 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P11533 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MSAHVLWYEEVEDDYEREDVQKKTFTKWINAQFAKCGRRCIEDLFNDFRD 50
51 GRKLLELLECLTGQKIAKEKGSTRVHALNNVNKALQILQRNNVDLVNIGS 100
101 SDIVDGNHKLTLGLIWNIILHWQVKDVMKNIMAGLQQTNSEKILLSWVRQ 150
151 STRNYPQVNVINFTSSWSDGLAFNALLHSHRPDLFDWNAVASQQSPVQRL 200
201 DHAFNIARQHLGIEKLLDPEDVATACPDKKSILMYVTSLFQVLPQQVTME 250
251 AIREVEMLPRHSRVTTEEHIQVHHQQHFSQEITVNIPQRPSPSPKPRFKS 300
301 YAYAQTAYVIPPDQKRRQVPPQFLETVEKRTYTTTVMRSEMDLDSYQTAL 350
351 EEVLTWLLSAEDALQAQGDISSDVEVVKEQFHTHEGFMMELTAHQGRVGN 400
401 VLQVGSQLLAMGKLSDDEENEIQEQMNLLNSRWESLRVASMEKQSNLHKI 450
451 LMDLQNQQLAQLADWLTKTEERTKKIDSEPLGPDLEDLKRQVEEHKAFQD 500
501 DLEQEQVKVNSLTHMVVVVDENSGDKATAALEEQLQHFGSRWAAICRWTE 550
551 DRWVLLQDILRKWQHFAEEQCLFDAWLTEKEGSLSKIQTSDFKDENEMLT 600
601 SLRKLAILKGDIEMKKQMMSKLKSLSRDLLVAVKNKAVAQKLESRLENFA 650
651 QRWDSLVQKLESDSKQVSQAVTTTQTSLTQTTVMETVTMVTTREQILVKH 700
701 AKEELPPPPPHKKRQLLVDSEIRKRFDSDTTELHSWMTRSEAVLQSPEFA 750
751 IYRKEGNLSDLRERVNAIQREKPEKYRKLQDASRSAEALVEQMVNEGLNA 800
801 DNIRQASEQLKSRWIEFCQLLSERLVWLEYQNSIIDFYSQLQRLEQTAIT 850
851 AENWLKAQPTPATDPATVKIQLEKCKDEIIRMSTLQPQIERLKAQSQALK 900
901 EKEQCPVFLDADLAAFTSHFKQILADMHTREKQLQTIFDSLPPARYKDTV 950
951 TTILSWIQQSETKVSIPPVAVAEYEIMEQRLGELKALQSSLQEQQKGLKY 1000
1001 LNTTVEDLSRKAPAEVSQKYRSEVELIVGRWKKLSSQLVEHCQKLEDLMT 1050
1051 KLQRFQNDTKTLKKWMAEVDVFLKEEWPALGDSEALEKQLEQCTALVNDI 1100
1101 QTIQPSLNSVNEIGKKMKREAEPEFASRIATELKDLNAQWEHICQQAHAK 1150
1151 KAALKGGLDKTVSLRKDLSEMHEWITQAEEEYLERDFEYKTPEELQKAVE 1200
1201 ELKRAKEDAMQKEVKVKLITDSVNNFIAKAPPAANEALKKELDVLITSYQ 1250
1251 RLCSRLNGKCKTLEEVWACWHELLSYLDAENKWLNEVELKLKATENIQGG 1300
1301 AEEISESLDSLERLMRHPEDNRNQIRELAQTLTDGGILDELINEKLEKFN 1350
1351 TRWEELQQEAVRRQKSLEQSIQSAQETDKTLRLIQESLAAIDKQLTAYTA 1400
1401 DRVDAAQVPQEAQKIQSELTSHEISLEEMKKRNRGKESAKRVLSQIDVAQ 1450
1451 KKLQDVSMKFRLFQKPANFEQRLQECKRILDEVKLQVPKLETKSVEQEVV 1500
1501 QSHLDHCMKLYKSLSEVKSEVETVIKTGRQIVQKQQTENPKELDERLTAL 1550
1551 KLQYNELGAKVTEKKQELEKCLKLSRKLRKEINSLTEWLAATDVELTKRS 1600
1601 AVQGMPSNLDAEIAWGKATRKEIEKRQVQLKNICDLGENLKTVLKGKESL 1650
1651 VEDKLSLLNSNWIAVTSRAEEWLNLLMEYQKHMEAFDQKVANVTTWIYRA 1700
1701 EILLDESDKQKPQQKEETLKRLKAELNDMHPKVDSVRDQAVDLMTNRGDH 1750
1751 CRKVIEPKLSELNHRFAAISQRIKSGKPFIPLKELEQFDFDIQKLLEPLE 1800
1801 VEIQQGVNLKEEDFNKDMSEDDESTVKELLQRGDTLQKRITDERKREEIK 1850
