 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5GN48 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MSEVSSDEREDVQKKTFTKWINAQFSKFGKQHIENLFNDLQDGRRLLDLL 50
51 EGLTGQKLPKEKGSTRVHALNNVNKALQVLQKNNVDLVNIGSTDIVDGNH 100
101 KLTLGLIWNIILHWQVKNVMKNIMAGLQQTNSEKILLSWVRQSTRNYPQV 150
151 NVINFTTSWSDGLALNALIHSHRPDLFDWNSVVCQQSATQRLEHAFNIAK 200
201 YQLGIEKLLDPEDVATTYPDKKSILMYVTSLFQVLPQQVSIEAIQEVEML 250
251 PRPSKVTREEHFQLHHQMHYSQQITVCLAQGYERTPSPKPRFKSYAYTQA 300
301 AYVTTSDPTRSPFPSQRLESPEDKSFGSSLLETEVNLDSYQTALEEVLSW 350
351 LLSAEDTLQAQGEISNDVEEVKEQFHTHEGYMMDLTSHQGRIGSVLQLGS 400
401 QLIGKGKLSEDEETEVQEQMNLLNSRWECLRVASVEKQSNLHKVLMDLQN 450
451 QQLKELNDWLTKTEEKTRKMEKEPLGPDLEDLKHQIQQHKVLQEDLEQEQ 500
501 VRVNSLTHMVVVVDESSGDHATAALEEQLKVLGDRWANICRWTEDRWVLL 550
551 QDILLKWQRFTEEQCLFSTWLSEKEDALNKIHTTGFKDQGEMLSSLQKLA 600
601 VLKTDLEKKKQTMDKLSSLNQDLLSTLKNTLVAQKMEAWLDNFAQRWDNL 650
651 VQKLEKSSTQISQAVTTTQPSLTQTTVMETVTMVTTREQILVKHAQEELP 700
701 PPPPQKKRQIIVDSEIRKRLDVDITELHSWITRSEAVLQSPEFAIYRKEG 750
751 NFSDLKEKVNAIEREKAEKFRKLQDASRSAQALVEQMVNEGVNADSIKQA 800
801 AEQLNSRWIEFCQLLSERLNWLEYQNRIITFYNQLQQLEQITTAAENWLK 850
851 TQPITTSEPTAVKSQLKICKDEVNRLSALQPQIERLKIESIALKEKGQGP 900
901 MFLDADSVAFTNHFNQVFADMQAKEKELQIIFDTLPPMRYQETMSTILTW 950
951 IQHSEAKLSIPQATVTEYEIMEQRLGELQALQSSLQEQQNGLNYLSTTVK 1000
1001 EMSKKAPSNISRKYQSEFEEIEGRWKKLSAQLMEHCQKLEEQIAKLRKLQ 1050
1051 NHIKTLKNWMAEVDIFLKEEWPALGDSEILRKQLKQCRLLVSDIQTIQPS 1100
1101 LNSVNEGGQKIKKEAEPEFASRLETELRELNTQWDYICRQVYARKEALKG 1150
1151 GLDKTISLQKDLSEMHEWMTQAEEEYLERDFEYKTPDELQTAVEEMKRAK 1200
1201 EEAQQKEAKVKLLTESVNSVIAQAPPAAQEALKKELDTLTTNYQWLCTRL 1250
1251 NGKCKTLEEVWACWHELLSYLEKANKWLSEVEFKLKTTENIPGGAEEISE 1300
1301 VLESLENLMQHSEDNPNQIRILAQTLTDGGVMDELINEELETFNSRWREL 1350
1351 HEEAVRRQKLLEQSIQSAQEIEKSLHLIQDSLSSIDHQLAVYIADKVDAA 1400
1401 QMPQEAQKIQSDLTSHEISLEEMKKHYQGKEAAPRVLSQIELAQKKLQDV 1450
1451 SMKFRLFQKPANFEQRLQESKMILDEVKMHLPALEIKSVEQEVVQSQLNH 1500
1501 CVNLYKSLSEVKSEVEMVIKTGRQIVQKKQTENPKELDERVTALKLHYNE 1550
1551 LGAKVTERKQQLEKCLKLSRKMRKEMNVLTEWLAATDTELTKRSAVEGMP 1600
1601 SNLDSEVVWGKATQKEIEKQKFHLKSISEIGEALKMVLGKKETLVEDKLS 1650
1651 LLNSNWIAVTSRAEEWLNLLLEYQKHMENFDQNVDHITKWIIQADTLLDE 1700
1701 SEKKKPQQKEDVLKRLKAEMNDMRPKVDSTRDQAANLMANRGDHCRKVIE 1750
1751 PKISELNHRFAAISHRIKTGKASIPLKELEQFNSDIQKLLEPLEAEIQQG 1800
1801 VNLKEEDFNKDMSEDNEGTVKELLQRGDNLQQRITDERKREEIKIKQQLL 1850
