 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O88278 from www.uniprot.org...
The NucPred score for your sequence is 0.58 (see score help below)
1 MARRPLWWGLPGPSTPLLLLLLFSLFPSSREEMGGGGDQGWDPGVATATG 50
51 PRAQIGSGAVALCPESPGVWEDGDPGLGVREPVFMKLRVGRQNARNGRGA 100
101 PEQPNREPVVQALGSREQEAGQGSGYLLCWHPEISSCGRTGHLRRGSLPL 150
151 DALSPGDSDLRNSSPHPSELLAQPDSPRPVAFQRNGRRSIRKRVETFRCC 200
201 GKLWEPGHKGQGERSATSTVDRGPLRRDCLPGSLGSGLGEDSAPRAVRTA 250
251 PAPGSAPHESRTAPERMRSRGLFRRGFLFERPGPRPPGFPTGAEAKRILS 300
301 TNQARSRRAANRHPQFPQYNYQTLVPENEAAGTAVLRVVAQDPDPGEAGR 350
351 LVYSLAALMNSRSLELFSIDPQSGLIRTAAALDRESMERHYLRVTAQDHG 400
401 SPRLSATTMVAVTVADRNDHAPVFEQAQYRETLRENVEEGYPILQLRATD 450
451 GDAPPNANLRYRFVGSPAARTAAAAAFEIDPRSGLISTSGRVDREHMESY 500
501 ELVVEASDQGQEPGPRSATVRVHITVLDENDNAPQFSEKRYVAQVREDVR 550
551 PHTVVLRVTATDKDKDANGLVHYNIISGNSRGHFAIDSLTGEIQVMAPLD 600
601 FEAEREYALRIRAQDAGRPPLSNNTGLASIQVVDINDHSPIFVSTPFQVS 650
651 VLENAPLGHSVIHIQAVDADHGENSRLEYSLTGVASDTPFVINSATGWVS 700
701 VSGPLDRESVEHYFFGVEARDHGSPPLSASASVTVTVLDVNDNRPEFTMK 750
751 EYHLRLNEDAAVGTSVVSVTAVDRDANSAISYQITGGNTRNRFAISTQGG 800
801 MGLVTLALPLDYKQERYFKLVLTASDRALHDHCYVHINITDANTHRPVFQ 850
851 SAHYSVSMNEDRPVGSTVVVISASDDDVGENARITYLLEDNLPQFRIDAD 900
901 SGAITLQAPLDYEDQVTYTLAITARDNGIPQKADTTYVEVMVNDVNDNAP 950
951 QFVASHYTGLVSEDAPPFTSVLQISATDRDAHANGRVQYTFQNGEDGDGD 1000
1001 FTIEPTSGIVRTVRRLDREAVPVYELTAYAVDRGVPPLRTPVSIQVTVQD 1050
1051 VNDNAPVFPAEEFEVRVKENSIVGSVVAQITAVDPDDGPNAHIMYQIVEG 1100
1101 NIPELFQMDIFSGELTALIDLDYEARQEYVIVVQATSAPLVSRATVHVRL 1150
1151 VDQNDNSPVLNNFQILFNNYVSNRSDTFPSGIIGRIPAYDPDVSDHLFYS 1200
1201 FERGNELQLLVVNQTSGELRLSRKLDNNRPLVASMLVTVTDGLHSVTAQC 1250
1251 VLRVVIITEELLANSLTVRLENMWQERFLSPLLGHFLEGVAAVLATPTED 1300
1301 VFIFNIQNDTDVGGTVLNVSFSALAPRGAGAGAAGPWFSSEELQEQLYVR 1350
1351 RAALAARSLLDVLPFDDNVCLREPCENYMKCVSVLRFDSSAPFLASASTL 1400
1401 FRPIQPIAGLRCRCPPGFTGDFCETELDLCYSNPCRNGGACARREGGYTC 1450
1451 VCRPRFTGEDCELDTEAGRCVPGVCRNGGTCTNAPNGGFRCQCPAGGAFE 1500
1501 GPRCEVAARSFPPSSFVMFRGLRQRFHLTLSLSFATVQPSGLLFYNGRLN 1550
1551 EKHDFLALELVAGQVRLTYSTGESSTVVSPTVPGGLSDGQWHTVHLRYYN 1600
1601 KPRTDALGGAQGPSKDKVAVLSVDDCNVAVALRFGAEIGNYSCAAAGVQT 1650
