 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P98088 from www.uniprot.org...
The NucPred score for your sequence is 0.67 (see score help below)
1 MSVGRRKLALLWALALALACTRHTGHAQDGSSESSYKHHPALSPIARGPS 50
51 GVPLRGATVFPSLRTIPVVRASNPAHNGRVCSTWGSFHYKTFDGDVFRFP 100
101 GLCNYVFSEHCGAAYEDFNIQLRRSQESAAPTLSRVLMKVDGVVIQLTKG 150
151 SVLVNGHPVLLPFSQSGVLIQQSSSYTKVEARLGLVLMWNHDDSLLLELD 200
201 TKYANKTCGLCGDFNGMPVVSELLSHNTKLTPMEFGNLQKMDDPTDQCQD 250
251 PVPEPPRNCSTGFGICEELLHGQLFSGCVALVDVGSYLEACRQDLCFCED 300
301 TDLLSCVCHTLAEYSRQCTHAGGLPQDWRGPDFCPQKCPNNMQYHECRSP 350
351 CADTCSNQEHSRACEDHCVAGCFCPEGTVLDDIGQTGCVPVSKCACVYNG 400
401 AAYAPGATYSTDCTNCTCSGGRWSCQEVPCPGTCSVLGGAHFSTFDGKQY 450
451 TVHGDCSYVLTKPCDSSAFTVLAELRRCGLTDSETCLKSVTLSLDGAQTV 500
501 VVIKASGEVFLNQIYTQLPISAANVTIFRPSTFFIIAQTSLGLQLNLQLV 550
551 PTMQLFMQLAPKLRGQTCGLCGNFNSIQADDFRTLSGVVEATAAAFFNTF 600
601 KTQAACPNIRNSFEDPCSLSVENEKYAQHWCSQLTDADGPFGRCHAAVKP 650
651 GTYYSNCMFDTCNCERSEDCLCAALSSYVHACAAKGVQLGGWRDGVCTKP 700
701 MTTCPKSMTYHYHVSTCQPTCRSLSEGDITCSVGFIPVDGCICPKGTFLD 750
751 DTGKCVQASNCPCYHRGSMIPNGESVHDSGAICTCTHGKLSCIGGQAPAP 800
801 VCAAPMVFFDCRNATPGDTGAGCQKSCHTLDMTCYSPQCVPGCVCPDGLV 850
851 ADGEGGCITAEDCPCVHNEASYRAGQTIRVGCNTCTCDSRMWRCTDDPCL 900
901 ATCAVYGDGHYLTFDGQSYSFNGDCEYTLVQNHCGGKDSTQDSFRVVTEN 950
951 VPCGTTGTTCSKAIKIFLGGFELKLSHGKVEVIGTDESQEVPYTIRQMGI 1000
1001 YLVVDTDIGLVLLWDKKTSIFINLSPEFKGRVCGLCGNFDDIAVNDFATR 1050
1051 SRSVVGDVLEFGNSWKLSPSCPDALAPKDPCTANPFRKSWAQKQCSILHG 1100
1101 PTFAACHAHVEPARYYEACVNDACACDSGGDCECFCTAVAAYAQACHEVG 1150
1151 LCVSWRTPSICPLFCDYYNPEGQCEWHYQPCGVPCLRTCRNPRGDCLRDV 1200
1201 RGLEGCYPKCPPEAPIFDEDKMQCVATCPTPPLPPRCHVHGKSYRPGAVV 1250
1251 PSDKNCQSCLCTERGVECTYKAEACVCTYNGQRFHPGDVIYHTTDGTGGC 1300
1301 ISARCGANGTIERRVYPCSPTTPVPPTTFSFSTPPLVVSSTHTPSNGPSS 1350
