 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P02719 from www.uniprot.org...
The NucPred score for your sequence is 0.25 (see score help below)
1 MARKFSSARPEMFRRFTPDSLEEIEAFTELKKSCTLEKKEPESTPRIDLE 50
51 AGKPLPMIYGDPPEDLLNIPLEDLDPFYKTQKTFIVISKGNIINRFNAER 100
101 ALYIFSPFNPIRRGAIRVFVNSAFNFFIMFTIFSNCIFMTISNPPAWSKI 150
151 VEYTFTGIYTFEVIVKVLSRGFCIGHFTFLRDPWNWLDFSVVTMTYITEF 200
201 IDLRNVSALRTFRVLRALKTITIFPGLKTIVRALIESMKQMGDVVILTVF 250
251 SLAVFTLAGMQLFMGNLRHKCIRWPISNVTLDYESAYNTTFDFTAYIENE 300
301 ENQYFLDGALDALLCGNNSDAGKCPEGYTCMKAGRNPNYGYTNYDNFAWT 350
351 FLCLFRLMLQDYWENLYQMTLRAAGKSYMVFFIMVIFLGSFYLINLILAV 400
401 VAMAYEEQNQATLAEAQEKEAEFQRAVEQLRIQQEQINDERKASLASQLT 450
451 QNQEAEITDDGDDAIKECNGKAFPLANIREPSSVKLSTEEQRSDSKSMDS 500
501 KHSVDKPSLKHKAASTMSVFTLEDLEAARRPCPPVWYKFAGFVFKWNCCG 550
551 PWVFLKKWVHFVMMDPFTDLFITLCIILNTLFMSIEHHPMNESFQSLLSA 600
601 GNLVFTTIFAAEMVLKIIALDPYYYFQQTWNIFDSIIVSLSLLELGLSNM 650
651 QGMSVLRSLRLLRIFKLAKSWPTLNILIKIICNSVGALGNLTIVLAIIVF 700
701 IFALVGFQLFGKNYKEYVCKISDDCELPRWHMNDFFHSFLIVFRALCGEW 750
751 IETMWDCMEVGGVPMCLAVYMMVIIIGNLVMLNLFLALLLSSFSSDNLSS 800
801 IEEDDEVNSLQVASERISRAKNWVKIFITGTVQALVLWIQGKKPPSDDVV 850
851 GEEGDNEGKKDTLPLNYLDGEKIVDGITNCVESPTLNLPIVKGESEIEEE 900
901 GLVDSSDEEDTNKKKHALNDEDSSVCSTVDYSPSEQDPLAKEEEEEEEEE 950
951 PEELESKDPEACFTEKCIWRFPFLDVDITQGKGKIWWNLRRTCYTIVEHD 1000
1001 YFETFIIFMILLSSGVLAFEDIYIWRRRVIKVILEYADKVFTYVFIVEML 1050
1051 LKWVAYGFKRYFTDAWCWLDFVIVGASIMGITSSLLGYEELGAIKNLRTI 1100
1101 RALRPLRALSRFEGMKVVVRALLGAIPSIMNVLLVCLMFWLIFSIMGVNL 1150
1151 FAGKFYRCINTTTDEILPVEEVNNRSDCMALMYTNEVRWVNLKVNYDNAG 1200
1201 MGYLSLLQVSTFKGWMDIMYAAVDSREVEDQPIYEINVYMYLYFVIFIVF 1250
1251 GAFFTLNLFIGVIIDNFNRQKQKLGGEDLFMTEEQKKYYNAMKKLGSKKA 1300
1301 AKCIPRPSNVVQGVVYDIVTQPFTDIFIMALICINMVAMMVESEDQSQVK 1350
1351 KDILSQINVIFVIIFTVECLLKLLALRQYFFTVGWNVFDFAVVVISIIGL 1400
1401 LLSDIIEKYFVSPTLFRVIRLARIARVLRLIRAAKGIRTLLFALMMSLPA 1450
1451 LFNIGLLLFLIMFIFSIFGMSNFAYVKKQGGVDDIFNFETFGNSMICLFE 1500
1501 ITTSAGWDGLLLPTLNTGPPDCDPDVENPGTDVRGNCGNPGKGITFFCSY 1550
1551 IILSFLVVVNMYIAIILENFGVAQEESSDLLCEDDFVMFDETWHKFDVHG 1600
1601 TQFLDYNDLPRFVNALQEPMRIPNPNRHKLAKMDMYVVMEDKISYLDVLL 1650
1651 AVTQEVLGDTTEMEAMRLSIQAKFKKDNPSPTFFEPVVTTLRRKEEEWAS 1700
1701 VVIQRAFRQYLLMRAVSHASFLSQIKHMNEGPKDGVGSQDSLITQKMNAL 1750
1751 YRGNPELTMPLEQQIKPMLDKPRMPSLSVPETYPIQIPKEVTNEVILHSA 1800
1801 PMVRQNYSYSGAIVVRESIV 1820
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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