 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q04592 from www.uniprot.org...
The NucPred score for your sequence is 0.76 (see score help below)
1 MDWDWGNRCSRPGRRDLLCVLALLAGCLLPVCRTRVYTNHWAVKIAGGFA 50
51 EADRIASKYGFINVGQIGALKDYYHFYHSRTIKRSVLSSRGTHSFISMEP 100
101 KVEWIQQQVVKKRTKRDYDLSHAQSTYFNDPKWPSMWYMHCSDNTHPCQS 150
151 DMNIEGAWKRGYTGKNIVVTILDDGIERTHPDLMQNYDALASCDVNGNDL 200
201 DPMPRYDASNENKHGTRCAGEVAATANNSHCTVGIAFNAKIGGVRMLDGD 250
251 VTDMVEAKSVSYNPQHVHIYSASWGPDDDGKTVDGPAPLTRQAFENGVRM 300
301 GRRGLGSVFVWASGNGGRSKDHCSCDGYTNSIYTISISSTAESGKKPWYL 350
351 EECSSTLATTYSSGESYDKKIITTDLRQRCTDNHTGTSASAPMAAGIIAL 400
401 ALEANPFLTWRDVQHVIVRTSRAGHLNANDWKTNAAGFKVSHLYGFGLMD 450
451 AEAMVMEAEKWTTVPQQHVCVESTDRQIKTIRPNSAVRSIYKASGCSDNP 500
501 NHHVNYLEHVVVRITITHPRRGDLAIYLTSPSGTRSQLLANRLFDHSMEG 550
551 FKNWEFMTIHCWGERAAGDWVLEVYDTPSQLRNFKTPGKLKEWSLVLYGT 600
601 SVQPYSPTNEFPKVERFRYSRVEDPTDDYGAEDYAGPCDPECSEVGCDGP 650
651 GPDHCSDCLHYYYKLKNNTRICVSSCPPGHYHADKKRCRKCAPNCESCFG 700
701 SHGNQCLSCKYGYFLNEETSSCVTQCPDGSYEDIKKNVCGKCSENCKACI 750
751 GFHNCTECKGGLSLQGSRCSVTCEDGQFFNGHDCQPCHRFCATCSGAGAD 800
801 GCINCTEGYVMEEGRCVQSCSVSYYLDHSSEGGYKSCKRCDNSCLTCNGP 850
851 GFKNCSSCPSGYLLDLGTCQMGAICKDGEYIDDQGHCQTCEASCAKCWGP 900
901 TQEDCISCPVTRVLDDGRCVMNCPSWKFEFKKQCHPCHYTCQGCQGSGPS 950
951 NCTSCRADKHGQERFLYHGECLENCPVGHYPAKGHACLPCPDNCELCYNP 1000
1001 HVCSRCMSGYVIIPPNHTCQKLECRQGEFQDSEYEECMPCEEGCLGCTED 1050
1051 DPGACTSCATGYYMFERHCYKACPEKTFGVKWECRACGTNCGSCDQHECY 1100
1101 WCEEGFFLSGGSCVQDCGPGFHGDQELGECKPCHRACENCTGSGYNQCSS 1150
1151 CQEGLQLWHGTCLWSTWPQVEGKDWNEAVPTEKPSLVRSLLQDRRKWKVQ 1200
1201 IKRDATSQNQPCHSSCKTCNGSLCASCPTGMYLWLQACVPSCPQGTWPSV 1250
1251 TSGSCEKCSEDCVSCSGADLCQQCLSQPDNTLLLHEGRCYHSCPEGFYAK 1300
1301 DGVCEHCSSPCKTCEGNATSCNSCEGDFVLDHGVCWKTCPEKHVAVEGVC 1350
1351 KHCPERCQDCIHEKTCKECMPDFFLYNDMCHRSCPKSFYPDMRQCVPCHK 1400
1401 NCLECNGPKEDDCKVCADTSKALHNGLCLDECPEGTYKEEENDECRDCPE 1450
1451 SCLICSSAWTCLACREGFTVVHDVCTAPKECAAVEYWDEGSHRCQPCHKK 1500
1501 CSRCSGPSEDQCYTCPRETFLLNTTCVKECPEGYHTDKDSQQCVLCHSSC 1550
1551 RTCEGPHSMQCLSCRPGWFQLGKECLLQCRDGYYGESTSGRCEKCDKSCK 1600
1601 SCRGPRPTDCQSCDTFFFLLRSKGQCHRACPEHYYADQHAQTCERCHPTC 1650
1651 DKCSGKEAWSCLSCVWSYHLLKGICIPECIVGEYREGKGENFNCKKCHES 1700
1701 CMECKGPGSKNCTGCSAGLLLDMDDNRCLHCCNASHSRRSQDCCDCQSST 1750
1751 DECILPAREAEFYEHTKTALLVTSGAMLLLLLGAAAVVWRKSRSRPVAKG 1800
1801 RYEKLAEPTVSYSSYRSSYLDEDQVIEYRDRDYDEDDEDDIVYMGQDGTV 1850
1851 YRKFKYGLLDETEDDELEYDDESYSYQ 1877
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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