1651 SSKKSLDLTGPLLLGGVPNLPENFPVSRKDFIGCMRDLHIDGRRVDMAAF 1700
1701 VANNGTTAGCQAKSHFCASGPCKNGGLCSERWGGFSCDCPVGFGGKDCRL 1750
1751 TMAHPYHFQGNGTLSWDFGNDMPVSVPWYLGLSFRTRATKGVLMQVQLGP 1800
1801 HSVLLCKLDQGLLSVTLSRASGHAVHLLLDQMTVSDGRWHDLRLELQEEP 1850
1851 GGRRGHHIFMVSLDFTLFQDTMAMGSELEGLKVKHLHVGGPPPSSKEEGP 1900
1901 QGLVGCIQGVWTGFTPFGSSALPPPSHRINVEPGCTVTNPCASGPCPPHA 1950
1951 NCKDLWQTFSCTCWPGYYGPGCVDACLLNPCQNQGSCRHLQGGPHGYTCD 2000
2001 CASGYFGQHCEHRMDQQCPRGWWGSPTCGPCNCDVHKGFDPNCNKTSGQC 2050
2051 HCKEFHYRPRGSDSCLPCDCYPVGSTSRSCAPHSGQCPCRPGALGRQCNS 2100
2101 CDSPFAEVTASGCRVLYDACPKSLRSGVWWPQTKFGVLATVPCPRGALGL 2150
2151 RGTGAAVRLCDEDHGWLEPDFFNCTSPAFRELSLLLDGLELNKTALDTVE 2200
2201 AKKLAQRLREVTGQTDHYFSQDVRVTARLLAYLLAFESHQQGFGLTATQD 2250
2251 AHFNENLLWAGSALLAPETGDLWAALGQRAPGGSPGSAGLVRHLEEYAAT 2300
2301 LARNMDLTYLNPVGLVTPNIMLSIDRMEQPSSSQGAHRYPRYHSNLFRGQ 2350
2351 DAWDPHTHVLLPSQSPQPSPSEVLPTSSNAENATASGVVSPPAPLEPESE 2400
2401 PGISIVILLVYRALGGLLPAQFQAERRGARLPQNPVMNSPVVSVAVFRGR 2450
2451 NFLRGALVSPINLEFRLLQTANRSKAICVQWDPPGPADQHGMWTARDCEL 2500
2501 VHRNGSHARCRCSRTGTFGVLMDASPRERLEGDLELLAVFTHVVVAASVT 2550
2551 ALVLTAAVLLSLRSLKSNVRGIHANVAAALGVAELLFLLGIHRTHNQLLC 2600
2601 TVVAILLHYFFLSTFAWLLVQGLHLYRMQVEPRNVDRGAMRFYHALGWGV 2650
2651 PAVLLGLAVGLDPEGYGNPDFCWISIHEPLIWSFAGPIVLVIVMNGIMFL 2700
2701 LAARTSCSTGQREAKKTSVLRTLRSSFLLLLLVSASWLFGLLAVNHSVLA 2750
2751 FHYLHAGLCGLQGLAVLLLFCVLNADARAAWTPACLGKKAAPEETRPAPG 2800
2801 PGSGAYNNTALFEESGLIRITLGASTVSSVSSARSGRAQDQDSQRGRSYL 2850
2851 RDNVLVRHGSTAEHAEHSLQAHAGPTDLDVAMFHRDAGADSDSDSDLSLE 2900
2901 EERSLSIPSSESEDNGRTRGRFQRPLRRAAQSERLLAHPKDVDGNDLLSY 2950
2951 WPALGECEAAPCALQAWGSERRLGLDSNKDAANNNQPELALTSGDETSLG 3000
3001 RAQRQRKGILKNRLQYPLVPQTRGTPELSWCRAATLGHRAVPAASYGRIY 3050
3051 AGGGTGSLSQPASRYSSREQLDLLLRRQLSRERLEEVPVPAPVLHPLSRP 3100
3101 GSQERLDTAPARLEPRDRGSTLPRRQPPRDYPGTMAGRFGSRDALDLGAP 3150
3151 REWLSTLPPPRRNRDLDPQHPPLPLSPQRPLSRDPLLPSRPLDSLSRISN 3200
3201 SRERLDQVPSRHPSREALGPAPQLLRAREDPASGPSHGPSTEQLDILSSI 3250
3251 LASFNSSALSSVQSSSTPSGPHTTATPSATASALGPSTPRSATSHSISEL 3300
3301 SPDSEVPRSEGHS 3313
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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