSHRH 1400
1401 GEKAAHSSRRSPTSRAASAHHRPGSSLTRERNFLGDVIPPGITIVAQAVR 1450
1451 SDNRNQGYQATAYVRSDAAKVDVQLVVVQLAETNWKACADAVILPLKAQA 1500
1501 RMRWGKECRDYRIAALATTGQMARKLAVQLKVQWGIIPSWIKKTSTALMR 1550
1551 YVPGVALVLGFSEAHQRNPSRELIVRAVATSPRSIDTVIKVPGVTLYYQG 1600
1601 LRVPFTLALGASSSSYETRDITAWNFLPEIASQIAQEDQSTCEVSKGDFK 1650
1651 TFDRMSFTCSFNKSCNVVVAQDCTEHPKFIITTRKVDHQSLSREVHINTS 1700
1701 SANITICPAADSSLLVTCNKESVLSDSGVSEYEKDNIKIYKNGKTVIVEA 1750
1751 PIHGLKNVNFDGEILKVTVASWMRGKTCGVCGNNDREKHNELLMPNHKLA 1800
1801 HSCSAFVHSWVLLEETCSGGCKLQRRYVKLNRNPTIDGEESTCYSVDPVL 1850
1851 KCMKDCTPIEKTSVKVGFHCFPKATAVSLLEWQRSSDKKSASEDVVESVD 1900
1901 ADIDCTCTGDCS 1912
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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