 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P38853 from www.uniprot.org...
The NucPred score for your sequence is 0.64 (see score help below)
1 MAGFSFAKKFTHKKHGKTPSDASISDQSREASLSTPPNEKFFTKQETPQK 50
51 GRQFSQGYHSNVNKTSSPPMFARKQVSESRIQPSAVPPQQRNVSGPSTTL 100
101 HKQLSKQREYTVWNRIKLQNSPFPRYRHVASAYVTDKNQIYVIGGLHDQS 150
151 VYGDTWILTAFDNATRFSTTTIDISEATPPPRVGHAAVLCGNAFVVFGGD 200
201 THKVNKEGLMDDDIYLLNINSYKWTVPAPVGPRPLGRYGHKISIIATTQM 250
251 KTKLYVFGGQFDDTYFNDLAVYDLSSFRRPDSHWEFLKPRTFTPPPITNF 300
301 TMISYDSKLWVFGGDTLQGLVNDVFMYDPAINDWFIIDTTGEKPPPVQEH 350
351 ATVVYNDLMCVVGGKDEHDAYLNSVYFLNLKSRKWFKLPVFTAGIPQGRS 400
401 GHSLTLLKNDKILIMGGDKFDYARVEEYDLHTSDIDMQRGTIVYTLDLAR 450
451 IKDLCPGVMDVPTDTPTPRNGNLDLATPVTPTSHQTKNMNVPISAAPLAS 500
501 APSPAPKDFSDADRLNREVHNRNVSTEHQNQSHPVNSESHLIAEPNILTP 550
551 YVPSESSQTPVMKITSNKPFDTPTIQKEPDLSETMDPTVGNQRIPSSIYG 600
601 DNLTPANQIKNNSPILETLPSNEIKTPQNGNIEEIKHLPDADEKIDSTTT 650
651 FDQEINGDKLGTSSMSKVEEDGNVADEDDEIGVAQMASSPSKDQFKIKHY 700
701 NESSELSQNNTEIDKLSEPVDITIKKSDTAGHDSANHVIDASDEKNVSPM 750
751 GDVPTDTKNEEASVPINRDATTEVVDRALFEKLRSELQSLKELTHEKALE 800
801 AGAHIKELETELWQLKSQKNSGTTKEIDELDSVRLQSKCEILEADNHSLE 850
851 DKVNELEELVNSKFLDIENLNEVIQFQNEKIKSLELEPNYKEKLEELQIE 900
901 HENLSRENERLKNESKQHNEDIINNVANYSSQLGSLISHWKENRANSSFL 950
951 ESSSSLISVSDENGEKTVGEPYGDQSRHHRVVINKLTNRLDDLLERSQEL 1000
1001 TISKEKLSSEYHALKMEHSSLSQDVLVKENEIKKIQNDYKESISSMDSAS 1050
1051 KALMVSQRELEKYKSLNKKLIDELDELKFKNGVCSENFENGLRSTEESSN 1100
1101 NVKNSNSIRENQFNIKINDLKAELFITNQERDDLKSEVLELKKRLLNLEN 1150
1151 NTKQVNEDADSDLL 1164
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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