 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9HFR4 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MAAWMPQNIQKRLLRYVLQQLSLFAEIDLPNLDVSLGTNSKVHLKELQLD 50
51 PDKVDIPNFYLRNGVVDDMDLTLTLSNGVNIEATGVNLTLAPSMTNGVDF 100
101 DPGKLSFSLYQSTADLATSVFFVDQDNNELPKTDKELDDEHDSSKNVAND 150
151 DDSSPLADKENPKQPSKMGNMMAKAVEMALSRLQVQLKDINLTIITDEST 200
201 FQICIEEASFDTTDGVRKIEFKGAELSVLKPSVYPGDQPQGESQDDSEDD 250
251 DSVDESSSFDSHPTDFMSSSFIGSREELENSMLNSQSSVYMSATSKLLTK 300
301 PKHSTRHPSETKPSVTLGHVNEGTLTFEGLQSFENVSIIIDQIHVAVSPI 350
351 PESLTALLETLSKRLKILRVHSAKSKPSTTTTYTFSSKDGLQEEPEFSDQ 400
401 ISTSSSTGFKELKINEIVISLNSALGKNGAFAIDNSLCLSIESLELAMVS 450
451 EGNLSGTLKTLKMNKSETSEVFSFQPGSNYDVRFEMILDDDNDLYNITIL 500
501 CPKPGVFNLNEEVLNHLLSFCSKIPSTLHSLQSYQYQKAKLASLSNPQSN 550
551 SNNDQENEIYLQTASFEFILQLTSSDKVKLTSFPLSFNSNTGVIEVERIV 600
601 VERKFKENQVQGDPPSQDRFKMQPHKSTNSGCLTCRKRQVKCDERKPFCL 650
651 NCEKSEQKCTGFTHLSKDLPSSSSSLSSDDSYKSILLINGLKTYLFKKPK 700
701 KVKTFDKSGIQCSYLSPNASEIELIELEMTMSDFTSLIDSLELFFEQLKV 750
751 VNHPKMDIAKEKTVFRTSFNSRNPVRNPRALGSQTGAMFSHSSLVSFLRI 800
801 NSVSFLLKEALNGFGDVSGRIKSINCNFHVKGFQDFFVGSLDVARIHKLS 850
851 SQLILEKVDAPGNKKPMLIARLKSSSLTIQLADCLAHYYGEWFSFLHQCN 900
901 MKEREIVKRQEDIGPKDRARDSNNFVISLHLNNIIIELNPVSLKSKAVLF 950
951 IKKGCGTVTFGSQRLSISSSLTEITPLLIDDTKNLKNEIKLDSKNSHPLG 1000
1001 NSFLLLLMDMGYISMGMLSSVSVSFETESSASLIAPVNVNVMCDTLRLDI 1050
1051 CADSLQCLLNLIKDLRQPIIVPFEQKYRTQPETPLDALKDVDFDAFLPQK 1100
1101 TKDSNNVAATKTRTFSETALNGAFRPKVYSDGSLEIIDNYYEHESVNQGL 1150
1151 DNRTYGDLEVFEENSEDDISLENSLNMKHDHFYHTSFMDNTRNTEHVPFK 1200
1201 LNLSVEAVYIRLFDGYDWKETRVSILNAVRRVEAKAAAELSRIEKIRNTK 1250
1251 LRRDSSASNSGNEGSLQTDDDSATSGSVSEPVIEETLYQSIHLSLPVGME 1300
1301 PTLLAERVNNDVNFKRASSDSLDKTQSPSSVSSKSQVSSPQTRRLKLKRS 1350
1351 TFHKVALELRNLSITFKLLSSFDPTTIKNKSSIRNPNSAEIVNRISVEVD 1400
1401 DFLITDNIPTSIWKTFVTYLKEAGSRELNSKMVHLDIATIRPTRLLATTE 1450
1451 MRIETNVLPLRLYVDQDTLDFLTRFGEFKDLRFVPQVVDDDDLFVSKLHV 1500
1501 FPVRLKLDYKPKTVDYAGIRSGKTSEFMNFFILDEASIGLRKGWTWIGGL 1550
1551 NNQVNSHTKREYGTPDITRNQLKGVLSGLASINSVVKISSGFKDLIAIPM 1600
1601 EEYRRDGRLITGVRKGAFSFAKTTGNELLKLGVKLVAGTQTILENTEQVL 1650
1651 GGEGNLARMPVQVKERKQRRDSDEGNFYVITDRQTYERNLKKTSMNESII 1700
1701 GKTSLREANRFDEEYDDDDDDEDDLVNKIKPHLRNTMLDSQLFLDDNEIE 1750
1751 DEXRGARRPDESQKTISLYANQPTTMKEGLQLAYQSFGRNIDSAKRAISK 1800
1801 ASSKVSEDNRTSNIAYEVMKATPVVVLRPIIGTTEAVSKALLGGLNQFDP 1850
1851 DNRSKSEDKYKQ 1862
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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