 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9H7D0 from www.uniprot.org...
The NucPred score for your sequence is 0.89 (see score help below)
1 MARWIPTKRQKYGVAIYNYNASQDVELSLQIGDTVHILEMYEGWYRGYTL 50
51 QNKSKKGIFPETYIHLKEATVEDLGQHETVIPGELPLVQELTSTLREWAV 100
101 IWRKLYVNNKLTLFRQLQQMTYSLIEWRSQILSGTLPKDELAELKKKVTA 150
151 KIDHGNRMLGLDLVVRDDNGNILDPDETSTIALFKAHEVASKRIEEKIQE 200
201 EKSILQNLDLRGQSIFSTIHTYGLYVNFKNFVCNIGEDAELFMALYDPDQ 250
251 STFISENYLIRWGSNGMPKEIEKLNNLQAVFTDLSSMDLIRPRVSLVCQI 300
301 VRVGHMELKEGKKHTCGLRRPFGVAVMDITDIIHGKVDDEEKQHFIPFQQ 350
351 IAMETYIRQRQLIMSPLITSHVIGENEPLTSVLNKVIAAKEVNHKGQGLW 400
401 VSLKLLPGDLTQVQKNFSHLVDRSTAIARKMGFPEIILPGDVRNDIYVTL 450
451 IHGEFDKGKKKTPKNVEVTMSVHDEEGKLLEKAIHPGAGYEGISEYKSVV 500
501 YYQVKQPCWYETVKVSIAIEEVTRCHIRFTFRHRSSQETRDKSERAFGVA 550
551 FVKLMNPDGTTLQDGRHDLVVYKGDNKKMEDAKFYLTLPGTKMEMEEKEL 600
601 QASKNLVTFTPSKDSTKDSFQIATLICSTKLTQNVDLLGLLNWRSNSQNI 650
651 KHNLKKLMEVDGGEIVKFLQDTLDALFNIMMEMSDSETYDFLVFDALVFI 700
701 ISLIGDIKFQHFNPVLETYIYKHFSATLAYVKLSKVLNFYVANADDSSKT 750
751 ELLFAALKALKYLFRFIIQSRVLYLRFYGQSKDGDEFNNSIRQLFLAFNM 800
801 LMDRPLEEAVKIKGAALKYLPSIINDVKLVFDPVELSVLFCKFIQSIPDN 850
851 QLVRQKLNCMTKIVESTLFRQSECREVLLPLLTDQLSGQLDDNSNKPDHE 900
901 ASSQLLSNILEVLDRKDVGATAVHIQLIMERLLRRINRTVIGMNRQSPHI 950
951 GSFVACMIALLQQMDDSHYSHYISTFKTRQDIIDFLMETFIMFKDLIGKN 1000
1001 VYAKDWMVMNMTQNRVFLRAINQFAEVLTRFFMDQASFELQLWNNYFHLA 1050
1051 VAFLTHESLQLETFSQAKRNKIVKKYGDMRKEIGFRIRDMWYNLGPHKIK 1100
1101 FIPSMVGPILEVTLTPEVELRKATIPIFFDMMQCEFNFSGNGNFHMFENE 1150
1151 LITKLDQEVEGGRGDEQYKVLLEKLLLEHCRKHKYLSSSGEVFALLVSSL 1200
1201 LENLLDYRTIIMQDESKENRMSCTVNVLNFYKEKKREDIYIRYLYKLRDL 1250
1251 HRDCENYTEAAYTLLLHAELLQWSDKPCVPHLLQKDSYYVYTQQELKEKL 1300
1301 YQEIISYFDKGKMWEKAIKLSKELAETYESKVFDYEGLGNLLKKRASFYE 1350
1351 NIIKAMRPQPEYFAVGYYGQGFPSFLRNKIFIYRGKEYERREDFSLRLLT 1400
1401 QFPNAEKMTSTTPPGEDIKSSPKQYMQCFTVKPVMSLPPSYKDKPVPEQI 1450
1451 LNYYRANEVQQFRYSRPFRKGEKDPDNEFATMWIERTTYTTAYTFPGILK 1500
1501 WFEVKQISTEEISPLENAIETMELTNERISNCVQQHAWDRSLSVHPLSML 1550
1551 LSGIVDPAVMGGFSNYEKAFFTEKYLQEHPEDQEKVELLKRLIALQMPLL 1600
1601 TEGIRIHGEKLTEQLKPLHERLSSCFRELKEKVEKHYGVITLPPNLTERK 1650
1651 QSRTGSIVLPYIMSSTLRRLSITSVTSSVVSTSSNSSDNAPSRPGSDGSI 1700
1701 LEPLLERRASSGARVEDLSLREENSENRISKFKRKDWSLSKSQVIAEKAP 1750
1751 EPDLMSPTRKAQRPKSLQLMDNRLSPFHGSSPPQSTPLSPPPLTPKATRT 1800
1801 LSSPSLQTDGIAATPVPPPPPPKSKPYEGSQRNSTELAPPLPVRREAKAP 1850
1851 PPPPPKARKSGIPTSEPGSQ 1870
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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