1351 AHTGPPSSAWPTTAGTSPRTRLPTASASLPPVCGEKCLWSPWMDVSRPGR 1400
1401 GTDSGDFDTLENLRAHGYRVCESPRSVECRAEDAPGVPLRALGQRVQCSP 1450
1451 DVGLTCRNREQASGLCYNYQIRVQCCTPLPCSTSSSPAQTTPPTTSKTTE 1500
1501 TRASGSSAPSSTPGTVSLSTARTTPAPGTATSVKKTFSTPSPPPVPATST 1550
1551 SSMSTTAPGTSVVSSKPTPTEPSTSSCLQELCTWTEWIDGSYPAPGINGG 1600
1601 DFDTFQNLRDEGYTFCESPRSVQCRAESFPNTPLADLGQDVICSHTEGLI 1650
1651 CLNKNQLPPICYNYEIRIQCCETVNVCRDITRLPKTVATTRPTPHPTGAQ 1700
1701 TQTTFTTHMPSASTEQPTATSRGGPTATSVTQGTHTTLVTRNCHPRCTWT 1750
1751 KWFDVDFPSPGPHGGDKETYNNIIRSGEKICRRPEEITRLQCRAKSHPEV 1800
1801 SIEHLGQVVQCSREEGLVCRNQDQQGPFKMCLNYEVRVLCCETPRGCHMT 1850
1851 STPGSTSSSPAQTTPSTTSKTTETQASGSSAPSSTPGTVSLSTARTTPAP 1900
1901 GTATSVKKTFSTPSPPPVPATSTSSMSTTAPGTSVVSSKPTPTEPSTSSC 1950
1951 LQELCTWTEWIDGSYPAPGINGGDFDTFQNLRDEGYTFCESPRSVQCRAE 2000
2001 SFPNTPLADLGQDVICSHTEGLICLNKNQLPPICYNYEIRIQCCETVNVC 2050
2051 RDITRPPKTVATTRPTPHPTGAQTQTTFTTHMPSASTEQPTATSRGGPTA 2100
2101 TSVTQGTHTTPVTRNCHPRCTWTTWFDVDFPSPGPHGGDKETYNNIIRSG 2150
2151 EKICRRPEEITRLQCRAKSHPEVSIEHLGQVVQCSREEGLVCRNQDQQGP 2200
2201 FKMCLNYEVRVLCCETPKGCPVTSTPVTAPSTPSGRATSPTQSTSSWQKS 2250
2251 RTTTLVTTSTTSTPQTSTTYAHTTSTTSAPTARTTSAPTTRTTSASPAST 2300
2301 TSGPGNTPSPVPTTSTISAPTTSITSAPTTSTTSAPTSSTTSGPGTTPSP 2350
2351 VPTTSITSAPTTSTTSAPTTSTTSARTSSTTSATTTSRISGPETTPSPVP 2400
2401 TTSTTSATTTSTTSAPTTSTTSAPTSSTTSSPQTSTTSAPTTSTTSGPGT 2450
2451 TPSPVPTTSTTSAPTTRTTSAPKSSTTSAATTSTTSGPETTPRPVPTTST 2500
2501 TSSPTTSTTSAPTTSTTSASTTSTTSGAGTTPSPVPTTSTTSAPTTSTTS 2550
2551 APISSTTSATTTSTTSGPGTTPSPVPTTSTTSAPTTSTTSGPGTTPSAVP 2600
2601 TTSITSAPTTSTNSAPISSTTSATTTSRISGPETTPSPVPTASTTSASTT 2650
2651 STTSGPGTTPSPVPTTSTISVPTTSTTSASTTSTTSASTTSTTSGPGTTP 2700
2701 SPVPTTSTTSAPTTSTTSAPTTSTISAPTTSTTSATTTSTTSAPTPRRTS 2750
2751 APTTSTISASTTSTTSATTTSTTSATTTSTISAPTTSTTLSPTTSTTSTT 2800
2801 ITSTTSAPISSTTSTPQTSTTSAPTTSTTSGPGTTSSPVPTTSTTSAPTT 2850
