 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8J1G4 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MLEQAEKLMKRNSSGAMSAPQSKPLARSRSSTMPTTTQKRVRSSQQSEGE 50
51 PEYNIKVYVRCRSRNEREIREKSSVVISTLGNNGREVILTNPGTGSNKTY 100
101 TFDRVFGVESDQESMFNQVARAYINEMIEGYNCTVFAYGQTGTGKTYTMS 150
151 GDITMMGSSEDDPNFVLLSEHAGIIPRVLVELFRELREVSEDYSVKVSFL 200
201 ELYNEKLRDLLVDDKDVSLEDHNFNGMAPPESIRIYDSLKTDRTSPNGYS 250
251 IFVKGMEEMYIRSAQEGLKLLMDGSLKRKVAATKCNDLSSRSHTIFTITT 300
301 NVTKIHPISGEQYVKVGKLNLVDLAGSENINRSGAENKRAQEAGLINKSL 350
351 LTLGRVINALVDHSQHIPYRESKLTRLLQDSLGGKTKTCIIATISPAKIS 400
401 MEETVSTLEYATRAKSIKNTPQVNQLMAKESCIIEYIQEIERLRKELRAS 450
451 HSKEGIYITQEKFETYESNSILVEEQQAKIDNLQEQLRRLKEKFLEQTKL 500
501 IKEKDGQIKELDVANRKYLEQSKDLTIYINGIHSKLEDYEHTMIGIHNNN 550
551 MKLLEDINDNRGNIHEDLLAKVDHIETCNLIISREITSLISIRNVLQAYS 600
601 DRFKTVLGGVFEELQEKLTQVGRTTEESQLDVDLSFVDEKFEEVTDIIKA 650
651 TCENLVRTMDEHVSNMKLETTDLTSSCASLLEKECQALHGKLQKYVESMK 700
701 QELNSTLQEMVRDLDMKASSMLNVVQCTKDGLISHKKELEADLESQKREH 750
751 FDIAQTMEEQLQKIVGKERQNIQESMKASYDFLMKQMVETELRQKNFEES 800
801 IVSKVKGLLSHSNNGMSKMSSYAVGRLYDSAIGGVNSIENTVSSATFSMK 850
851 NDLQEFQMDISPICDSRRFGDEFTAVETRISEAIREELTPKLQDITSKAC 900
901 NLIGLGVQDINQKALGVSDDQRRELRSVINNTNNHADRLRSEIGTLVNYV 950
951 SQEHRDNIMQISQTQDEILQEQIASIGRTFDVLGNINKPDANVRTSVPIE 1000
1001 HELNSAINELPPLYMPQRPLSLCSHGRQLLDEAYSGNENLSPSTGKFSNF 1050
1051 PTPCGDMSAQTPTTPMPVPDQPLTKMPVPQTISSLRSLRRLTMDICEHSA 1100
1101 DMTLGSIHESQKAMDSSRRYTLEPRLFEK 1129
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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