 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q19020 from www.uniprot.org...
The NucPred score for your sequence is 0.78 (see score help below)
1 MDSGNESSIIQPPNLKTAAEGDVKEYIVQVVTSHFGLSPRDQTTLDVELT 50
51 AATTFITDFINEADKNVIVVDRVVAREQGDQPAGAESGGEESAPATFQVH 100
101 DGLFMTDRGQAMMFVKQSNVIEAEKKIATQVSAFPLNGGSAWEQLHFLMS 150
151 RLLNPYCKSFIGQSGRGERDGDKLAPTVQKCFTEAEAALLHLQQNIDIPE 200
201 INLVVNQHILDAIEQAGKENRRAKIEDLGDLVEDANFLNALQSGCNRWVK 250
251 EIRKVTQLERDPSSGTSLQEMTFWLNLERALLKISQKRDGEEVTLTLEAL 300
301 KCGKRFHATVGFDSDNGLKQKLAVVQDYNTLMKEFPLSELVSATDVPKLM 350
351 HAVVGIFLHLRKLRSTKYPLQRALRLVEAISRDLNSQLLKVLSSYNLMRT 400
401 PIAEFNEIMSQCQALFSKWDDEYDKFIALLRDINKKKRDDPSKLSWKVTA 450
451 VHKRLETRLMQILQFRKQHEQFRTVIERVLRPVGNGSREREQLMIDSSEG 500
501 EKSPDEQVDIAYEFLKNVDFLDVDSPAWENAFKRYEDQIGVVETAITTRL 550
551 KSQLESSRNSNEMFSIFSRYNALFIRPRIRGAIYEYQTRLINRVKEDINE 600
601 LQARFTKARGEQGVKIMQTVGLPPFSAKIMWIRNYERQLQRYMKRVEDVL 650
651 GKQWENHVDGRQLKADGDNFKVKLNTQPMFDEWVESVQSQNWTLPNKILT 700
701 VDRVQVDGRMQLQLKINYHSDSSVLYKEVSHLKSMGFRVPLKIVNWAHQA 750
751 NQMRPSATSLIEAARTFASVNAALASVQGVDSLLASYKKDIQNQLIEGAT 800
801 LGWDSYKVDQYQLKLAETVNTYQERCEELLNVVRIVNADLNVLKSCRYDK 850
851 ETIENLLTSIQKGVDQLSLGNYSNLAQWVNTLDRQIETILARRVEDAIRV 900
901 WTLVFSQSEEVEELRERQVVLPTVKNVVVDLCMTAQTLYISPSTRETREK 950
951 ILEQLYEWHSVCTAQMRISGKRFQMVMNEEIEPETYHNILNVMPEGQACL 1000
1001 EKAYDCVNGIMSDLEEYLSEWLSYQSLWVLQAEQLFEMLGTSLSKWMKTL 1050
1051 MEIRKGRLVFDTQDTRKVIFPVSVEYGKAQQKILFKYDYWHKEMLVKFGA 1100
1101 VVGDEMQKFFNSVSKWRNVLETQSVDGGSTSDTIGLISFVQSLKKQTKSG 1150
1151 QDAVDLYRSSQRLLNQQRYQFPAQWLYSENVEGEWSAFTEILSLRDASIQ 1200
1201 TQMMNLQTKFAQEDELVEKRTVETLTEWNKSKPVEGAQRPQEALNVITAF 1250
1251 EAKLNKLTEERNKMRKARVALDLSDSAHAPSEGDKLTVATEELAAMKDVW 1300
1301 KALQPVYTGIDEAKEKTWLSVQPRKIRQSLDELMNQLKQLPVKCRTYKSY 1350
1351 EHVKQMLHTYGKMNMLVAELKSEALKERHWHQMMKEMRVNWNLSDLTLGQ 1400
1401 VWDADILRHEHTIKKILLVAQGEMALEEFLREMREYWQNYEVELVNYQNK 1450
1451 TRLIKGWDDLFNKLKEHQNSLSAMKLSPYYKQFEESAQSWDEKLNKINAM 1500
