 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P11137 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 MADERKDEAKAPHWTSAPLTEASAHSHPPEIKDQGGAGEGLVRSANGFPY 50
51 REDEEGAFGEHGSQGTYSNTKENGINGELTSADRETAEEVSARIVQVVTA 100
101 EAVAVLKGEQEKEAQHKDQTAALPLAAEETANLPPSPPPSPASEQTVTVE 150
151 EDLLTASKMEFHDQQELTPSTAEPSDQKEKESEKQSKPGEDLKHAALVSQ 200
201 PETTKTYPDKKDMQGTEEEKAPLALFGHTLVASLEDMKQKTEPSLVVPGI 250
251 DLPKEPPTPKEQKDWFIEMPTEAKKDEWGLVAPISPGPLTPMREKDVFDD 300
301 IPKWEGKQFDSPMPSPFQGGSFTLPLDVMKNEIVTETSPFAPAFLQPDDK 350
351 KSLQQTSGPATAKDSFKIEEPHEAKPDKMAEAPPSEAMTLPKDAHIPVVE 400
401 EHVMGKVLEEEKEAINQETVQQRDTFTPSGQEPILTEKETELKLEEKTTI 450
451 SDKEAVPKESKPPKPADEEIGIIQTSTEHTFSEQKDQEPTTDMLKQDSFP 500
501 VSLEQAVTDSAMTSKTLEKAMTEPSALIEKSSIQELFEMRVDDKDKIEGV 550
551 GAATSAELDMPFYEDKSGMSKYFETSALKEEATKSIEPGSDYYELSDTRE 600
601 SVHESIDTMSPMHKNGDKEFQTGKESQPSPPAQEAGYSTLAQSYPSDLPE 650
651 EPSSPQERMFTIDPKVYGEKRDLHSKNKDDLTLSRSLGLGGRSAIEQRSM 700
701 SINLPMSCLDSIALGFNFGRGHDLSPLASDILTNTSGSMDEGDDYLPATT 750
751 PALEKAPCFPVESKEEEQIEKVKATGEESTQAEISCESPFLAKDFYKNGT 800
801 VMAPDLPEMLDLAGTRSRLASVSADAEVARRKSVPSETVVEDSRTGLPPV 850
851 TDENHVIVKTDSQLEDLGYCVFNKYTVPLPSPVQDSENLSGESGTFYEGT 900
901 DDKVRRDLATDLSLIEVKLAAAGRVKDEFSVDKEASAHISGDKSGLSKEF 950
951 DQEKKANDRLDTVLEKSEEHADSKEHAKKTEEAGDEIETFGLGVTYEQAL 1000
1001 AKDLSIPTDASSEKAEKGLSSVPEIAEVEPSKKVEQGLDFAVQGQLDVKI 1050
1051 SDFGQMASGLNIDDRRATELKLEATQDMTPSSKAPQEADAFMGVESGHMK 1100
1101 EGTKVSETEVKEKVAKPDLVHQEAVDKEESYESSGEHESLTMESLKADEG 1150
1151 KKETSPESSLIQDEIAVKLSVEIPCPPAVSEADLATDERADVQMEFIQGP 1200
1201 KEESKETPDISITPSDVAEPLHETIVSEPAEIQSEEEEIEAQGEYDKLLF 1250
1251 RSDTLQITDLGVSGAREEFVETCPSEHKGVIESVVTIEDDFITVVQTTTD 1300
1301 EGESGSHSVRFAALEQPEVERRPSPHDEEEFEVEEAAEAQAEPKDGSPEA 1350
1351 PASPEREEVALSEYKTETYDDYKDETTIDDSIMDADSLWVDTQDDDRSIM 1400
1401 TEQLETIPKEEKAEKEARRSSLEKHRKEKPFKTGRGRISTPERKVAKKEP 1450
1451 STVSRDEVRRKKAVYKKAELAKKTEVQAHSPSRKFILKPAIKYTRPTHLS 1500
1501 CVKRKTTAAGGESALAPSVFKQAKDKVSDGVTKSPEKRSSLPRPSSILPP 1550
1551 RRGVSGDRDENSFSLNSSISSSARRTTRSEPIRRAGKSGTSTPTTPGSTA 1600
1601 ITPGTPPSYSSRTPGTPGTPSYPRTPHTPGTPKSAILVPSEKKVAIIRTP 1650
1651 PKSPATPKQLRLINQPLPDLKNVKSKIGSTDNIKYQPKGGQVQIVTKKID 1700
1701 LSHVTSKCGSLKNIRHRPGGGRVKIESVKLDFKEKAQAKVGSLDNAHHVP 1750
1751 GGGNVKIDSQKLNFREHAKARVDHGAEIITQSPGRSSVASPRRLSNVSSS 1800
1801 GSINLLESPQLATLAEDVTAALAKQGL 1827
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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