1851 QTKHNALKDLRSQRRKKALEISHQWYQYKRQADDLLKCLDDIEKKLASLP 1900
1901 EPQDEKKIKEIDRELQKKKEELDAVRRQAEGLSEDGAAMAVEPTQIQLSK 1950
1951 RWREIESKFAHFRRLNFAQIHTVHEESVMVMTEDMPLEISYVPSAYLTEI 2000
2001 THVSQALSEVEQLLNAPDLCAKDFEDLFKQEESLKNIKDSLQQISGRVDI 2050
2051 IHNKKTAGLQSATPVERTRLQEALSQLDFQWERVNKMYKDRQGKFDRSVE 2100
2101 KWRRFHYDMKIFNQWLTEAEHFLKKTQIPENWEHAKYKWYLKELQDGIGQ 2150
2151 RQTIVRVLNATGEEVIQQSSKTDASILQEKLGSLNLRWQEVCKQLAERKK 2200
2201 RLEEQKNILSEFQRDLNEFVLWLEEADNITSVALEPGNEQQLKEKLEEIK 2250
2251 LLAEELPLRQGTLKQLNETGGTVLVSAPISPEEQDKIENKLKQTNLQWIK 2300
2301 VSRILPEKQGEIEAHIKDLGQFEEQLNHLLVWLSPIKNQLEIYNQPNQTG 2350
2351 PFDIKETEVAVQAKQLDVEGILSKGQHLYKEKPATQPVKRKLEDLSSEWK 2400
2401 AVTHLLQELRAKWPGPTPGLTTIEAPTSQTVTLVTQPTVTKETAISKPEM 2450
2451 PSSLLLEVPALADFNRAWTELTDWLSLLDRVIKSQRVMVGDLEDINEMII 2500
2501 KQKATLQDLEQRRPQLEELITAAQNLKNKTSNQEARTIITDRIERIQSQW 2550
2551 DEVQEHLQNRRQQLNEMLKDSTQWLEAKEEAEQVLGQARAKLESWKEGPY 2600
2601 TMDAIQRKITETKQLAKDLRQWQINVDVANDLALKLLRDYSADDTRKVHM 2650
2651 ITENINASWANIHKRLSERETVLEETHRLLQQFPLDLEKFLAWLTEAETT 2700
2701 ANVLQDATHKERLLEDSKGVRELMKQWQDLQGEIEAHTDIYHNLDENGQK 2750
2751 ILRSLEGSDDAILLQRRLDNMNFKWSELRKKSLNIRSHLEASSDQWKRLH 2800
2801 LSLQELLVWLQLKDDELSRQAPIGGDCPAVQKQNDVHRAFKRELKTKEPV 2850
2851 IMSTLETVRIFLTEQPLEGLEKLYQEPRELPPEERAQNVTRLLRKQAEEV 2900
2901 NTEWEKLNLHSADWQRKIDEALERLQELQEATDELDLKLRQAEVIKGSWQ 2950
2951 PVGDLLIDSLQDHLEKVKALRGEKAPLKENVSHVNDLARQLTTLGIQLSP 3000
3001 YNLSTLEDLNTRWKLLQVAVEDRIRQLHEAHRDFGPASQHFLSTSVQGPW 3050
3051 ERAISPNKVPYYINHETQTTCWDHPKMTELYQSLADLNNVRFSAYRTAMK 3100
3101 LRRLQKALCLDLLSLSAACDALDQHNLKQNDQPMDILQIINCLTTVYDRL 3150
3151 EQEHNNLVNVPLCVDMCLNWLLNVYDTGRTGRIRVLSFKTGIVSLCKAHL 3200
3201 EDKYRYLFKQVASSTGFCDQRRLGLLLHDSIQIPRQLGEVASFGGSNIEP 3250
3251 SVRSCFQFANNKPEIEAALFLDWMRLEPQSMVWLPVLHRVAAAETAKHQA 3300
3301 KCNICKECPIIGFRYRSLKHFNYDICQSCFFSGRVAKGHKMHYPMVEYCT 3350
3351 PTTSGEDVRDFAKVLKNKFRTKRYFAKHPRMGYLPVQTVLEGDNMETPVT 3400
3401 LINFWPVDSAPASSPQLSHDDTHSRIEHYASRLAEMENSNGSYLNDSISP 3450
3451 NESIDDEHLLIQHYCQSLNQDSPLSQPRSPAQILISLESEERGELERILA 3500
3501 DLEEENRNLQAEYDRLKQQHEHKGLSPLPSPPEMMPTSPQSPRDAELIAE 3550
3551 AKLLRQHKGRLEARMQTLEDHNKQLESQLHRLRQLLEQPQAEAKVNGTTV 3600
3601 SSPSTSLQRSDSSQPMLLRVVGSQTSESMGEEDLLSPPQDTSTGLEEVME 3650
3651 QLNNSFPSSRGRNTPGKPVREDTM 3674
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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