2851 STTSAPTTRTTSVPTSSTTSTATTSTTSGPGTTPSPVPTTSTTSAPTTRT 2900
2901 TSAPTTSTTSAPTTSTTSAPTSSTTSATTTSTISVPTTSTTSVPGTTPSP 2950
2951 VPTTSTISVPTTSTTSASTTSTTSGPGTTPSPVPTTSTTSAPTTSTTSAP 3000
3001 TTSTISAPTTSTPSAPTTSTTLAPTTSTTSAPTTSTTSTPTSSTTSSPQT 3050
3051 STTSASTTSITSGPGTTPSPVPTTSTTSAPTTSTTSAATTSTISAPTTST 3100
3101 TSAPTTSTTSASTASKTSGLGTTPSPIPTTSTTSPPTTSTTSASTASKTS 3150
3151 GPGTTPSPVPTTSTIFAPRTSTTSASTTSTTPGPGTTPSPVPTTSTASVS 3200
3201 KTSTSHVSISKTTHSQPVTRDCHLRCTWTKWFDIDFPSPGPHGGDKETYN 3250
3251 NIIRSGEKICRRPEEITRLQCRAESHPEVSIEHLGQVVQCSREEGLVCRN 3300
3301 QDQQGPFKMCLNYEVRVLCCETPKGCPVTSTPVTAPSTPSGRATSPTQST 3350
3351 SSWQKSRTTTLVTTSTTSTPQTSTTSAPTTSTTSAPTTSTTSAPTTSTTS 3400
3401 TPQTSISSAPTSSTTSAPTSSTISARTTSIISAPTTSTTSSPTTSTTSAT 3450
3451 TTSTTSAPTSSTTSTPQTSKTSAATSSTTSGSGTTPSPVTTTSTASVSKT 3500
3501 STSHVSVSKTTHSQPVTRDCHPRCTWTKWFDVDFPSPGPHGGDKETYNNI 3550
3551 IRSGEKICRRPEEITRLQCRAKSHPEVSIEHLGQVVQCSREEGLVCRNQD 3600
3601 QQGPFKMCLNYEVRVLCCETPKGCPVTSTSVTAPSTPSGRATSPTQSTSS 3650
3651 WQKSRTTTLVTSSITSTTQTSTTSAPTTSTTPASIPSTTSAPTTSTTSAP 3700
3701 TTSTTSAPTTSTTSTPQTTTSSAPTSSTTSAPTTSTISAPTTSTISAPTT 3750
3751 STTSAPTASTTSAPTSTSSAPTTNTTSAPTTSTTSAPITSTISAPTTSTT 3800
3801 STPQTSTISSPTTSTTSTPQTSTTSSPTTSTTSAPTTSTTSAPTTSTTST 3850
3851 PQTSISSAPTSSTTSAPTASTISAPTTSTTSFHTTSTTSPPTSSTSSTPQ 3900
3901 TSKTSAATSSTTSGSGTTPSPVPTTSTASVSKTSTSHVSVSKTTHSQPVT 3950
3951 RDCHPRCTWTKWFDVDFPSPGPHGGDKETYNNIIRSGEKICRRPEEITRL 4000
4001 QCRAESHPEVSIEHLGQVVQCSREEGLVCRNQDQQGPFKMCLNYEVRVLC 4050
4051 CETPKGCPVTSTPVTAPSTPSGRATSPTQSTSSWQKSRTTTLVTTSTTST 4100
4101 PQTSTTSAPTTSTIPASTPSTTSAPTTSTTSAPTTSTTSAPTHRTTSGPT 4150
4151 TSTTLAPTTSTTSAPTTSTNSAPTTSTISASTTSTISAPTTSTISSPTSS 4200
4201 TTSTPQTSKTSAATSSTTSGSGTTPSPVPTTSTTSASTTSTTSAPTTSTT 4250
4251 SGPGTTPSPVPSTSTTSAATTSTTSAPTTRTTSAPTSSMTSGPGTTPSPV 4300
