 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q86V15 from www.uniprot.org...
The NucPred score for your sequence is 0.89 (see score help below)
1 MDLGTAEGTRCTDPPAGKPAMAPKRKGGLKLNAICAKLSRQVVVEKRADA 50
51 GSHTEGSPSQPRDQERSGPESGAARAPRSEEDKRRAVIEKWVNGEYSEEP 100
101 APTPVLGRIAREGLELPPEGVYMVQPQGCSDEEDHAEEPSKDGGALEEKD 150
151 SDGAASKEDSGPSTRQASGEASSLRDYAASTMTEFLGMFGYDDQNTRDEL 200
201 ARKISFEKLHAGSTPEAATSSMLPTSEDTLSKRARFSKYEEYIRKLKAGE 250
251 QLSWPAPSTKTEERVGKEVVGTLPGLRLPSSTAHLETKATILPLPSHSSV 300
301 QMQNLVARASKYDFFIQKLKTGENLRPQNGSTYKKPSKYDLENVKYLHLF 350
351 KPGEGSPDMGGAIAFKTGKVGRPSKYDVRGIQKPGPAKVPPTPSLAPAPL 400
401 ASVPSAPSAPGPGPEPPASLSFNTPEYLKSTFSKTDSITTGTVSTVKNGL 450
451 PTDKPAVTEDVNIYQKYIARFSGSQHCGHIHCAYQYREHYHCLDPECNYQ 500
501 RFTSKQDVIRHYNMHKKRDNSLQHGFMRFSPLDDCSVYYHGCHLNGKSTH 550
551 YHCMQVGCNKVYTSTSDVMTHENFHKKNTQLINDGFQRFRATEDCGTADC 600
601 QFYGQKTTHFHCRRPGCTFTFKNKCDIEKHKSYHIKDDAYAKDGFKKFYK 650
651 YEECKYEGCVYSKATNHFHCIRAGCGFTFTSTSQMTSHKRKHERRHIRSS 700
701 GALGLPPSLLGAKDTEHEESSNDDLVDFSALSSKNSSLSASPTSQQSSAS 750
751 LAAATAATEAGPSATKPPNSKISGLLPQGLPGSIPLALALSNSGLPTPTP 800
801 YFPILAGRGSTSLPVGTPSLLGAVSSGSAASATPDTPTLVASGAGDSAPV 850
851 AAASVPAPPASIMERISASKGLISPMMARLAAAALKPSATFDPGSGQQVT 900
901 PARFPPAQVKPEPGESTGAPGPHEASQDRSLDLTVKEPSNESNGHAVPAN 950
951 SSLLSSLMNKMSQGNPGLGSLLNIKAEAEGSPAAEPSPFLGKAVKALVQE 1000
1001 KLAEPWKVYLRRFGTKDFCDGQCDFLHKAHFHCVVEECGALFSTLDGAIK 1050
1051 HANFHFRTEGGAAKGNTEAAFPASAAETKPPMAPSSPPVPPVTTATVSSL 1100
1101 EGPAPSPASVPSTPTLLAWKQLASTIPQMPQIPASVPHLPASPLATTSLE 1150
1151 NAKPQVKPGFLQFQENDPCLATDCKYANKFHFHCLFGNCKYVCKTSGKAE 1200
1201 SHCLDHINPNNNLVNVRDQFAYYSLQCLCPNQHCEFRMRGHYHCLRTGCY 1250
1251 FVTNITTKLPWHIKKHEKAERRAANGFKYFTKREECGRLGCKYNQVNSHF 1300
1301 HCIREGCQFSFLLKHQMTSHARKHMRRMLGKNFDRVPPSQGPPGLMDAET 1350
1351 DECMDYTGCSPGAMSSESSTMDRSCSSTPVGNESTAAGNTISMPTASGAK 1400
1401 KRFWIIEDMSPFGKRRKTASSRKMLDEGMMLEGFRRFDLYEDCKDAACQF 1450
1451 SLKVTHYHCTRENCGYKFCGRTHMYKHAQHHDRVDNLVLDDFKRFKASLS 1500
1501 CHFADCPFSGTSTHFHCLRCRFRCTDSTKVTAHRKHHGKQDVISAAGFCQ 1550
1551 FSSSADCAVPDCKYKLKCSHFHCTFPGCRHTVVGMSQMDSHKRKHEKQER 1600
1601 GEPAAEGPAPGPPISLDGSLSLGAEPGSLLFLQSAAAGLGLALGDAGDPG 1650
1651 PPDAAAPGPREGAAAAAAAAGESSQEDEEEELELPEEEAEDDEDEDDDED 1700
1701 DDDEDDDEDDDDEDLRTDSEESLPEAAAEAAGAGARTPALAALAALGAPG 1750
1751 PAPTAASSP 1759
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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