 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P11532 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MLWWEEVEDCYEREDVQKKTFTKWVNAQFSKFGKQHIENLFSDLQDGRRL 50
51 LDLLEGLTGQKLPKEKGSTRVHALNNVNKALRVLQNNNVDLVNIGSTDIV 100
101 DGNHKLTLGLIWNIILHWQVKNVMKNIMAGLQQTNSEKILLSWVRQSTRN 150
151 YPQVNVINFTTSWSDGLALNALIHSHRPDLFDWNSVVCQQSATQRLEHAF 200
201 NIARYQLGIEKLLDPEDVDTTYPDKKSILMYITSLFQVLPQQVSIEAIQE 250
251 VEMLPRPPKVTKEEHFQLHHQMHYSQQITVSLAQGYERTSSPKPRFKSYA 300
301 YTQAAYVTTSDPTRSPFPSQHLEAPEDKSFGSSLMESEVNLDRYQTALEE 350
351 VLSWLLSAEDTLQAQGEISNDVEVVKDQFHTHEGYMMDLTAHQGRVGNIL 400
401 QLGSKLIGTGKLSEDEETEVQEQMNLLNSRWECLRVASMEKQSNLHRVLM 450
451 DLQNQKLKELNDWLTKTEERTRKMEEEPLGPDLEDLKRQVQQHKVLQEDL 500
501 EQEQVRVNSLTHMVVVVDESSGDHATAALEEQLKVLGDRWANICRWTEDR 550
551 WVLLQDILLKWQRLTEEQCLFSAWLSEKEDAVNKIHTTGFKDQNEMLSSL 600
601 QKLAVLKADLEKKKQSMGKLYSLKQDLLSTLKNKSVTQKTEAWLDNFARC 650
651 WDNLVQKLEKSTAQISQAVTTTQPSLTQTTVMETVTTVTTREQILVKHAQ 700
701 EELPPPPPQKKRQITVDSEIRKRLDVDITELHSWITRSEAVLQSPEFAIF 750
751 RKEGNFSDLKEKVNAIEREKAEKFRKLQDASRSAQALVEQMVNEGVNADS 800
801 IKQASEQLNSRWIEFCQLLSERLNWLEYQNNIIAFYNQLQQLEQMTTTAE 850
851 NWLKIQPTTPSEPTAIKSQLKICKDEVNRLSDLQPQIERLKIQSIALKEK 900
901 GQGPMFLDADFVAFTNHFKQVFSDVQAREKELQTIFDTLPPMRYQETMSA 950
951 IRTWVQQSETKLSIPQLSVTDYEIMEQRLGELQALQSSLQEQQSGLYYLS 1000
1001 TTVKEMSKKAPSEISRKYQSEFEEIEGRWKKLSSQLVEHCQKLEEQMNKL 1050
1051 RKIQNHIQTLKKWMAEVDVFLKEEWPALGDSEILKKQLKQCRLLVSDIQT 1100
1101 IQPSLNSVNEGGQKIKNEAEPEFASRLETELKELNTQWDHMCQQVYARKE 1150
1151 ALKGGLEKTVSLQKDLSEMHEWMTQAEEEYLERDFEYKTPDELQKAVEEM 1200
1201 KRAKEEAQQKEAKVKLLTESVNSVIAQAPPVAQEALKKELETLTTNYQWL 1250
1251 CTRLNGKCKTLEEVWACWHELLSYLEKANKWLNEVEFKLKTTENIPGGAE 1300
1301 EISEVLDSLENLMRHSEDNPNQIRILAQTLTDGGVMDELINEELETFNSR 1350
1351 WRELHEEAVRRQKLLEQSIQSAQETEKSLHLIQESLTFIDKQLAAYIADK 1400
1401 VDAAQMPQEAQKIQSDLTSHEISLEEMKKHNQGKEAAQRVLSQIDVAQKK 1450
1451 LQDVSMKFRLFQKPANFEQRLQESKMILDEVKMHLPALETKSVEQEVVQS 1500
1501 QLNHCVNLYKSLSEVKSEVEMVIKTGRQIVQKKQTENPKELDERVTALKL 1550
1551 HYNELGAKVTERKQQLEKCLKLSRKMRKEMNVLTEWLAATDMELTKRSAV 1600
1601 EGMPSNLDSEVAWGKATQKEIEKQKVHLKSITEVGEALKTVLGKKETLVE 1650
1651 DKLSLLNSNWIAVTSRAEEWLNLLLEYQKHMETFDQNVDHITKWIIQADT 1700
1701 LLDESEKKKPQQKEDVLKRLKAELNDIRPKVDSTRDQAANLMANRGDHCR 1750
1751 KLVEPQISELNHRFAAISHRIKTGKASIPLKELEQFNSDIQKLLEPLEAE 1800
1801 IQQGVNLKEEDFNKDMNEDNEGTVKELLQRGDNLQQRITDERKREEIKIK 1850