1501 FDVWIDVQRRWVYLEGLFSGSAEISTLLPFESSRFATITTDVLALMKKVA 1550
1551 ASPRILDVVNMQGAQRLLERLADMLAKIQKALGEYLERERSSFPRFYFVG 1600
1601 DEDLLEIMGNSKDITRIQKHLKKMFAGITAIDINEEDRSITAFHSREGEK 1650
1651 VDLVKIVSTKDVRINDWLQALEAEMKHTLARQLAASLTHFSKMNIQTMTT 1700
1701 DDYVEWLDKFPAQVITLTAEIWWCDEMEKTLADGKGAENVEQAVVKTLEL 1750
1751 LADSVLKEQPPIRRKKMEALITELVHKRDTCRKLVSMKIRAANDFGWLQC 1800
1801 MRFYFDPKQVDPVRCCVVKMANSQFFYGFEYLGIQERLVRTPLTDRCYLT 1850
1851 MTQALHSRLGGSPFGPAGTGKTESVKALGHQLGRFVLVFNCDETFDFQAM 1900
1901 GRILVGLCQVGAWGCFDEFNRLEERMLSAVSQQIQTIQEAVRAGGDMSVD 1950
1951 LVGKRLNVNSNIGIFITMNPGYSGRSNLPDNLKQLFRSLAMTQPDRQLIA 2000
2001 QVMLFSQGFRTAETLANKIVPLFILCKEQLSDQCHYDFGLRALKYVLVSA 2050
2051 GNIKRDKLDKMGSAALEDVAEQQMLIQSVCETLVPKLVNEDIALLFSLLS 2100
2101 DVFPGIHYTANQMRELRQQLSTVCDEHLLIYSDVQGEMGSMWLDKVLQLY 2150
2151 QITNLNHGLMLVGSSGSGKTMAWKVLLKALERWENVEGVAHVIDAKAMSK 2200
2201 DSLYGVMDPNTREWTDGLFTSVIRKIIDNVRGEADRRQWIIFDGDVDPEW 2250
2251 VENLNSVLDDNKLLTLPNGERLSIPPNVRIIFEVADLKYATLATVSRCGM 2300
2301 VWFSEEVVTSEMLFERYLSIIRRVPLDSDSAISFSSSSAPVNLIGEDAKP 2350
2351 TRSIEIQRTAALALQTHFSPDGIVPGSLKYAVSELEHIMPPTPQRLLSSF 2400
2401 FSMMSYSIRKIVSHDEGLIDDSVEIDQIQSFVLRSMLTNLVWAFSGDGKW 2450
2451 KSREMMSDFIRQATTISLPPNQQACLIDYEVQLSGDWQPWLSKVPTMEIE 2500
2501 SHRVAAADLVVPTIDTVRHEMLLAAWLAEHKPLVLCGPPGSGKTMTLLAA 2550
2551 LRSQQEMEVVNVNFSSSTTPELLLRTFDHYCEYRRTPNGVVLAPVQLSQW 2600
2601 LVIFCDEINLPAPDKYGTQRVISFLRQLVELNGFYRTSDHSWVSLERIQF 2650
2651 VGACNPPTDPGRHPMTSRFLRHVPIVYVDYPGQTSLQQIYGTFNRAMLKM 2700
2701 TPAVRGLADQLTNAMVDVYLASQEHFTQDDQPHYVYSPRELTRWVRGISE 2750
2751 AITPLESLSAEQLVRLWAHEAIRLFQDRLVTEEEREWTDKLVDTTAERYF 2800
2801 GNACRLDEALKRPLLYSCWLSRNYVPVTREELQDYVSARLKGFYEEELDV 2850
2851 KLVLFDQMLDHVLRIDRIYRQSQGHLLLIGTAGAGKTTLSRFVAWLNGLS 2900
2901 VFQLKVHSKYTAADFDEDMRTVLRRAGCRNEKLCFIMDESNMLDTGFLER 2950
2951 LNTLLANGEVPGLFEGDEHTTLMTQIKEGAQRQGLILDSHDELYKWFTQQ 3000
3001 VMRNLHVVFTMNPSGSGLRERASTSPALFNRCVLNWFGDWSENALYQVGS 3050
3051 ELTRTMDLDRTDYEGSVRLTPSCELVPSQPTYRDAVVNTLCLVHKTVQKF 3100
