 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O15230 from www.uniprot.org...
The NucPred score for your sequence is 0.58 (see score help below)
1 MAKRLCAGSALCVRGPRGPAPLLLVGLALLGAARAREEAGGGFSLHPPYF 50
51 NLAEGARIAASATCGEEAPARGSPRPTEDLYCKLVGGPVAGGDPNQTIRG 100
101 QYCDICTAANSNKAHPASNAIDGTERWWQSPPLSRGLEYNEVNVTLDLGQ 150
151 VFHVAYVLIKFANSPRPDLWVLERSMDFGRTYQPWQFFASSKRDCLERFG 200
201 PQTLERITRDDAAICTTEYSRIVPLENGEIVVSLVNGRPGAMNFSYSPLL 250
251 REFTKATNVRLRFLRTNTLLGHLMGKALRDPTVTRRYYYSIKDISIGGRC 300
301 VCHGHADACDAKDPTDPFRLQCTCQHNTCGGTCDRCCPGFNQQPWKPATA 350
351 NSANECQSCNCYGHATDCYYDPEVDRRRASQSLDGTYQGGGVCIDCQHHT 400
401 TGVNCERCLPGFYRSPNHPLDSPHVCRRCNCESDFTDGTCEDLTGRCYCR 450
451 PNFSGERCDVCAEGFTGFPSCYPTPSSSNDTREQVLPAGQIVNCDCSAAG 500
501 TQGNACRKDPRVGRCLCKPNFQGTHCELCAPGFYGPGCQPCQCSSPGVAD 550
551 DRCDPDTGQCRCRVGFEGATCDRCAPGYFHFPLCQLCGCSPAGTLPEGCD 600
601 EAGRCLCQPEFAGPHCDRCRPGYHGFPNCQACTCDPRGALDQLCGAGGLC 650
651 RCRPGYTGTACQECSPGFHGFPSCVPCHCSAEGSLHAACDPRSGQCSCRP 700
701 RVTGLRCDTCVPGAYNFPYCEAGSCHPAGLAPVDPALPEAQVPCMCRAHV 750
751 EGPSCDRCKPGFWGLSPSNPEGCTRCSCDLRGTLGGVAECQPGTGQCFCK 800
801 PHVCGQACASCKDGFFGLDQADYFGCRSCRCDIGGALGQSCEPRTGVCRC 850
851 RPNTQGPTCSEPARDHYLPDLHHLRLELEEAATPEGHAVRFGFNPLEFEN 900
901 FSWRGYAQMAPVQPRIVARLNLTSPDLFWLVFRYVNRGAMSVSGRVSVRE 950
951 EGRSATCANCTAQSQPVAFPPSTEPAFITVPQRGFGEPFVLNPGTWALRV 1000
1001 EAEGVLLDYVVLLPSAYYEAALLQLRVTEACTYRPSAQQSGDNCLLYTHL 1050
1051 PLDGFPSAAGLEALCRQDNSLPRPCPTEQLSPSHPPLITCTGSDVDVQLQ 1100
1101 VAVPQPGRYALVVEYANEDARQEVGVAVHTPQRAPQQGLLSLHPCLYSTL 1150
1151 CRGTARDTQDHLAVFHLDSEASVRLTAEQARFFLHGVTLVPIEEFSPEFV 1200
1201 EPRVSCISSHGAFGPNSAACLPSRFPKPPQPIILRDCQVIPLPPGLPLTH 1250
1251 AQDLTPAMSPAGPRPRPPTAVDPDAEPTLLREPQATVVFTTHVPTLGRYA 1300
1301 FLLHGYQPAHPTFPVEVLINAGRVWQGHANASFCPHGYGCRTLVVCEGQA 1350
1351 LLDVTHSELTVTVRVPKGRWLWLDYVLVVPENVYSFGYLREEPLDKSYDF 1400
1401 ISHCAAQGYHISPSSSSLFCRNAAASLSLFYNNGARPCGCHEVGATGPTC 1450
1451 EPFGGQCPCHAHVIGRDCSRCATGYWGFPNCRPCDCGARLCDELTGQCIC 1500
1501 PPRTIPPDCLLCQPQTFGCHPLVGCEECNCSGPGIQELTDPTCDTDSGQC 1550
1551 KCRPNVTGRRCDTCSPGFHGYPRCRPCDCHEAGTAPGVCDPLTGQCYCKE 1600
1601 NVQGPKCDQCSLGTFSLDAANPKGCTRCFCFGATERCRSSSYTRQEFVDM 1650
1651 EGWVLLSTDRQVVPHERQPGTEMLRADLRHVPEAVPEAFPELYWQAPPSY 1700
1701 LGDRVSSYGGTLRYELHSETQRGDVFVPMESRPDVVLQGNQMSITFLEPA 1750
1751 YPTPGHVHRGQLQLVEGNFRHTETRNTVSREELMMVLASLEQLQIRALFS 1800
1801 QISSAVFLRRVALEVASPAGQGALASNVELCLCPASYRGDSCQECAPGFY 1850