1851 QQLLQTKHNALKDLRSQRRKKALEISHQWYQYKRQADDLLKCLDDIEKKL 1900
1901 ASLPEPRDERKIKEIDRELQKKKEELNAVRRQAEGLSEDGAAMAVEPTQI 1950
1951 QLSKRWREIESKFAQFRRLNFAQIHTVREETMMVMTEDMPLEISYVPSTY 2000
2001 LTEITHVSQALLEVEQLLNAPDLCAKDFEDLFKQEESLKNIKDSLQQSSG 2050
2051 RIDIIHSKKTAALQSATPVERVKLQEALSQLDFQWEKVNKMYKDRQGRFD 2100
2101 RSVEKWRRFHYDIKIFNQWLTEAEQFLRKTQIPENWEHAKYKWYLKELQD 2150
2151 GIGQRQTVVRTLNATGEEIIQQSSKTDASILQEKLGSLNLRWQEVCKQLS 2200
2201 DRKKRLEEQKNILSEFQRDLNEFVLWLEEADNIASIPLEPGKEQQLKEKL 2250
2251 EQVKLLVEELPLRQGILKQLNETGGPVLVSAPISPEEQDKLENKLKQTNL 2300
2301 QWIKVSRALPEKQGEIEAQIKDLGQLEKKLEDLEEQLNHLLLWLSPIRNQ 2350
2351 LEIYNQPNQEGPFDVKETEIAVQAKQPDVEEILSKGQHLYKEKPATQPVK 2400
2401 RKLEDLSSEWKAVNRLLQELRAKQPDLAPGLTTIGASPTQTVTLVTQPVV 2450
2451 TKETAISKLEMPSSLMLEVPALADFNRAWTELTDWLSLLDQVIKSQRVMV 2500
2501 GDLEDINEMIIKQKATMQDLEQRRPQLEELITAAQNLKNKTSNQEARTII 2550
2551 TDRIERIQNQWDEVQEHLQNRRQQLNEMLKDSTQWLEAKEEAEQVLGQAR 2600
2601 AKLESWKEGPYTVDAIQKKITETKQLAKDLRQWQTNVDVANDLALKLLRD 2650
2651 YSADDTRKVHMITENINASWRSIHKRVSEREAALEETHRLLQQFPLDLEK 2700
2701 FLAWLTEAETTANVLQDATRKERLLEDSKGVKELMKQWQDLQGEIEAHTD 2750
2751 VYHNLDENSQKILRSLEGSDDAVLLQRRLDNMNFKWSELRKKSLNIRSHL 2800
2801 EASSDQWKRLHLSLQELLVWLQLKDDELSRQAPIGGDFPAVQKQNDVHRA 2850
2851 FKRELKTKEPVIMSTLETVRIFLTEQPLEGLEKLYQEPRELPPEERAQNV 2900
2901 TRLLRKQAEEVNTEWEKLNLHSADWQRKIDETLERLRELQEATDELDLKL 2950
2951 RQAEVIKGSWQPVGDLLIDSLQDHLEKVKALRGEIAPLKENVSHVNDLAR 3000
3001 QLTTLGIQLSPYNLSTLEDLNTRWKLLQVAVEDRVRQLHEAHRDFGPASQ 3050
3051 HFLSTSVQGPWERAISPNKVPYYINHETQTTCWDHPKMTELYQSLADLNN 3100
3101 VRFSAYRTAMKLRRLQKALCLDLLSLSAACDALDQHNLKQNDQPMDILQI 3150
3151 INCLTTIYDRLEQEHNNLVNVPLCVDMCLNWLLNVYDTGRTGRIRVLSFK 3200
3201 TGIISLCKAHLEDKYRYLFKQVASSTGFCDQRRLGLLLHDSIQIPRQLGE 3250
3251 VASFGGSNIEPSVRSCFQFANNKPEIEAALFLDWMRLEPQSMVWLPVLHR 3300
3301 VAAAETAKHQAKCNICKECPIIGFRYRSLKHFNYDICQSCFFSGRVAKGH 3350
3351 KMHYPMVEYCTPTTSGEDVRDFAKVLKNKFRTKRYFAKHPRMGYLPVQTV 3400
3401 LEGDNMETPVTLINFWPVDSAPASSPQLSHDDTHSRIEHYASRLAEMENS 3450
3451 NGSYLNDSISPNESIDDEHLLIQHYCQSLNQDSPLSQPRSPAQILISLES 3500
3501 EERGELERILADLEEENRNLQAEYDRLKQQHEHKGLSPLPSPPEMMPTSP 3550
3551 QSPRDAELIAEAKLLRQHKGRLEARMQILEDHNKQLESQLHRLRQLLEQP 3600
3601 QAEAKVNGTTVSSPSTSLQRSDSSQPMLLRVVGSQTSDSMGEEDLLSPPQ 3650
3651 DTSTGLEEVMEQLNNSFPSSRGRNTPGKPMREDTM 3685
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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