 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P35499 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MARPSLCTLVPLGPECLRPFTRESLAAIEQRAVEEEARLQRNKQMEIEEP 50
51 ERKPRSDLEAGKNLPMIYGDPPPEVIGIPLEDLDPYYSNKKTFIVLNKGK 100
101 AIFRFSATPALYLLSPFSVVRRGAIKVLIHALFSMFIMITILTNCVFMTM 150
151 SDPPPWSKNVEYTFTGIYTFESLIKILARGFCVDDFTFLRDPWNWLDFSV 200
201 IMMAYLTEFVDLGNISALRTFRVLRALKTITVIPGLKTIVGALIQSVKKL 250
251 SDVMILTVFCLSVFALVGLQLFMGNLRQKCVRWPPPFNDTNTTWYSNDTW 300
301 YGNDTWYGNEMWYGNDSWYANDTWNSHASWATNDTFDWDAYISDEGNFYF 350
351 LEGSNDALLCGNSSDAGHCPEGYECIKTGRNPNYGYTSYDTFSWAFLALF 400
401 RLMTQDYWENLFQLTLRAAGKTYMIFFVVIIFLGSFYLINLILAVVAMAY 450
451 AEQNEATLAEDKEKEEEFQQMLEKFKKHQEELEKAKAAQALEGGEADGDP 500
501 AHGKDCNGSLDTSQGEKGAPRQSSSGDSGISDAMEELEEAHQKCPPWWYK 550
551 CAHKVLIWNCCAPWLKFKNIIHLIVMDPFVDLGITICIVLNTLFMAMEHY 600
601 PMTEHFDNVLTVGNLVFTGIFTAEMVLKLIAMDPYEYFQQGWNIFDSIIV 650
651 TLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGAL 700
701 GNLTLVLAIIVFIFAVVGMQLFGKSYKECVCKIALDCNLPRWHMHDFFHS 750
751 FLIVFRILCGEWIETMWDCMEVAGQAMCLTVFLMVMVIGNLVVLNLFLAL 800
801 LLSSFSADSLAASDEDGEMNNLQIAIGRIKLGIGFAKAFLLGLLHGKILS 850
851 PKDIMLSLGEADGAGEAGEAGETAPEDEKKEPPEEDLKKDNHILNHMGLA 900
901 DGPPSSLELDHLNFINNPYLTIQVPIASEESDLEMPTEEETDTFSEPEDS 950
951 KKPPQPLYDGNSSVCSTADYKPPEEDPEEQAEENPEGEQPEECFTEACVQ 1000
1001 RWPCLYVDISQGRGKKWWTLRRACFKIVEHNWFETFIVFMILLSSGALAF 1050
1051 EDIYIEQRRVIRTILEYADKVFTYIFIMEMLLKWVAYGFKVYFTNAWCWL 1100
1101 DFLIVDVSIISLVANWLGYSELGPIKSLRTLRALRPLRALSRFEGMRVVV 1150
1151 NALLGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYYCINTTTSERFDI 1200
1201 SEVNNKSECESLMHTGQVRWLNVKVNYDNVGLGYLSLLQVATFKGWMDIM 1250
1251 YAAVDSREKEEQPQYEVNLYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQ 1300
1301 QKKKLGGKDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPQNKIQGMVYDLV 1350
1351 TKQAFDITIMILICLNMVTMMVETDNQSQLKVDILYNINMIFIIIFTGEC 1400
1401 VLKMLALRQYYFTVGWNIFDFVVVILSIVGLALSDLIQKYFVSPTLFRVI 1450
1451 RLARIGRVLRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFG 1500
1501 MSNFAYVKKESGIDDMFNFETFGNSIICLFEITTSAGWDGLLNPILNSGP 1550
1551 PDCDPNLENPGTSVKGDCGNPSIGICFFCSYIIISFLIVVNMYIAIILEN 1600
1601 FNVATEESSEPLGEDDFEMFYETWEKFDPDATQFIAYSRLSDFVDTLQEP 1650
1651 LRIAKPNKIKLITLDLPMVPGDKIHCLDILFALTKEVLGDSGEMDALKQT 1700
1701 MEEKFMAANPSKVSYEPITTTLKRKHEEVCAIKIQRAYRRHLLQRSMKQA 1750
1751 SYMYRHSHDGSGDDAPEKEGLLANTMSKMYGHENGNSSSPSPEEKGEAGD 1800
1801 AGPTMGLMPISPSDTAWPPAPPPGQTVRPGVKESLV 1836
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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