 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8WNU4 from www.uniprot.org...
The NucPred score for your sequence is 0.71 (see score help below)
1 MQPATQLQFTNHLSPNGQCILQPPPTPSLPDKMEKAPPQPQHEGLKSEEH 50
51 LPQQPAAAKTVSRHIPRLRAVVESQAFKNILVDEMDMMHARAATLIQANW 100
101 RGYRLRQKLISQMTAAKAIQEAWRRFNKRHILHSSKLLVKKVRAEDGNIP 150
151 YHAPQQVRFQHPEENCLLSPPVMVNKETQFPSYDNLVLCRPQSSPLLKPP 200
201 AAQGTPEPCVQAPHAAGVRGVAFLPHQTVTIRFPCPVSLDAKCQSCLLTR 250
251 TIRSTCLVHIEGDSVKTKRVTARTNKAGVPETPLSRRYDKAVMGPSRAQT 300
301 QGPVEAETPKAPFQICPGPVITKTLLQTYPVVSMTLPQTYSASTTTTTPH 350
351 KTSPVPKITITKTPVQMYPGPTVMTKTAPHTCPMPTMTKIQVHSTASRTG 400
401 TPRQTCRATITAKHQPQVSLLASIMKSPPQVCPGPAMAKTPPQTHPVATP 450
451 AKSPLQTCLAATTSNTSSQMSPVGLTKPSRQTRLAAMITKTPAQLRSVAT 500
501 ILKTLCLASPTVANVKAPPQVAVRGSIHDNPPKAKATMNMKQAAEAVKAS 550
551 SPSCLAEGKIRCLAQSRLGTGAPRAPAKLPLEAEKIKTGPQKPVKADMAL 600
601 KTSVAVEVAGAPSWTKVAEEGDKPSHLYVPVDVAVTLPRGQPAAPLTNAS 650
651 SQRHPPCLSQRPLATPLTKASSQGHLPIELTKTPSLAHLVTCLSKMHSQA 700
701 HLATGAMKVQSQVPLATCLTKTQSRGQPIITKRLIPAHQAADLSSNTHSQ 750
751 VLLTGSKVSNQACQHLSGLSAPPWAKPEDRWTQPKPHGHVPGKTTQGGPC 800
801 PAACEVQGTLVPLMAPTGHSTCHVESWGDSGATRAQPSMPSQVVPCQEDT 850
851 GPVDAGVASGQSWKRVWEPARGAASWETRRNKAVVHPRQSGEPMVSMQAA 900
901 EEIRILAVTTIQAGVRGYLVRRRIRVWHRRATVIQATWRGYRMRRNLAHL 950
951 CRATTIIQAAWRGYSTRRDQARHRQMLHPVTWVELGGGARVMSDRSWVQD 1000
1001 GRARTVSDHRCFQSCQAKPCSVCHSLSSRIGSPPSVVMLVGSSPRTCHTC 1050
1051 GRTQPTRVVQGMGRGAGGPGAVSRASAYQRAVPSPRQPRRRDKAATAIQS 1100
1101 AWRGFKIRQQMRQQQMAAKMVQATWRGHHTRSCLKSTEALLGPADPWSSS 1150
1151 QHMHWASSQHTHWPGI 1166
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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