 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P20357 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MADERKDEGKAPHWTSASLTEAAAHPHSPEMKDQGGAGEGLSRNANGFPY 50
51 REEEEGAFGEHRSQGTYSDTKENGINGELTSADRETAEEVSARIVQVVTA 100
101 EAVAVLKGEQEKEAQHKDQPAALPLAAEETANLPPSPPPSPASEQTATVE 150
151 EDLLTASKMEFPEQEKFPSSFAEPLDKGEMEFKMPSKPGEDFEHAALVPD 200
201 TSKTPQDKKDLQGMEGEKLPPVPFAQTFGTNLEDRKQSTEPSIVMPSIGL 250
251 SAEPPAPKEPKDWFIEMPTESKKDEWGLAAPISPGPLTPMREKDVLEDIP 300
301 RWEGKQFDSPMPSPFHGGSFTLPLDTMKNERVSEGPRPFAPVFFQSDDKV 350
351 SLQDPSALATSKESSKDEEPLKDKADKVADVSISEVTTLLGNVHSPVVEG 400
401 YVGENISGEVKVTTDQEKKETSAPSVQEPTLTETEPQTKLDEKSTVSIEE 450
451 AVAKKEESFKLRDDKTGVIQTSTEQSFSKEDQKGQEHTIDELKQDSFPIS 500
501 LEQAVTDAAMTSKTLGKVTSEPEAVSERREIQGLFEEKTADKNKLEGAGS 550
551 ATIAEVEMPFYEDKSGMSKYFETSALKEDMTRSTELGSDYYELSDSRGSA 600
601 QESLDTISPKNQHDEKELQAKASQPSPPAQEAGYSTLAQSYTPDHPSELP 650
651 EEPSSPQERMFTIDPKVYGEKRDLHSKNKDDLTLSRSLGLGGRSAIEQRS 700
701 MSINLPMSCLDSIALGFNFGRGHDLSPLASDILTNTSGSMDEGDDYLPPT 750
751 TPAVEKMPCFPIESKEEEDKAEQAKVTGGQTIQVETSSESPFPAKEYYKN 800
801 GTVMAPDLPEMLDLAGTRSRLASVSADAEVARRKSVPSEAMLAESSTSLP 850
851 PVADESPVTVKPDSQLEDMGYCVFNKYTVPLPSPVQDSENLSGESGSFYE 900
901 GTDDKVRRDLATDLSLIEVKLAAAGRVKDEFTAEKEASPPTSADKSRLSR 950
951 EFDHDRKANDKLDTVLEKSEEHIDSKEHAKESEEMGGKVELFGLGITYDQ 1000
1001 ASTKELITTKDTSPEKTEKGLSSVPEVAEVEPTTKADQGLDFAATKAEPS 1050
1051 QLDIKVSDFGQMASGMNVDAGKAIELKFEVAQELTLSSEAPQEADSFMGV 1100
1101 ESGHIKEGGKVNETEVKEKVTKPDLVHQEAVDKEESYESSGEHESLTMES 1150
1151 LKPDEGKKETSPETSLIQDEVALKLSVEIPCPPPVSEADLSTDEKGEVQM 1200
1201 EFIQLPKEESTETPDIPAIPSDVTQPQPEAIVSEPAEVPSEEEEIEAGGE 1250
1251 YDKLLFRSDTLQISDLLVSESREEFVETCPGELKGVVESVVTIEDDFITV 1300
1301 VQTTTDEGESGSHSVRFAAPAQPEEERRPRPHDEELEIEMAAEAQAEPKD 1350
1351 GSPDAPATPEKEEVAFSEYKTETYDDYKDETTIDDSIMDADSLWVDTQDD 1400
1401 DRSILTEQLETIPKEERAEKDARRPSLEKHRKEKPFKTGRGRISTPERKV 1450
1451 AKKEPSTVSRDEVRRKKAVYKKAELAKKSEVQAHSPSRKLILKPAIKYTR 1500
1501 PTHLSCVKRKTTAASGDLAQAPGAFKQAKDKVTDGISKSPEKRSSLPRPS 1550
1551 SILPPRRGVSGDREENSFSLNSSISSARRTTRSEPIRRAGKSGTSTPTTP 1600
1601 GSTAITPGTPPSYSSRTPGTPGTPSYPRTPGTPKSGILVPSEKKVAIIRT 1650
1651 PPKSPATPKQLRLINQPLPDLKNVKSKIGSTDNIKYQPKGGQVQIVTKKI 1700
1701 DLSHVTSKCGSLKNIRHRPGGGRVKIESVKLDFKEKAQAKVGSLDNAHHV 1750
1751 PGGGNVKIDSQKLNFREHAKARVDHGAEIITQSPSRSSVASPRRLSNVSS 1800
1801 SGSINLLESPQLATLAEDVTAALAKQGL 1828
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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