 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P97929 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MPVEYKRRPTFWEIFKARCSTADLGPISLNWFEELSSEAPPYNSEPPEES 50
51 EYKPHGYEPQLFKTPQRNPPYHQFASTPIMFKERSQTLPLDQSPFRELGK 100
101 VVASSKHKTHSKKKTKVDPVVDVASPPLKSCLSESPLTLRCTQAVLQREK 150
151 PVVSGSLFYTPKLKEGQTPKPISESLGVEVDPDMSWTSSLATPPTLSSTV 200
201 LIARDEEARSSVTPADSPATLKSCFSNHNESPQKNDRSVPSVIDSENKNQ 250
251 QEAFSQGLGKMLGDSSGKRNSFKDCLRKPIPNILEDGETAVDTSEEDSFS 300
301 LCFPKRRTRNLQKMRMGKTRKKIFSETRTDELSEEARRQTDDKNSFVFEM 350
351 ELRESDPLDPGVTSQKPFYSQNEEICNEAVQCSDSRWSQSNLSGLNETQT 400
401 GKITLPHISSHSQNISEDFIDMKKEGTGSITSEKSLPHISSLPEPEKMFS 450
451 EETVVDKEHEGQHFESLEDSIAGKQMVSRTSQAACLSPSIRKSIFKMREP 500
501 LDETLGTVFSDSMTNSTFTEEHEASACGLGILTACSQREDSICPSSVDTG 550
551 SWPTTLTDTSATVKNAGLISTLKNKKRKFIYSVSDDASLQGKKLQTHRQL 600
601 ELTNLSAQLEASAFEVPLTFTNVNSGIPDSSDKKRCLPNDPEEPSLTNSF 650
651 GTATSKEISYIHALISQDLNDKEAIVIEEKPQPYTAREADFLLCLPERTC 700
701 ENDQKSPKVSNGKEKVLVSACLPSAVQLSSISFESQENPLGDHNGTSTLK 750
751 LTPSSKLPLSKADMVSREKMCKMPEKLQCESCKVNIELSKNILEVNEICI 800
801 LSENSKTPGLLPPGENIIEVASSMKSQFNQNAKIVIQKDQKGSPFISEVA 850
851 VNMNSEELFPDSGNNFAFQVTNKCNKPDLGSSVELQEEDLSHTQGPSLKN 900
901 SPMAVDEDVDDAHAAQVLITKDSDSLAVVHDYTEKSRNNIEQHQKGTEDK 950
951 DFKSNSSLNMKSDGNSDCSDKWSEFLDPVLNHNFGGSFRTASNKEIKLSE 1000
1001 HNVKKSKMFFKDIEEQYPTRLACIDIVNTLPLANQKKLSEPHIFDLKSVT 1050
1051 TVSTQSHNQSSVSHEDTDTAPQMLSSKQDFHSNNLTTSQKAEITELSTIL 1100
1101 EESGSQFEFTQFRKPSHIAQNTSEVPGNQMVVLSTASKEWKDTDLHLPVD 1150
1151 PSVGQTDHSKQFEGSAGVKQSFPHLLEDTCNKNTSCFLPNINEMEFGGFC 1200
1201 SALGTKLSVSNEALRKAMKLFSDIENSEEPSAKVGPRGFSSSAHHDSVAS 1250
1251 VFKIKKQNTEKSFDEKSSKCQVTLQNNIEMTTCIFVGRNPEKYIKNTKHE 1300
1301 DSYTSSQRNNLENSDGSMSSTSGPVYIHKGDSDLPADQGSKCPESCTQYA 1350
1351 REENTQIKENISDLTCLEIMKAEETCMKSSDKKQLPSDKMEQNIKEFNIS 1400
1401 FQTASGKNTRVSKESLNKSVNIFNRETDELTVISDSLNSKILHGINKDKM 1450
1451 HTSCHKKAISIKKVFEDHFPIVTVSQLPAQQHPEYEIESTKEPTLLSFHT 1500
1501 ASGKKVKIMQESLDKVKNLFDETQYVRKTASFSQGSKPLKDSKKELTLAY 1550
1551 EKIEVTASKCEEMQNFVSKETEMLPQQNYHMYRQTENLKTSNGTSSKVQE 1600
1601 NIENNVEKNPRICCICQSSYPVTEDSALAYYTEDSRKTCVRESSLSKGRK 1650
1651 WLREQGDKLGTRNTIKIECVKEHTEDFAGNASYEHSLVIIRTEIDTNHVS 1700