4301 PTTSTTSAPTTSTTSGPGTTPSPVPTTSTTSAPITSTTSGPGSTPSPVPT 4350
4351 TSTTSAPTTSTTSASTASTTSGPGTTPSPVPTTSTTSAPTTRTTSASTAS 4400
4401 TTSGPGSTPSPVPTTSTTSAPTTRTTPASTASTTSGPGTTPSPVPTTSTT 4450
4451 SASTTSTISLPTTSTTSAPITSMTSGPGTTPSPVPTTSTTSAPTTSTTSA 4500
4501 STASTTSGPGTTPSPVPTTSTTSAPTTSTTSASTASTTSGPGTSLSPVPT 4550
4551 TSTTSAPTTSTTSGPGTTPSPVPTTSTTSAPTTSTTSGPGTTPSPVPTTS 4600
4601 TTPVSKTSTSHLSVSKTTHSQPVTSDCHPLCAWTKWFDVDFPSPGPHGGD 4650
4651 KETYNNIIRSGEKICRRPEEITRLQCRAESHPEVNIEHLGQVVQCSREEG 4700
4701 LVCRNQDQQGPFKMCLNYEVRVLCCETPRGCPVTSVTPYGTSPTNALYPS 4750
4751 LSTSMVSASVASTSVASSSVASSSVAYSTQTCFCNVADRLYPAGSTIYRH 4800
4801 RDLAGHCYYALCSQDCQVVRGVDSDCPSTTLPPAPATSPSISTSEPVTEL 4850
4851 GCPNAVPPRKKGETWATPNCSEATCEGNNVISLRPRTCPRVEKPTCANGY 4900
4901 PAVKVADQDGCCHHYQCQCVCSGWGDPHYITFDGTYYTFLDNCTYVLVQQ 4950
4951 IVPVYGHFRVLVDNYFCGAEDGLSCPRSIILEYHQDRVVLTRKPVHGVMT 5000
5001 NEIIFNNKVVSPGFRKNGIVVSRIGVKMYATIPELGVQVMFSGLIFSVEV 5050
5051 PFSKFANNTEGQCGTCTNDRKDECRTPRGTVVASCSEMSGLWNVSIPDQP 5100
5101 ACHRPHPTPTTVGPTTVGSTTVGPTTVGSTTVGPTTPPAPCLPSPICQLI 5150
5151 LSKVFEPCHTVIPPLLFYEGCVFDRCHMTDLDVVCSSLELYAALCASHDI 5200
5201 CIDWRGRTGHMCPFTCPADKVYQPCGPSNPSYCYGNDSASLGALPEAGPI 5250
5251 TEGCFCPEGMTLFSTSAQVCVPTGCPRCLGPHGEPVKVGHTVGMDCQECT 5300
5301 CEAATWTLTCRPKLCPLPPACPLPGFVPVPAAPQAGQCCPQYSCACNTSR 5350
5351 CPAPVGCPEGARAIPTYQEGACCPVQNCSWTVCSINGTLYQPGAVVSSSL 5400
5401 CETCRCELPGGPPSDAFVVSCETQICNTHCPVGFEYQEQSGQCCGTCVQV 5450
5451 ACVTNTSKSPAHLFYPGETWSDAGNHCVTHQCEKHQDGLVVVTTKKACPP 5500
5501 LSCSLDEARMSKDGCCRFCPPPPPPYQNQSTCAVYHRSLIIQQQGCSSSE 5550
5551 PVRLAYCRGNCGDSSSMYSLEGNTVEHRCQCCQELRTSLRNVTLHCTDGS 5600
5601 SRAFSYTEVEECGCMGRRCPAPGDTQHSEEAEPEPSQEAESGSWERGVPV 5650
5651 SPMH 5654
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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