 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9GV77 from www.uniprot.org...
The NucPred score for your sequence is 0.32 (see score help below)
1 MASALLCFLAAILPGMIAAQNTWVLGTSDVRTVEPVNPVGGGVSLGGIDI 50
51 DSTENRIIVQNTGIAVPFGREKAIDPNSELVINVQAGDSCSIKVLPRQSD 100
101 PLSQIPGRLVPPSFPCDFSPGEVKYVHFGSRKPQTDKVKLQLRYDTATDV 150
151 YIIPFTIDVRVESKQLEIVTRNVPLVVQDLMGTSDALDADKLEFEFDSNT 200
201 EVCKVTVLSSTSGLPRYGEVMNHDEQGQMIDCNDFLELGIQYRHTAATSS 250
251 PREDYIPLVVELQNQQGQVIKQEYFQSMVRIIDGDDNTPPSLVLSSDMMM 300
301 EVDQFVMTAITPSILAAEDVETPADMLIFNITSQTLGPDDGMIVSTDDRN 350
351 QPITSFTQKDLRDLKIAYKPPPRDTDVQTIYQIELEIVDSELATSETHSL 400
401 LIVVKPKNTLAPVVTTNTGLVLFEGQSRPLLGGQNLGISDEDNLQDVIIA 450
451 PINGSRYGELRIGNQRIKQFTIADLIEGAVTYHHYGTDTYSDNIIFRMTD 500
501 GQHEVEFLFPITIAPIDDEAPIVDVNTGVTVNENEVVAITNFVLSATDID 550
551 SDDSEIRFVLEQPLSDMGNLFLRQVNIPEDPQNWISQDNFYEREVTEFTL 600
601 EDIQNGHLFYQHGGSHNADPVFDRILFRVVDSADPQPNESPVQELLVKVM 650
651 PQDLQPPEMFGGTTLQLSVDEFQITPILKKNLRFTDMDSNDRELKYTIVS 700
701 PLTDSDSNNNLPVGDIVLTDEPNTPINMFTQAQINHMKVSYKPPSTELGI 750
751 APRAITFQFVVQDTQGNMGSPHNFIILLRPVDNQPPTITNTGVQVFERGT 800
801 VIIDQTMLDATDPDTDRNSIRVVLVQPPVFGTMNLNDIALEKGDEFTLGD 850
851 IENSRVKYVSGDAEEQSDEIHLEITDGVHVVPIVIHINVAPIDDEAPTLD 900
901 LPPGTIGSFLEVQENSFSLITSNILSASDPDTEDLLLTFIVDRQPNEGRI 950
951 ESNGVVADVFTQQDIVNGLVRYVHTGGEIGPSKRDDSFNLTLSDMSPDWI 1000
1001 LGGNEITQVEVYVTVLPVDNLAPNVTMGVQFYVDEAGKGNINMTHLQAPD 1050
1051 VDTEDDDILCTIVVAPSVGYLENISPAPGSEKSRGGMPISAFSIKDLRLN 1100
1101 HINYVQSIHQGMEPEEDQFTFRCTDGVNESPNFLFPINIIPVNDEEPQVY 1150
1151 AREIIVDEGGQRIIDEPLLRAEDGDVPADELHFFIVTPPQHGTITYTRLE 1200
1201 GDIPILNFTMDQIANGNDIKYIHDDSETTEDSFTVLLTDGKYEITKEITI 1250
1251 TILEVDDETPRLTINDGIDIEIGESRIISNRILKATDLDSADSNLTYTVR 1300
1301 YAPEKGLLQRLSKFDGSVVENITLGMNFTQWEVDNQRIRYVHTDGDGGRD 1350
1351 LIKFDITDGTNPLIDRYFYVTVDHIDNVHPSIINAGVTMQEGSRVTLTTS 1400
1401 IISTSDLNSPDEDLLFTITTAPTKGHLESTDNPGMPINSFTQLDLAGSKI 1450
1451 YYVHTADDEVKMDSFQFQVTDGFNTVVRTFRISFTDVDNKEPVVRYDTIR 1500
1501 LQEGDNKLITPFELGIDDRDTPANELRFTITQLPIHGNILRNNTALVTEF 1550