1701 ENQVSTLLSDPNVCHSYLSQSSFCHCDDMHNDSGYFLKNKIDSDVPPDMK 1750
1751 NAEGNTISPRVSATKERNLHPQTINEYCVQKLETNTSPHANKDVAIDPSL 1800
1801 LDSRNCKVGSLVFITAHSQETERTKEIVTDNCYKIVEQNRQSKPDTCQTS 1850
1851 CHKVLDDSKDFICPSSSGDVCINSRKDSFCPHNEQILQHNQSMSGLKKAA 1900
1901 TPPVGLETWDTSKSIREPPQAAHPSRTYGIFSTASGKAIQVSDASLEKAR 1950
1951 QVFSEMDGDAKQLSSMVSLEGNEKPHHSVKRENSVVHSTQGVLSLPKPLP 2000
2001 GNVNSSVFSGFSTAGGKLVTVSESALHKVKGMLEEFDLIRTEHTLQHSPI 2050
2051 PEDVSKILPQPCAEIRTPEYPVNSKLQKTYNDKSSLPSNYKESGSSGNTQ 2100
2101 SIEVSLQLSQMERNQDTQLVLGTKVSHSKANLLGKEQTLPQNIKVKTDEM 2150
2151 KTFSDVPVKTNVGEYYSKESENYFETEAVESAKAFMEDDELTDSEQTHAK 2200
2201 CSLFTCPQNETLFNSRTRKRGGVTVDAVGQPPIKRSLLNEFDRIIESKGK 2250
2251 SLTPSKSTPDGTVKDRSLFTHHMSLEPVTCGPFCSSKERQGAQRPHLTSP 2300
2301 AQELLSKGHPWRHSALEKSPSSPIVSILPAHDVSATRTERTRHSGKSTKV 2350
2351 FVPPFKMKSQFHGDEHFNSKNVNLEGKNQKSTDGDREDGNDSHVRQFNKD 2400
2401 LMSSLQSARDLQDMRIKNKERRHLRLQPGSLYLTKSSTLPRISLQAAVGD 2450
2451 RAPSACSPKQLYIYGVSKECINVNSKNAEYFQFDIQDHFGKEDLCAGKGF 2500
2501 QLADGGWLIPSNDGKAGKEEFYRALCDTPGVDPKLISSIWVANHYRWIVW 2550
2551 KLAAMEFAFPKEFANRCLNPERVLLQLKYRYDVEIDNSRRSALKKILERD 2600
2601 DTAAKTLVLCISDIISPSTKVSETSGGKTSGEDANKVDTIELTDGWYAVR 2650
2651 AQLDPPLMALVKSGKLTVGQKIITQGAELVGSPDACAPLEAPDSLRLKIS 2700
2701 ANSTRPARWHSRLGFFRDPRPFPLPLSSLFSDGGNVGCVDIIVQRVYPLQ 2750
2751 WVEKTVSGLYIFRSEREEEKEALRFAEAQQKKLEALFTKVHTEFKDHEED 2800
2801 TTQRCVLSRTLTRQQVHALQDGAELYAAVQYASDPDHLEACFSEEQLRAL 2850
2851 NNYRQMLNDKKQARIQSEFRKALESAEKEEGLSRDVTTVWKLRVTSYKKK 2900
2901 EKSALLSIWRPSSDLSSLLTEGKRYRIYHLAVSKSKSKFERPSIQLTATK 2950
2951 RTQYQQLPVSSETLLQVYQPRESLHFSRLSDPAFQPPCSEVDVVGVVVSV 3000
3001 VKPIGLAPLVYLSDECLNLLVVKFGIDLNEDIKPRVLIAASNLQCQPEST 3050
3051 SGVPTLFAGHFSIFSASPKEAYFQEKVNNLKHAIENIDTFYKEAEKKLIH 3100
3101 VLEGDSPKWSTPNKDPTREPHAASTCCASDLLGSGGQFLRISPTGQQSYQ 3150
3151 SPLSHCTLKGKSMPLAHSAQMAAKSWSGENEIDDPKTCRKRRALDFLSRL 3200
3201 PLPSPVSPICTFVSPAAQKAFQPPRSCGTKYATPIKKEPSSPRRRTPFQK 3250
3251 TSGVSLPDCDSVADEELALLSTQALTPDSVGGNEQAFPGDSTRNPQPAQR 3300
3301 PDQQVGPRSRKESLRDCRGDSSEKLAVES 3329
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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