1851 RDVKGLFLGRCVPCQCHGHSDRCLPGSGVCVDCQHNTEGAHCERCQAGFV 1900
1901 SSRDDPSAPCVSCPCPLSVPSNNFAEGCVLRGGRTQCLCKPGYAGASCER 1950
1951 CAPGFFGNPLVLGSSCQPCDCSGNGDPNLLFSDCDPLTGACRGCLRHTTG 2000
2001 PRCEICAPGFYGNALLPGNCTRCDCTPCGTEACDPHSGHCLCKAGVTGRR 2050
2051 CDRCQEGHFGFDGCGGCRPCACGPAAEGSECHPQSGQCHCRPGTMGPQCR 2100
2101 ECAPGYWGLPEQGCRRCQCPGGRCDPHTGRCNCPPGLSGERCDTCSQQHQ 2150
2151 VPVPGGPVGHSIHCEVCDHCVVLLLDDLERAGALLPAIHEQLRGINASSM 2200
2201 AWARLHRLNASIADLQSQLRSPLGPRHETAQQLEVLEQQSTSLGQDARRL 2250
2251 GGQAVGTRDQASQLLAGTEATLGHAKTLLAAIRAVDRTLSELMSQTGHLG 2300
2301 LANASAPSGEQLLRTLAEVERLLWEMRARDLGAPQAAAEAELAAAQRLLA 2350
2351 RVQEQLSSLWEENQALATQTRDRLAQHEAGLMDLREALNRAVDATREAQE 2400
2401 LNSRNQERLEEALQRKQELSRDNATLQATLHAARDTLASVFRLLHSLDQA 2450
2451 KEELERLAASLDGARTPLLQRMQTFSPAGSKLRLVEAAEAHAQQLGQLAL 2500
2501 NLSSIILDVNQDRLTQRAIEASNAYSRILQAVQAAEDAAGQALQQADHTW 2550
2551 ATVVRQGLVDRAQQLLANSTALEEAMLQEQQRLGLVWAALQGARTQLRDV 2600
2601 RAKKDQLEAHIQAAQAMLAMDTDETSKKIAHAKAVAAEAQDTATRVQSQL 2650
2651 QAMQENVERWQGQYEGLRGQDLGQAVLDAGHSVSTLEKTLPQLLAKLSIL 2700
2701 ENRGVHNASLALSASIGRVRELIAQARGAASKVKVPMKFNGRSGVQLRTP 2750
2751 RDLADLAAYTALKFYLQGPEPEPGQGTEDRFVMYMGSRQATGDYMGVSLR 2800
2801 DKKVHWVYQLGEAGPAVLSIDEDIGEQFAAVSLDRTLQFGHMSVTVERQM 2850
2851 IQETKGDTVAPGAEGLLNLRPDDFVFYVGGYPSTFTPPPLLRFPGYRGCI 2900
2901 EMDTLNEEVVSLYNFERTFQLDTAVDRPCARSKSTGDPWLTDGSYLDGTG 2950
2951 FARISFDSQISTTKRFEQELRLVSYSGVLFFLKQQSQFLCLAVQEGSLVL 3000
3001 LYDFGAGLKKAVPLQPPPPLTSASKAIQVFLLGGSRKRVLVRVERATVYS 3050
3051 VEQDNDLELADAYYLGGVPPDQLPPSLRRLFPTGGSVRGCVKGIKALGKY 3100
3101 VDLKRLNTTGVSAGCTADLLVGRAMTFHGHGFLRLALSNVAPLTGNVYSG 3150
3151 FGFHSAQDSALLYYRASPDGLCQVSLQQGRVSLQLLRTEVKTQAGFADGA 3200
3201 PHYVAFYSNATGVWLYVDDQLQQMKPHRGPPPELQPQPEGPPRLLLGGLP 3250
3251 ESGTIYNFSGCISNVFVQRLLGPQRVFDLQQNLGSVNVSTGCAPALQAQT 3300
3301 PGLGPRGLQATARKASRRSRQPARHPACMLPPHLRTTRDSYQFGGSLSSH 3350
3351 LEFVGILARHRNWPSLSMHVLPRSSRGLLLFTARLRPGSPSLALFLSNGH 3400
3401 FVAQMEGLGTRLRAQSRQRSRPGRWHKVSVRWEKNRILLVTDGARAWSQE 3450
3451 GPHRQHQGAEHPQPHTLFVGGLPASSHSSKLPVTVGFSGCVKRLRLHGRP 3500
3501 LGAPTRMAGVTPCILGPLEAGLFFPGSGGVITLDLPGATLPDVGLELEVR 3550
3551 PLAVTGLIFHLGQARTPPYLQLQVTEKQVLLRADDGAGEFSTSVTRPSVL 3600
3601 CDGQWHRLAVMKSGNVLRLEVDAQSNHTVGPLLAAAAGAPAPLYLGGLPE 3650
3651 PMAVQPWPPAYCGCMRRLAVNRSPVAMTRSVEVHGAVGASGCPAA 3695
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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