SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching O75093 from www.uniprot.org...

The NucPred score for your sequence is 0.77 (see score help below)

   1  MALTPGWGSSAGPVRPELWLLLWAAAWRLGASACPALCTCTGTTVDCHGT    50
51 GLQAIPKNIPRNTERLELNGNNITRIHKNDFAGLKQLRVLQLMENQIGAV 100
101 ERGAFDDMKELERLRLNRNQLHMLPELLFQNNQALSRLDLSENAIQAIPR 150
151 KAFRGATDLKNLQLDKNQISCIEEGAFRALRGLEVLTLNNNNITTIPVSS 200
201 FNHMPKLRTFRLHSNHLFCDCHLAWLSQWLRQRPTIGLFTQCSGPASLRG 250
251 LNVAEVQKSEFSCSGQGEAGRVPTCTLSSGSCPAMCTCSNGIVDCRGKGL 300
301 TAIPANLPETMTEIRLELNGIKSIPPGAFSPYRKLRRIDLSNNQIAEIAP 350
351 DAFQGLRSLNSLVLYGNKITDLPRGVFGGLYTLQLLLLNANKINCIRPDA 400
401 FQDLQNLSLLSLYDNKIQSLAKGTFTSLRAIQTLHLAQNPFICDCNLKWL 450
451 ADFLRTNPIETSGARCASPRRLANKRIGQIKSKKFRCSAKEQYFIPGTED 500
501 YQLNSECNSDVVCPHKCRCEANVVECSSLKLTKIPERIPQSTAELRLNNN 550
551 EISILEATGMFKKLTHLKKINLSNNKVSEIEDGAFEGAASVSELHLTANQ 600
601 LESIRSGMFRGLDGLRTLMLRNNRISCIHNDSFTGLRNVRLLSLYDNQIT 650
651 TVSPGAFDTLQSLSTLNLLANPFNCNCQLAWLGGWLRKRKIVTGNPRCQN 700
701 PDFLRQIPLQDVAFPDFRCEEGQEEGGCLPRPQCPQECACLDTVVRCSNK 750
751 HLRALPKGIPKNVTELYLDGNQFTLVPGQLSTFKYLQLVDLSNNKISSLS 800
801 NSSFTNMSQLTTLILSYNALQCIPPLAFQGLRSLRLLSLHGNDISTLQEG 850
851 IFADVTSLSHLAIGANPLYCDCHLRWLSSWVKTGYKEPGIARCAGPQDME 900
901 GKLLLTTPAKKFECQGPPTLAVQAKCDLCLSSPCQNQGTCHNDPLEVYRC 950
951 ACPSGYKGRDCEVSLDSCSSGPCENGGTCHAQEGEDAPFTCSCPTGFEGP 1000
1001 TCGVNTDDCVDHACANGGVCVDGVGNYTCQCPLQYEGKACEQLVDLCSPD 1050
1051 LNPCQHEAQCVGTPDGPRCECMPGYAGDNCSENQDDCRDHRCQNGAQCMD 1100
1101 EVNSYSCLCAEGYSGQLCEIPPHLPAPKSPCEGTECQNGANCVDQGNRPV 1150
1151 CQCLPGFGGPECEKLLSVNFVDRDTYLQFTDLQNWPRANITLQVSTAEDN 1200
1201 GILLYNGDNDHIAVELYQGHVRVSYDPGSYPSSAIYSAETINDGQFHTVE 1250
1251 LVAFDQMVNLSIDGGSPMTMDNFGKHYTLNSEAPLYVGGMPVDVNSAAFR 1300
1301 LWQILNGTGFHGCIRNLYINNELQDFTKTQMKPGVVPGCEPCRKLYCLHG 1350
1351 ICQPNATPGPMCHCEAGWVGLHCDQPADGPCHGHKCVHGQCVPLDALSYS 1400
1401 CQCQDGYSGALCNQAGALAEPCRGLQCLHGHCQASGTKGAHCVCDPGFSG 1450
1451 ELCEQESECRGDPVRDFHQVQRGYAICQTTRPLSWVECRGSCPGQGCCQG 1500
1501 LRLKRRKFTFECSDGTSFAEEVEKPTKCGCALCA 1534

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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