3101 NEMETKKGHRVMACTPRHFLDFIKQFMSLFHEKRSDLEEEKIHLNIGLNK 3150
3151 ISETEEQVKELQKSLKLKSNELQEKKEAANLKLKEMLGDQQKAEEEKKFS 3200
3201 EQLQKELAEQLKQMAEKKTFVENDLAQVEPAVAEAQTAVQGIKKSQLVEV 3250
3251 KSMSSPPVTVKLTLEAICILLGENVGTDWKAIRQVMMKDDFMTRILQFDT 3300
3301 ELLTPEILKQMEKYIQNPDWEFDKVNRASVACGPMVKWARAQLLYSTMLH 3350
3351 KVEPLRNELKRLEQEAAKKTQEGKVVDVRITELEESIGKYKEEYAQLIGQ 3400
3401 AENIKQDLLSVQEKVNRSTELLSSLRSERDRWSSGSAGFSQQMDSLVGDA 3450
3451 LLSSAFLAYAGYYDQMLRDEIFHKWFNHVVNAGLHFRHDLARIEYLSTVD 3500
3501 DRLQWQLNSLPVDDLCTENAIMLHRFNRYPLIIDPSGQAVEYIMKQFAGK 3550
3551 NIQKTSFLDESFRKNLESALRFGNSLLVQDVEAYDPILNPVLNREVKRAG 3600
3601 GRVLITIGDQDIDLSPSFQIFMITRDSTVEFSPDICSRVTFVNFTVTSSS 3650
3651 LASQCLNQVLRSERPDVDKKRNDLLKLQGEFAVRLRHLEKALLAALNESK 3700
3701 GKILDDNSVIETLEKLKNEAAEVAQKSAETDKVMAEVDAVSAQYQRLSTA 3750
3751 CSHIYHTLQQLNEIHFLYHYSLDFLVEIFTHVLKTPELSSTTDYAKRLRI 3800
3801 ITTSLFQTVFRRVSRGMLHTDKVLLALLLMRIHIRSNPSAPAYEQHFDLL 3850
3851 LGRSDFVAKNDEADSTIPGGLDFLTVENKKSIAKARKVVGFENVFAHLQH 3900
3901 NSAAVTSWLTNDNPESNVPVVWDDADGKLSPLCIAMNSLIVVHALRPDRL 3950
3951 MASAHRVVSTAFDDHFMQQDKVVDILSIVDNEVSPSEPVLLCSATGYDAS 4000
4001 GKIEDLAVETNRQLTSIAIGSAEGFNQADSALGTATKSGRWVLLKNVHLA 4050
4051 PSWLAQLEKRLHSMKPHAQFRLFLTAEIHPKLPSSILRASRVVVFEPATG 4100
4101 LKANLLRSLSSIPPQRLTKAPTERSRLYLLVCWLHALVQERLRYTPLGWS 4150
4151 TAYEFSDADLRVACDTLDAAVDAVAQGRPNVEPERLPWTTLRTLLSQCIY 4200
4201 GGKIDNQFDQVLLDCVLENLFTAKSFEQDHVLIPKYDGDDSLFTPNMSKK 4250
4251 DQMIGWVEELKNEQLPAWLGLPNNAEKVLLTKRGESMLRNMLKVTDEELA 4300
4301 FNEDGKEEVKPQWMAQLGELAKQWLQLLPKEIVKMRRTVENIKDPLFRFF 4350
4351 EREVNLGSQLLKDIRRDLNEISAVCRAEKKQNNETRALAASLQKGEVPTG 4400
4401 WKRYTVPREVTVMDWMTDLNERLKQLIRIGGADNLKRETFWLGGTFSPEA 4450
4451 YITATRQQVAQANTWSLEQLNLHIHIGRTDSTDVFRISGIDIRGAKSVGG 4500
4501 NKLELCELVKSECDIVEFSWKQDVADGTRLPLYLYGDRRQLISPLAFHLS 4550
4551 SATVFYQRGVALVANSTL 4568
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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