 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q04952 from www.uniprot.org...
The NucPred score for your sequence is 0.59 (see score help below)
1 MDFMSPKFSLTDVEYPAWCQDDEVPITMQEIREIFVELMDKFGFQKSSME 50
51 NMYQHLMGQLDSRASRTGAQNALVSLHVSYIGGEHANYRKWYFAAQLDLD 100
101 EEIGFQNMRLHGKARQRNVKMAKKRGVSIKEQIKQWNEKEQEFINNHPKI 150
151 TLTQEQLEDQTNLKSADYKWKLKMKKLTPENMIRQLALYLLCWGEANQVR 200
201 FAPECLCFIFKCALDYDISTSSSEKTVKSPEYSYLNDVITPLYEFLRGQV 250
251 YKKDAKGNWKRREKDHKNIIGYDDINQLFWYPEGFERIILNNGERLVDKP 300
301 LEERYLYFKDVAWSKVFYKTYRETRSWKHCFTNFNRFWIIHFAPFWFFTT 350
351 FNSPTLYTKNYIQLLNNQPTPQVRLSVIAFGGTIACLVQILATVFEWGFV 400
401 PREWPGAQHLSSRMIGLLFCLAINLGPSVYVLGFFEWDVHSKSAYIVSIV 450
451 QLIIAFLTTFFFAVRPLGGLFRPYLNKDKKHRRYISSQTFTASFPKLTGR 500
501 SKWFSYGLWVFVYLAKYIESYFFLTLSLRDPIRVLSIMDLSRCQGEYLLG 550
551 PILCKWQAKITLVLMLLSDLGLFFLDTYLWYIICNCIFSIVLSFSLGTSI 600
601 LTPWKNVYSRLPKRIYSKILATSEMDVKFKAKILISQVWNAIVISMYREH 650
651 LLSIEHLQRLLFQQVDSLMGDTRTLKSPTFFVAQDDSTFKSMEFFPSNSE 700
701 AKRRISFFAQSLATPISEPVPVDCMPTFTVLVPHYSEKILLGLKEIIREE 750
751 SPKSKITVLEYLKHLHPTEWECFVKDTKLLSMEKSFLKEAESSHDEDRLE 800
801 IPDALYDPRSSPLSDHTESRKLPTEDDLIKEKINDLPFSYFGFNSSEPSY 850
851 TLRTRIWASLRTQTLYRTLSGFMNYSKAIKLLYRIENPSLVSLYRGNNEA 900
901 LENDLENMASRKFRMVVAMQRYAKFNKDEVEATELLLRAYPNMFISYLLE 950
951 ELEQNESEKTYYSCLTNGYAEFDEESGLRKPIFKIRLSGNPILGDGKSDN 1000
1001 QNHSIIFYRGEYIQVIDANQDNYLEECLKIRSVLSEFEELELNPTIPYIP 1050
1051 GIEYEEEPPPIAIVGSREYIFSENIGVLGDIAAGKEQTFGTLFARTLAEI 1100
1101 GGKLHYGHPDFLNGIFMTTRGGLSKAQRGLHLNEDIYAGMNAICRGGKIK 1150
1151 HSDYYQCGKGRDLGFGSILNFTTKIGAGMGEQLLSREYYYLGTQLPMDRF 1200
1201 LSFFYAHPGFHLNNLFISFSVQLFFVLLLNLGALNHEIIACFYDKDAPIT 1250
1251 NLETPVGCYNIQPALHWVSIFVLSIFIVFFIAFAPLLIQEVLEKGIWRAA 1300
1301 SRFLHHLLSMAPLFEVFVCQVYSNSLLMDLTFGGAKYISTGRGFAITRLD 1350
1351 FFTLYSRFVNISIYSGFQVFFMLLFAIISMWQPALLWFWITVISMCFAPF 1400
1401 IFNPHQFAFMDFFIDYKTFIHWLFSGNTKYQKESWANFVKSSRSRFTGYK 1450
1451 SKTVDDISEDSGHDSKKARFWNVFFAELFLPFCVFLFNFTAFSFINAQTG 1500
1501 VSDSTPTSAVFRLLLVTFLPIFLNSIVLFLLFWVSLFVVPGLSYCCKDAG 1550
1551 AVIAFIAHTFSVLVYLLDFELMWFLQGWNFTRTLILLITCINMHLILFKV 1600
1601 FTTIFLTREYKNNKAHLAWWNGKWYNTGMGWSIILQPIREYFVKIMESSY 1650
1651 FAADFFLGHFLLFIQTPIILLPFIDYWHTMVLFWMNPRSIIAHKRILTRK 1700
1701 QRALRSRIVSKYFSLYFVMLGVLLFMLIAPFFAGDFVSSPQELLEGTLFE 1750
1751 GIFQPNNQNNNDTGPNAPSTILTTTPTLPTFRTVA 1785
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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