 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P29539 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MSKDFSDKKKHTIDRIDQHILRRSQHDNYSNGSSPWMKTNLPPPSPQAHM 50
51 HIQSDLSPTPKRRKLASSSDCENKQFDLSAINKNLYPEDTGSRLMQSLPE 100
101 LSASNSDNVSPVTKSVAFSDRIESSPIYRIPGSSPKPSPSSKPGKSILRN 150
151 RLPSVRTVSDLSYNKLQYTQHKLHNGNIFTSPYKETRVNPRALEYWVSGE 200
201 IHGLVDNESVSEFKEIIEGGLGILRQESEDYVARRFEVYATFNNIIPILT 250
251 TKNVNEVDQKFNILIVNIESIIEICIPHLQIAQDTLLSSSEKKNPFVIRL 300
301 YVQIVRFFSAIMSNFKIVKWLTKRPDLVNKLKVIYRWTTGALRNENSNKI 350
351 IITAQVSFLRDEKFGTFFLSNEEIKPIISTFTEIMEINSHNLIYEKLLLI 400
401 RGFLSKYPKLMIETVTSWLPGEVLPRIIIGDEIYSMKILITSIVVLLELL 450
451 KKCLDFVDEHERIYQCIMLSPVCETIPEKFLSKLPLNSYDSANLDKVTIG 500
501 HLLTQQIKNYIVVKNDNKIAMDLWLSMTGLLYDSGKRVYDLTSESNKVWF 550
551 DLNNLCFINNHPKTRLMSIKVWRIITYCICTKISQKNQEGNKSLLSLLRT 600
601 PFQMTLPYVNDPSAREGIIYHLLGVVYTAFTSNKNLSTDMFELFWDHLIT 650
651 PIYEDYVFKYDSIHLQNVLFTVLHLLIGGKNADVALERKYKKHIHPMSVI 700
701 ASEGVKLKDISSLPPQIIKREYDKIMKVVFQAVEVAISNVNLAHDLILTS 750
751 LKHLPEDRKDQTHLESFSSLILKVTQNNKDTPIFRDFFGAVTSSFVYTFL 800
801 DLFLRKNDSSLVNFNIQISKVGISQGNMTLDLLKDVIRKARNETSEFLII 850
851 EKFLELDDKKTEVYAQNWVGSTLLPPNISFREFQSLANIVNKVPNENSIE 900
901 NFLDLCLKLSFPVNLFTLLHVSMWSNNNFIYFIQSYVSKNENKLNVDLIT 950
951 LLKTSLPGNPELFSGLLPFLRRNKFMDILEYCIHSNPNLLNSIPDLNSDL 1000
1001 LLKLLPRSRASYFAANIKLFKCSEQLTLVRWLLKGQQLEQLNQNFSEIEN 1050
1051 VLQNASDSELEKSEIIRELLHLAMANPIEPLFSGLLNFCIKNNMADHLDE 1100
1101 FCGNMTSEVLFKISPELLLKLLTYKEKPNGKLLAAVIEKIENGDDDYILE 1150
1151 LLEKIIIQKEIQILEKLKEPLLVFFLNPVSSNMQKHKKSTNMLRELVLLY 1200
1201 LTKPLSRSAAKKFFSMLISILPPNPNYQTIDMVNLLIDLIKSHNRKFKDK 1250
1251 RTYNATLKTIGKWIQESGVVHQGDSSKEIEAIPDTKSMYIPCEGSENKLS 1300
1301 NLQRKVDSQDIQVPATQGMKEPPSSIQISSQISAKDSDSISLKNTAIMNS 1350
1351 SQQESHANRSRSIDDETLEEVDNESIREIDQQMKSTQLDKNVANHSNICS 1400
1401 TKSDEVDVTELHESIDTQSSEVNAYQPIEVLTSELKAVTNRSIKTNPDHN 1450
1451 VVNSDNPLKRPSKETPTSENKRSKGHETMVDVLVSEEQAVSPSSDVICTN 1500
1501 IKSIANEESSLALRNSIKVETNCNENSLNVTLDLDQQTITKEDGKGQVEH 1550
1551 VQRQENQESMNKINSKSFTQDNIAQYKSVKKARPNNEGENNDYACNVEQA 1600
1601 SPVRNEVPGDGIQIPSGTILLNSSKQTEKSKVDDLRSDEDEHGTVAQEKH 1650
1651 QVGAINSRNKNNDRMDSTPIQGTEEESREVVMTEEGINVRLEDSGTCELN 1700
1701 KNLKGPLKGDKDANINDDFVPVEENVRDEGFLKSMEHAVSKETGLEEQPE 1750
1751 VADISVLPEIRIPIFNSLKMQGSKSQIKEKLKKRLQRNELMPPDSPPRMT 1800
1801 ENTNINAQNGLDTVPKTIGGKEKHHEIQLGQAHTEADGEPLLGGDGNEDA 1850
1851 TSREATPSLKVHFFSKKSRRLVARLRGFTPGDLNGISVEERRNLRIELLD 1900
1901 FMMRLEYYSNRDNDMN 1916
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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