 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6J6J0 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MDLSAVRVEEVQNVINAMQKILECPICLELIKEPVSTKCDHIFCKFCMLK 50
51 LLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIICAFQLDTGLQ 100
101 YANSYNFAKKENNSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQET 150
151 SPSVQLSNLGTVRTLRTKQRIQPQKKSVYIELGSDSSEDTVNKATYCSVG 200
201 DQELLQITPQGTSDEISLDSAKKAACEFSETDVTNTEHHQPSNNDLNTTE 250
251 KRATERHPEKYQGSSVSNLHVEPCGTNTHASSLQHENSSLLLTKDRMNVE 300
301 KAEFCNKSKQPGLARSQHNRWAGSKETCNDRQTPSTEKKVDLNADPLCER 350
351 KEWNKQKLPCSENPRDTEDVPWITLNSSIQKVNEWFSRSDELLGSDDSHD 400
401 GRSESNAKVADVLDVLNEVDEYSGSSEKIDLLASDPHEALICKSERVHSK 450
451 SVESNIEDKIFGKTYRRKASLPNLSHVTENLIIGAFVTEPQIIQERPLTN 500
501 KLKRKRRATSGLHPEDFIKKADLAVQKTPEMINQGTNQMEQNGQVMNITN 550
551 SGHENKTKGDSIQNEKNPNPIESLEKESAFKTKAEPISSSISNMELELNI 600
601 HNSKAPKKNRLRRKSSTRHIHALELVVSRNLSPPNCTELQIDSCSSSEEI 650
651 KKKKYNQMPVRHSRNLQLMEDKEPATGAKKSNKPNEQTSKRHDSDTFPEL 700
701 KLTNAPGSFTNCSNTSELKEFVNPSLPREEKEEKLGTVKVSNNAKDPKDL 750
751 MLSGERVLQTERSVESSSISLVPGTDYGTQESISLLEVSTLGKAKTEPNK 800
801 CVSQCAAFENPKELIHGCFKDTRNDTEGFKYPLGHEVNHSQETSIEMEES 850
851 ELDTQYLQNTFKVSKRQSFALFSNPGNPEEECATFSAHSRSLKKQSPKVT 900
901 FECEQKEENQGKNESNIKPVQTANITAGFPVVCQKDKPVDYAKCSIKGGS 950
951 RFCLSSQFRGNETGLITPNKHGLSQNPYHIPPLFPIKSFVKTKCKKNLLE 1000
1001 ENSEEHSMSPEREMGNENIPSTVSIISRNNIRENVFKEASSSNINEVGSS 1050
1051 TNEVGSSINEVGSSDENIQAELGRSRGPKLNAMLRLGVLQPEVYKQSFPG 1100
1101 SNGKHPEIKKQEYEEVLQTVNTDFSPCLISDNLEQPMRSSHASQVCSETP 1150
1151 NDLLDDGEIKEDTSFAENDIKESSAVFSKSVQRGELSRSPSPFTHTHLAQ 1200
1201 GYRRGAKKLESSEENLSSEDEELPCFQHLLFGKVSNIPSQSTRHSTVATE 1250
1251 CLSKNTEENLLSLKNSLNDYSNQVILVKASQEHHLSEETKCSASLFSSQC 1300
1301 SELEDLTANTNTQDRFFIGSSKQMRHQSESQGVGLSDKELVSDDEERGTD 1350
1351 LEENNQEEQGVDSNLGEAASGYESETSVSEDCSGLSSQSDILTTQQRDTM 1400
1401 QDNLIKLQQEMAELEAVLEQHGSQPSNSYPSIISDSSALEDLRNPEQSTS 1450
1451 EKAVLTSQKSSEYPISQNPEGLSADKFEVSADSSTNKNKEPGVERSSPSK 1500
1501 CPSLDDRWYMHSCSGSLQNGNYPSQEELIKVVDVEKQQLEESGPHDLTEP 1550
1551 SYLPRQDLEGTPYLESGISLFSDDPESDASEDRAPESAHVGSIPSSTSAL 1600
1601 KVPQLKVAESAQSPAAAQTTNTAGYNAMEESVSREKPELTASTERVNKRM 1650
1651 SMVVSGLTPEEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERT 1700
1701 LKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPK 1750
1751 RARESQDRKIFRGLEICCYGPFTNMPTDQLEWIVQLCGASVVKELSSFTL 1800
1801 GTGVHPIVVVQPDAWTEDNGFHAIGQMCEAPVVTREWVLDSVALYQCQEL 1850
1851 DTYLIPQIPHSHY 1863
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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