1851 IKQQLLQTKHNALKDLRSQRRKKALEISHQWYQYKRQADDLMTWLDDIEK 1900
1901 KLASLPDHKDEQKLKEIGGELEKKKEDLNAVNRQAERLSKDGAAKAVEPT 1950
1951 LVQLSKRWRDFESKFAQFRRLNYAQIQTVLEDTTFVMTESMTVETTYVPS 2000
2001 TYLAEILQLLQALSEVEERLNSPVLQAKDCEDLLKQEECLKNIKDCLGRL 2050
2051 QGHIDIIHSKKTPALQSATPRETANIQDKLTQLNSQWEKVNKMYRDRQAR 2100
2101 FDKSKEKWRLFHCEMKSFNEWLTETEEKLSRAQIEAGDVGHVKTKQFLQE 2150
2151 LQDGIGRQQTVVKTLNVTGEEIIEQSSAADANVLKEQLGNLNTRWQEICR 2200
2201 QLVEKRKRIEEEKNILSEFQEDLNKLILWLEETENVIAIPLEPGNEDQLR 2250
2251 DCLGKVKLRVEELLPHKGILKRLNETGGTTLGSASLNPERKHKLESTLKE 2300
2301 ASRRLLKVSRDLPEKQKEIEILLKDFIELNQQINQLTLWITPVKNQLELY 2350
2351 NQVGQPGAFDIKETEAAVQAKQPNVEEVLSKGCHLYKEKPATHPVKKKLE 2400
2401 DLNADWKAINHLILQLKEKPTFGEPALTSPGVLTSGQTVAVDTQARVTKE 2450
2451 TTSFTPEMPSSVLLEVPALADFNKAWAELTDWLSRLDREIKAQRVTVGDL 2500
2501 DDINDMIIKQKANMQDLEQRRPQLDELITAAQNLKNKTSNQEARTIITDR 2550
2551 IEKIQSQWDDVHGYLQNRRQQLHEMQKDSTQWLEAKQEAEQVLEQAKAKL 2600
2601 ESWKEISYTVEALKKQNSELKQFSKEIRQWQMNIEGVNDVALKPVRDYSA 2650
2651 DDTRKVELMTDNINATWATINKRVSEREAALESALLMLQEFYLDLEKFLA 2700
2701 WLTEAETTANVLQDATHKEKTLEDPQMVRELMKQWQDLQAEIDAHTDIFH 2750
2751 NLDENGQKILRSLEGSEDAVLLQRRLDNMNFRWSELRKKSLNIRSHLEAS 2800
2801 TDQWKRLHLSLQELLAWLQLKEDELKQQAPIGGDIPTVQKQNDVHRTFKR 2850
2851 ELKTKEPVIMNALETVRLFLADQPVEGLEKVYPEPRDLSPEERAQNVTKV 2900
2901 LRRQADDVRTEWDKLNLRSADWQKKIDDALERLQGLQEAMDELDLKLRQA 2950
2951 EAFKGSWQPVGDLLIDSLQDHLEKVKVYRAEMVPLKEKVHQVNELAHRFA 3000
3001 PPDIQLSPYTLSCLEDLNTRWKVLQVAIDERIRQLHEAHRDFGPTSQHFL 3050
3051 TTSVQGPWERAISPNKVPYYINHETQTTCWDHPKMTELYQSLADLNNVRF 3100
3101 SAYRTAMKLRRLQKALCLDLLNLSAACDALDQHNLKQNDQPMDILQIINC 3150
3151 LTTIYDRLEQEHNNLVNVPLCVDMCLNWLLNVYDTGRTGRIRVLSFKTGV 3200
3201 VSLCKAHLEDKYRYLFKQVASSTGFCDQRRLGLLLHDSIQIPRQLGEVAS 3250
3251 FGGSNIEPSVRSCFQFANNKPEIEAALFLDWMRLEPQSMVWLPVLHRVAA 3300
3301 AETAKHQAKCNICKECPIIGFRYRSLKHFNYDICQSCFFSGRVAKGHKMH 3350
3351 YPMVEYCTPTTSGEDVRDFAKVLKNKFRTKRYFAKHPRMGYLPVQTVLEG 3400
3401 DNMETPVTLINFWPVDSALAEMENSNGSYLNDSISPNESIDDEHLLIQHY 3450
3451 CQSLNQESPLSQPRSPAQILISLESEERGELERILADLEEENRNLQAEYD 3500
3501 RLKQQHDHKGLSPLPSPPEMMPVSPQSPRDAELIAEAKLLRQHKGRLEAR 3550
3551 MQILEDHNKQLESQLHRLRQLLEQPQADAKVNGTTLSSPSTSLQRSDSSQ 3600
3601 PMLLRVVGSQTSETMGEDDLLSPPQDTSTGLEEVMEQLNNSFPSSRGRNA 3650
3651 PGKPVREATM 3660
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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