1551 TMHDINENLISYQHDGSEQTADSFSFIVTDGTHNEFYVLPDITTLTRQPQ 1600
1601 QVPIEIVPVDNGAPQIVVNRGAPTLDLLGTGELGFMITNKYLMSEDRDSV 1650
1651 DNSLLYVITTQPQHGYIMNIALGNISITNFTQSDVNNMYIQYIVYPNVDA 1700
1701 TSDTFFVEVRDAGGNTLPNQPFRLNWSWISLEKEYYEVNETERYLNIKLV 1750
1751 RRGYLGETSFVGIQTADGTAIADEDFRGKSARQVQFNPGQTEGFWRVRIL 1800
1801 NDRLYEQAEVFEIILHDPVMGALEYPDRAVVTIFDAEDESGVFIDLPDNY 1850
1851 VIEEDIGEFLVPIRRTGDLSQELMASCSTMPGSATGSDPSPVLSFSDYIS 1900
1901 RMEEDPDNMVAFDKGEDLAYCRILIIDDSLYEEDETFQVKLSNPMGGRIG 1950
1951 NPSAINVIIAGDTDDVPSFYFGEPEYKVDENAPFVEVTVFRTGTDVSKMA 2000
2001 SVTVRSRASNPVSAVAGEDYAGISRNLDFAPGVNQQTVKVYIIDDRGQPR 2050
2051 LEGPETFELVLNMPMNGVLGAPSKTVITINDTISDLPKVEFRHPTYEVNE 2100
2101 NDIRITAEVVRSGDLSIESSVRCYTRQGSAQVMMDYDERPNTEASIITFL 2150
2151 PGERSKTCTVLLMDDNVFEPDEAFRLVLGSPRTASGVPAVVGEQNVTVVT 2200
2201 VHDVGDAPIIKFPETKFSIDEPTDLDSVVTVSIPVIRMGDNTQTSIVRVF 2250
2251 TKDGSARSGIDYNPLSQVLEFGFNVTERVVEIEILPDEDRNEMREAFTLH 2300
2301 ITNDQMMIADVQMNHAIIYIEQEGQASGVTFPSQPVVVSLLDYDDIPNAR 2350
2351 TNPPRGYPLICVSPCNPKYPDFATTGPICDSEGLNDTVTQFRWMVSAPTS 2400
2401 ESGVTSPLRQTDSDTFFSSTKSITLDSVYFGPGSRVQCVARAVGSEGDAG 2450
2451 REHPSNSIVISTTDGMCMPRVANAIGAEPFTARMRYTGPADPDYPNKVRL 2500
2501 TVTMPHVDGMLPVISTRQLSNFELALSKDGYRVGTHRCSNLLDYNEIPTD 2550
2551 FGFITEETKNPNVVGDTYAYQYSPELRGEETLRFYRNLNLEACLWEFNAY 2600
2601 YDMSELLDECGGLVGTDGQVLDLVQSYVSMRIPLFVSFVFHSPVATGGWK 2650
2651 HFDQQSTLQLTFVYDTSILWQNGIGSQVTTGTQSLQGNLYPTSMRIDEDG 2700
2701 RLVVNFRTEALFNGLFVQSHQSTDVVSTVNSIDHPGITYSLSLLRTEPTY 2750
2751 AQPEQLWQFVSDLSVSDYSGTYTIQLVPCTTLPNTVYSQPPVCNPEDIIT 2800
2801 FELPIRFQQVSDPVPEEYSLNTEFVLVGKESIYLSDGSMGFGEGSDVAYN 2850
2851 PGDTIFGRIHVDPVQNLGAGFNLDIQKVFLCTGRDGYIPKYNPAANEYGC 2900
2901 VADTPNLLYAFKILDRGAPDTIVREFNGLPFNATLAIDNAADLELVQQPG 2950
2951 ADGFRLASDALFEVDYGRTWYLHSIYSMRSSESSGIGKRETEHHAISSRQ 3000
3001 RRQANSEALVDPAQGQGTNMKRVALQGPQDVDNNLGGTYELAPKGTNVVM 3050
3051 IAVVIGVILIILLVALVIGVVVRRRQAKQQPVVVVNGSAKVVSNVHFDDN 3100
3101 TEV 3103
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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