 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9GKK8 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MDLSALRVEEVQNVINAMQKILECPICLELIKEPVSTKCDHIFCKFCMLK 50
51 LLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIICAFQLDTGLE 100
101 YANSYNFAKKENNSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQET 150
151 SLSVQLSNLGTVRTLRTKQRIQPQKKSVYIELGSDSSEDTVNKATYCSVG 200
201 DQELLQITPQGTRDEISLDSAKKAACEFSETDVTNTEHHQPSNNDLNTTE 250
251 KRATERHPEKYQGSSVSNLHVEPCGTNTHASSLQHENSSLLLTKDRMNVE 300
301 KAEFCNKSKQPGLARSQHNRWAGSKETCNDRRTPSTEKKVDLNADPLCER 350
351 KEWNKQKLPCSENPRDTEDVPWITLNSSIQKVNEWFSRSDELLGSDDSHD 400
401 GGSESNAKVADVLDVLNEVDEYSGSSEKIDLLASDPHEALICKSERVHSK 450
451 SVESNTEDKIFGKTYRRKASLPNLSHVTENLIIGAFVTEPQIIQERPLTN 500
501 KLKRKRRATSGLHPEDFIKKADLAVQKTPEMINQGTNQMEQNGQVMNITN 550
551 SGHENKTKGDSIQNEKNPNPIESLEKESAFKTKAEPISSSISNMELELNI 600
601 HNSKAPKKNRLRRKSSTRHIHALELVVSRNLSPPNCTELQIDSCSSSEEI 650
651 KKKKYNQMPVRHSRNLQLMEDKEPATGVKKSNKPNEQTSKRHDSDTFPEL 700
701 KLTNAPGSFTNCSNTSELKEFVNPSLPREEKEEKLETVKVSNNAEDPKDL 750
751 MLSGERVLQTERSVESSSISLVPGTDYGTQESISLLEVSTLGKAKTEPNK 800
801 CVSQCAAFENPKGLIHGCSKDTRNDTEGFKYPLGHEVNHSRETSIEMEES 850
851 ELDAQYLQNTFKVSKRQSFALFSNPGNPEEECATFSAHCRSLKKQSPKVT 900
901 FEREQKEQNQGKNESNIKPVQTVNITAGFPVVCQKDKPVDYAKCSIKGGS 950
951 RFCLSSQFRGNETGLITPNKHGLLQNPYHIPPLFPIKSFVKTKCKKNLLE 1000
1001 ENFEEHSMSPEREMGNENIPSTVSTISRNNIRENVFKEASSSNINEVGSS 1050
1051 TNEVGSSINEVGSSDENIQAELGRNRGPKLNAMLRLGVLQPEVYKQSLPG 1100
1101 SNCKHPEIKKQEYEEVVQTVNTDFSPCLISDNLEQPMGSSHASQVCSETP 1150
1151 DDLLDDGEIKEDTSFAENDIKESSAVFSKSVQRGELSRSPSPFTHTHLAQ 1200
1201 GYRRGAKKLESSEENLSSEDEELPCFQHLLFGKVSNIPSQSTRHSTVATE 1250
1251 CLSKNTEENLLSLKNSLNDCSNQVILAKASQEHHLSEETKCSASLFSSQC 1300
1301 SELEDLTANTNTQDPFLIGSSKQMRHQSESQGVGLSDKELVSDDEERGTG 1350
1351 LEENNQEEQSMDSNLGEAASGCESETSVSEDCSGLSSQSDILTTQQRDTM 1400
1401 QDNLIKLQQEMAELEAVLEQHGSQPSNSYPSIISDSSALEDLQNPEQSTS 1450
1451 EKAVLTSQKSSEYPISQNPEGLSADKFEVSADSSTSKNKEPGVERSSPSK 1500
1501 CPSLDDRWYMHSCSGSLQNRNYPSQEELIKVVDVEEQQLEESGPHDLTET 1550
1551 SYLPRQDLEGTPYLESGISLFSDDPESDPSEDKAPESAHVGNIPSSTSAL 1600
1601 KVPQLKVAESAQSPAAAHTTNTAGYNAMEESVSREKPELTASTERVNKRM 1650
1651 SMVVSGLTPEEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERT 1700
1701 LKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPK 1750
1751 RARESQDRKIFRGLEICCYGPFTNMPTDQLEWMVQLCGASVVKELSSFTL 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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