 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P13944 from www.uniprot.org...
The NucPred score for your sequence is 0.31 (see score help below)
1 MRTALCSAVAALCAAALLSSIEAEVNPPSDLNFTIIDEHNVQMSWKRPPD 50
51 AIVGYRITVVPTNDGPTKEFTLSPSTTQTVLSDLIPEIEYVVSIASYDEV 100
101 EESLPVFGQLTIQTGGPGIPEEKKVEAQIQKCSISAMTDLVFLVDGSWSV 150
151 GRNNFRYILDFMVALVSAFDIGEEKTRVGVVQYSSDTRTEFNLNQYFRRS 200
201 DLLDAIKRIPYKGGNTMTGEAIDYLVKNTFTESAGARKGFPKVAIVITDG 250
251 KAQDEVEIPARELRNIGVEVFSLGIKAADAKELKLIASQPSLKHVFNVAN 300
301 FDGIVDIQNEIILQVCSGVDEQLGELVSGEEVVEPASNLVATQISSKSVR 350
351 ITWDPSTSQITGYRVQFIPMIAGGKQHVLSVGPQTTALNVKDLSPDTEYQ 400
401 INVYAMKGLTPSEPITIMEKTQQVKVQVECSRGVDVKADVVFLVDGSYSI 450
451 GIANFVKVRAFLEVLVKSFEISPRKVQISLVQYSRDPHMEFSLNRYNRVK 500
501 DIIQAINTFPYRGGSTNTGKAMTYVREKVFVTSKGSRPNVPRVMILITDG 550
551 KSSDAFKEPAIKLRDADVEIFAVGVKDAVRTELEAIASPPAETHVYTVED 600
601 FDAFQRISFELTQSVCLRIEQELAAIRKKSYVPAKNMVFSDVTSDSFKVS 650
651 WSAAGSEEKSYLIKYKVAIGGDEFIVSVPASSTSSVLTNLLPETTYAVSV 700
701 IAEYEDGDGPPLDGEETTLEVKGAPRNLRITDETTDSFIVGWTPAPGNVL 750
751 RYRLVYRPLTGGERRQVTVSANERSTTLRNLIPDTRYEVSVIAEYQSGPG 800
801 NALNGYAKTDEVRGNPRNLRVSDATTSTTMKLSWSAAPGKVQHVLYNLHT 850
851 RYAGVETKELTVKGDTTSKELKGLDEATRYALTVSALYASGAGEALSGEG 900
901 ETLEERGSPRNLITTDITDTTVGLSWTPAPGTVNNYRIVWKSLYDDTMGE 950
951 KRVPGNTVDAVLDGLEPETKYRISIYAAYSSGEGDPVEGEAFTDVSQSAR 1000
1001 TVTVDNETENTMRVSVAALTWEGLVLARVLPNRSGGRQMFGKVNASATSI 1050
1051 VLKRLKPRTTYDLSVVPIYDFGQGKSRKAEGTTASPFKPPRNLRTSDSTM 1100
1101 SSFRVTWEPAPGRVKGYKVTFHPTEDDRNLGELVVGPYDSTVVLEELRAG 1150
1151 TTYKVNVFGMFDGGESNPLVGQEMTTLSDTTTEPFLSRGLECRTRAEADI 1200
1201 VLLVDGSWSIGRPNFKTVRNFISRIVEVFDIGPDKVQIGLAQYSGDPRTE 1250
1251 WNLNAYRTKEALLDAVTNLPYKGGNTLTGMALDFILKNNFKQEAGLRPRA 1300
1301 RKIGVLITDGKSQDDVVTPSRRLRDEGVELYAIGIKNADENELKQIATDP 1350
1351 DDIHAYNVADFSFLASIGEDVTTNLCNSVKGPGDLPPPSNLVISEVTPHS 1400
1401 FRLRWSPPPESVDRYRVEYYPTTGGPPKQFYVSRMETTTVLKDLTPETEY 1450
1451 IVNVFSVVEDESSEPLIGREITYPLSSVRNLNVYDIGSTSMRVRWEPVNG 1500
1501 ATGYLLTYEPVNATVPTTEKEMRVGPSVNEVQLVDLIPNTEYTLTAYVLY 1550
