 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O70472 from www.uniprot.org...
The NucPred score for your sequence is 0.74 (see score help below)
1 MGKRAGGAAAAAAAASTSSAAGLEPAAGRGGGPRSAAAGLLGALHLVMTL 50
51 VVAAARAEKEAFIQSESIIEVLRFDDGGLLQTETTLGLGSYQQKSISLYR 100
101 GNCRPIRFEPPMLDFHEQPVGMPKMEKVYLHNPSSEETITLVSISATTSH 150
151 FHASFFQNRKILPGGNTSFDVVFLARVVGNVENTLFINTSNHGVFTYQVF 200
201 GVGVPNPYRLRPFLGARVPVNSSFSPIINIHNPHSEPLQVVEMYSSGGDL 250
251 HLELPTGQQGGTRKLWEIPPYETKGVMRASFSSREADNHTAFIRIKTNAS 300
301 DSTEFIILPVEVEVTTAPGIYSSTEMLDFGTLRTQDLPKVLNLHLLNSGT 350
351 KDVPITSVRPTPQNDAITVHFKPVTLKASESKYTKVASISFDASRAKKPS 400
401 QFSGKITVKAKEKSYSKLEIPYQAEVLDGYLGFDHAATLFHIQDSPADPV 450
451 ERPIYLTNTFSFAILIHDVLLPEEARIMFQVHNFSQPVLILPNESGYIFT 500
501 LFFMPSTSSMHIDNNILLVTNASKFHLPVRVYTGFLDYFVLPPKIEERFI 550
551 DFGVLSATEASSILFAIINSNPIELAIKSWHIIGDGLSIELVATERGNRS 600
601 TVIASLPELERSSLPDQSPVTLASGHFAVFRVKLTAKKLEGVHDGAIQIT 650
651 TDYEILTIPVKAVIAVGSLTCFPKHMVLPPSFPGKIVHQSLNIMNSFSQK 700
701 VKIQQIRSLSEDVRFYYKRLRGNREDLEPGKKSKIANIYFDPGLQCGDHC 750
751 YIGLPFLSKSEPKVQPGVAMQEDLWDADWDAHQSLFKAWMGIKENAGHRL 800
801 NAMFEVNTDLQKNIVSKVSAELSWPSVLSSPRLLKFPLTNTNCSSEEEIS 850
851 LENPADVPVYVQFIPLALYSNPSVFADKLVSRFNLSKVAKLDLRTLEFQV 900
901 YRNSAHPLQSPTGFTEGLSRHFILNLILKPGEKKSVKVKFTPLHNRTVSS 950
951 LIIVRNNLTVMDAVMVQGQGTTENLRVAGKLPGPGSSLRFKITEALLKDC 1000
1001 IDRLKLREPNFTLKRTFKVENTGQLEIRVETIEISGYACEGYGFKVVNCQ 1050
1051 EFALSANASRDIVILFTPDFTASRVIRELKFVTSSGSEFVFVLNASLPYH 1100
1101 MLAACAEALPRPNWELALYIIISGVMSALFLLVIGTAYLEAQGIWEPFRR 1150
1151 RLSFEASNPPFDVGRPFDLRRIVGISSEGNLNTLGCEHSHGRGFYSNASS 1200
1201 RPGTGSHRQCGTSVHPHSSHGSKNSADVDNVRTRNSSSMSSRTSPQAAAS 1250
1251 QSTSKTSPLVSETAAATQGHTASRKSRGAKQGQHSSQHHSHSHSPLEQHS 1300
1301 QPPPPVPQHQEPPPERLSPAPLTHPSHPERASTTRHSSEDSDITSLIEAM 1350
1351 DKDFDHHDSSPLDVFTEQPPSPMSKSKGKGKSLQQRKAKPPKKQEEKEKR 1400
1401 GKGKPQEDELKDALADDDSSSTTTETSNPDTEPLLREDTEKHKGRPAVPE 1450
1451 KQESELSQGKPKSKKLLNAKKEIPTDVKGSSFELPYTPSLENKQRRNLPT 1500
1501 KIPLPTTLASGSKSRNPPKTKGTNKLVENRPVALSKFLPSSQELGNTSSS 1550
1551 EGEKDSPPPEWDAVPVHKPSSSTDSLYKLSLQTLNADIFLKQRQTSPTPA 1600
1601 SPSLPTAPCPFTSRGSYSSVVNSSGSDTKAKQTSSSKSKLTKAASLPGKN 1650
1651 GNPTFAAVAAGYDKSPGGNGFAKISSNKSDFSSSLGISHIPVDSDGSDSS 1700
1701 GLWSPVSNPNSPDFTPLNSFSAFGNSFNLTGAVFSKLSRSCSQSSQRSWN 1750
1751 EFNSGPSYLWDSPATDPSPSWPASSSSPTHTATSILGNSSGLWSTTPFSS 1800
1801 SIWSSNINSNLPFSTPTNALSSISLMGTENSAAAHTPSASGPADDLGQTY 1850
1851 NPWRIWSPTVGRRSSDPWSNSHFPHEN 1877
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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