 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6J6I9 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MDLSAVRVEEVQNVINAMQKILECPICLELIKEPVSTKCDHIFCRFCMLK 50
51 LLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIIHAFQLDTGLQ 100
101 FANSYNFAKKENHSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQET 150
151 SLSVPLSNLGIVRTLRTKQQIQPQKKSVYIELGSDSSEDTVNKATYCSVG 200
201 DQELLQITPQGTRDETSLDSAKKAACEFSEKDITNTEHHQSSNNDLNTTE 250
251 KHATERHPEKYQGSSVSNLHVEPCGTNTHASSLQHENSLLLTKDRMNVEK 300
301 AEFCNKSKQPGLARSQHNRWTGSKETCNDRQTPSTEKKVDLNANALYERK 350
351 EWNKQKLPCSENPRDAEDVPWITLNSSIQKVNEWFSRSDELLSSDDSHDG 400
401 GSESNAKVADVLDVLNEVDEYSGSSEKIDLLASDPHEPLICKSERVHSSS 450
451 VESNIKDKIFGKTYRRKANLPNLSHVTENLIIGALVTESQIMQERPLTNK 500
501 LKRKRRTTSGLHPEDFIKKADLAVQKTPEIINQGTNQMEQNGQVMNITNS 550
551 AHENKTKGDSIQNEKNPNAIESLEEESAFKTKAEPISSSINNMELELNIH 600
601 NSKAPKKNRLRRKSSTRHIHALELVVSRNLSPPNCTELQIDSCSSSEEIK 650
651 KKNYNQMPVRHSRNLQLMEDKESATGAKKSNKPNEQTSKRHASDTFPELK 700
701 LTKVPGSFTNCSNTSELKEFVNPSLSREEKEEKLETVKVSNNAKDPKDLM 750
751 LSGERVLQTERSVESSSISLVPDTDYGTQESISLLEVSTLGKAKTERNKC 800
801 MSQCAAFENPKELIHGCSEDTRNDTEGFKYPLGSEVNHSQETSIEIEESE 850
851 LDTQYLQNTFKVSKRQSFALFSNPGNPEEECATFSAHSRSLKKQSPKVTS 900
901 ECEQKEENQGKKESNIKPVQTVNITAGFSVVCQKDKPVDNAKCSIKGGSR 950
951 FCLSSQFRGNETGLITPNKHGLLQNPYHIPPLFPVKSFVKTKCNKNLLEE 1000
1001 NSEEHSVSPERAVGNENIIPSTVSTISHNNIRENAFKEASSSNINEVGSS 1050
1051 TNEVGSSINEVGPSDENIQAELGRNRGPKLNAVLRLGLLQPEVCKQSLPI 1100
1101 SNCKHPEIKKQEHEELVQTVNTDFSPCLISDNLEQPMGSSHASEVCSETP 1150
1151 DDLLDDGEIKEDTSFAANDIKESSAVFSKSIQRGELSRSPSPFTHTHLAQ 1200
1201 GYQKEAKKLESSEENLSSEDEELPCFQHLLFGKVSNIPSQTTRHSTVATE 1250
1251 CLSKNTEENLLSLKNSLTDCSNQVILAKASQEHHLSEETKCSGSLFSSQC 1300
1301 SELEDLTANTNTQDPFLIGSSKRMRHQSESQGVGLSDKELVSDDEERGTG 1350
1351 LEEDNQEEQSVDSNLGEAASGYESETSVSEDCSRLSSQSEILTTQQRDTM 1400
1401 QDNLIKLQQEMAELEAVLEQHGSQPSNSYPSIITDSSALEDLRNPEQSTS 1450
1451 EKAVLTSQKSSEYPINQNPEGLSADKFEVSADSSTSKNKEPGVERSSPSK 1500
1501 CQSLEDRWYVHSSSGSLQNGNYPSQEELIKVVDVETQQLEKSGPHDLMEP 1550
1551 SYLPRQDLDGTPYLESGISLFSDDPESDPSEDRAPESAHVGSIPSSTSAL 1600
1601 KVPQWQVAESAQSPAAAHNTNTAGYNAMEESVSRENPKLTASTERVNKRM 1650
1651 SLVVSGLTPEEFMLVYKFARRYHIALTNLISEETTHVVMKTDAEFVCERT 1700
1701 LKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPK 1750
1751 RARESPDRKIFRGLEICCYGPFTNMPTDQLEWMVQLCGASVVKELSSFTL 1800
1801 GTGFHPIVVVQPDAWTEDNGFHAIGQMCEAPVVTREWVLDSVALYQCQEL 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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