 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5XPJ6 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MALTRYQIRNEYGLADKELYQSADKEDPEALLEAASMAGLVGVLRQLGDL 50
51 SEFAAEVFHCLHEQLMTTAARGHGLAMRLQHLEADFPSVEIPILSQTDHS 100
101 TFFYEPGLEWHSDLQTKEDLISPRNLPRCIMDSYEECHGPPQLFLLDKFD 150
151 VAGSGSCLKRYSDPSLLKTHTTSAVVATSKLGKDKRLRQSKKKGSHTTIK 200
201 ETPEDSRTSHAKLHQLFFLEHVENGHRNPEFHVKLKRRQLNGPPINSSSG 250
251 ASYMEKFLKNSSPYCERVHGTMDQSSPAMETEVTVCSEQEDLPIPSLVYS 300
301 NSGGTRKYNEMEIESIAGHEILEIPFVPHEITVNEKSPVVCLESSSSVNL 350
351 CCKTNNDADSPASTESEVKEAGSDDKAGCDHGFPGFGQPQICTNAEVNQT 400
401 EVLTQFSNVLRHSPEEGESSLLCTDIQRASPESKPHKAEEAAVDLDESFS 450
451 QMTPDIDSAGMGTLEILQTPFSLSCYESPANLPEDSGSHLELQSNKANAE 500
501 ACEVFEVRRDPMLNISPETHLLKVTQVPQDAYEGGTNDVHSQHVFSVETA 550
551 SEISVSALVEDQFSSITNQEIEALESEDISSEAGHFIPDTKKSLNETSVA 600
601 LESDFLLPNHYISTFDNFEDLSLSADAQDYAAPKEDETNSQDGSSMNPAQ 650
651 SKHISTSEISSENGTLMSDTPRDLHTGYGSLSASSCLEDGLANPDLAEIS 700
701 SYSGQEDPQTMSIVSDDSSDPEVPIPDGTCFAGDVDHDNQTGLNNKAIET 750
751 VPQKELETISDPQESLLGTEECLSSEYCLQIQNQRQESPSETGSANSRTS 800
801 SDESPPTQNGSVGVQSSPLDVFPSSITEIEALHAPYQEIFTSLNDHISES 850
851 VLSKGLTDEEDFLNVSPESILPLSTSLHETPQANPEITPPLPPLPPTQWW 900
901 MGKLVESTEMPSLAGSGNNSFNIQRDENTQNGSVQANEAQYPSEVSVTDG 950
951 ENHNFHIYTEESKATEEQSPSGVNGTSDTYMESKHKCLNRTPEDSFSLAE 1000
1001 SAQGLEADWRTEAMALEWFSQNLREHNNPHPAKLEEEEPQVDHPLEKPGQ 1050
1051 TKFRQTLRDNNSYNQNQKAGKLKRDEDTLVIGIDRSMLRKVSEGNRTHVG 1100
1101 ARVDENDSLLEIIRSKSFNLRPADASGRPNFQVAVPKTNLKVAAILEKAN 1150
1151 TLRQAMAGSDDEHDSDSWSE 1170
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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