1551 GDITSDPLTSQEVTLPLPGPRGVTIRDVTHSTMNVLWDPAPGKVRKYIIR 1600
1601 YKIADEADVKEVEIDRLKTSTTLTDLSSQRLYNVKVVAVYDEGESLPVVA 1650
1651 SCYSAVPSPVNLRITEITKNSFRGTWDHGAPDVSLYRITWGPYGRSEKAE 1700
1701 SIVNGDVNSLLFENLNPDTLYEVSVTAIYPDESETVDDLIGSERTLPLVP 1750
1751 ITTPAPKSGPRNLQVYNATSHSLTVKWDPASGRVQRYKIIYQPINGDGPE 1800
1801 QSTMVGGRQNSVVIQKLQPDTPYAITVSSMYADGEGGRMTGRGRTKPLTT 1850
1851 VKNMLVYDPTTSTLNVRWDHAEGNPRQYKVFYRPTAGGAEEMTTVPGNTN 1900
1901 YVILRSLEPNTPYTVTVVPVFPEGDGGRTTDTGRTLERGTPRNIQVYNPT 1950
1951 PNSMNVRWEPAPGPVQQYRVNYSPLSGPRPSESIVVPANTRDVMLERLTP 2000
2001 DTAYSINVIALYADGEGNPSQAQGRTLPRSGPRNLRVFDETTNSLSVQWD 2050
2051 HADGPVQQYRIIYSPTVGDPIDEYTTVPGIRNNVILQPLQSDTPYKITVV 2100
2101 AVYEDGDGGQLTGNGRTVGLLPPQNIYITDEWYTRFRVSWDPSPSPVLGY 2150
2151 KIVYKPVGSNEPMEVFVGEVTSYTLHNLSPSTTYDVNVYAQYDSGMSIPL 2200
2201 TDQGTTLYLNVTDLTTYKIGWDTFCIRWSPHRSATSYRLKLNPADGSRGQ 2250
2251 EITVRGSETSHCFTGLSPDTEYNATVFVQTPNLEGPPVSVREHTVLKPTE 2300
2301 APTPPPTPPPPPTIPPARDVCRGAKADIVFLTDASWSIGDDNFNKVVKFV 2350
2351 FNTVGAFDLINPAGIQVSLVQYSDEAQSEFKLNTFDDKAQALGALQNVQY 2400
2401 RGGNTRTGKALTFIKEKVLTWESGMRRGVPKVLVVVTDGRSQDEVRKAAT 2450
2451 VIQHSGFSVFVVGVADVDYNELAKIASKPSERHVFIVDDFDAFEKIQDNL 2500
2501 VTFVCETATSTCPLIYLEGYTSPGFKMLESYNLTEKHFASVQGVSLESGS 2550
2551 FPSYVAYRLHKNAFVSQPIREIHPEGLPQAYTIIMLFRLLPESPSEPFAI 2600
2601 WQITDRDYKPQVGVVLDPGSKVLSFFNKDTRGEVQTVTFDNDEVKKIFYG 2650
2651 SFHKVHIVVTSSNVKIYIDCSEILEKPIKEAGNITTDGYEILGKLLKGDR 2700
2701 RSATLEIQNFDIVCSPVWTSRDRCCDLPSMRDEAKCPALPNACTCTQDSV 2750
2751 GPPGPPGPPGGPGAKGPRGERGLTGSSGPPGPRGETGPPGPQGPPGPQGP 2800
2801 NGLQIPGEPGRQGMKGDAGQPGLPGRSGTPGLPGPPGPVGPPGERGFTGK 2850
2851 DGPTGPRGPPGPAGAPGVPGVAGPSGKPGKPGDRGTPGTPGMKGEKGDRG 2900
2901 DIASQNMMRAVARQVCEQLINGQMSRFNQMLNQIPNDYYSNRNQPGPPGP 2950
2951 PGPPGAAGTRGEPGPGGRPGFPGPPGVQGPPGERGMPGEKGERGTGSQGP 3000
3001 RGLPGPPGPQGESRTGPPGSTGSRGPPGPPGRPGNAGIRGPPGPPGYCDS 3050
3051 SQCASIPYNGQGFPEPYVPESGPYQPEGEPFIVPMESERREDEYEDYGVE 3100
3101 MHSPEYPEHMRWKRSLSRKAKRKP 3124
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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