 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q05707 from www.uniprot.org...
The NucPred score for your sequence is 0.21 (see score help below)
1 MKIFQRKMRYWLLPPFLAIVYFCTIVQGQVAPPTRLRYNVISHDSIQISW 50
51 KAPRGKFGGYKLLVTPTSGGKTNQLNLQNTATKAIIQGLMPDQNYTVQII 100
101 AYNKDKESKPAQGQFRIKDLEKRKDPKPRVKVVDRGNGSRPSSPEEVKFV 150
151 CQTPAIADIVILVDGSWSIGRFNFRLVRHFLENLVTAFDVGSEKTRIGLA 200
201 QYSGDPRIEWHLNAFSTKDEVIEAVRNLPYKGGNTLTGLALNYIFENSFK 250
251 PEAGSRTGVSKIGILITDGKSQDDIIPPSRNLRESGVELFAIGVKNADVN 300
301 ELQEIASEPDSTHVYNVAEFDLMHTVVESLTRTLCSRVEEQDREIKASAH 350
351 AITGPPTELITSEVTARSFMVNWTHAPGNVEKYRVVYYPTRGGKPDEVVV 400
401 DGTVSSTVLKNLMSLTEYQIAVFAIYAHTASEGLRGTETTLALPMASDLL 450
451 LYDVTENSMRVKWDAVPGASGYLILYAPLTEGLAGDEKEMKIGETHTDIE 500
501 LSGLLPNTEYTVTVYAMFGEEASDPVTGQETTLALSPPRNLRISNVGSNS 550
551 ARLTWDPTSRQINGYRIVYNNADGTEINEVEVDPITTFPLKGLTPLTEYT 600
601 IAIFSIYDEGQSEPLTGVFTTEEVPAQQYLEIDEVTTDSFRVTWHPLSAD 650
651 EGLHKLMWIPVYGGKTEEVVLKEEQDSHVIEGLEPGTEYEVSLLAVLDDG 700
701 SESEVVTAVGTTLDSFWTEPATTIVPTTSVTSVFQTGIRNLVVGDETTSS 750
751 LRVKWDISDSDVQQFRVTYMTAQGDPEEEVIGTVMVPGSQNNLLLKPLLP 800
801 DTEYKVTVTPIYTDGEGVSVSAPGKTLPSSGPQNLRVSEEWYNRLRITWD 850
851 PPSSPVKGYRIVYKPVSVPGPTLETFVGADINTILITNLLSGMDYNVKIF 900
901 ASQASGFSDALTGMVKTLFLGVTNLQAKHVEMTSLCAHWQVHRHATAYRV 950
951 VIESLQDRQKQESTVGGGTTRHCFYGLQPDSEYKISVYTKLQEIEGPSVS 1000
1001 IMEKTQSLPTRPPTFPPTIPPAKEVCKAAKADLVFMVDGSWSIGDENFNK 1050
1051 IISFLYSTVGALNKIGTDGTQVAMVQFTDDPRTEFKLNAYKTKETLLDAI 1100
1101 KHISYKGGNTKTGKAIKYVRDTLFTAESGTRRGIPKVIVVITDGRSQDDV 1150
1151 NKISREMQLDGYSIFAIGVADADYSELVSIGSKPSARHVFFVDDFDAFKK 1200
1201 IEDELITFVCETASATCPVVHKDGIDLAGFKMMEMFGLVEKDFSSVEGVS 1250
1251 MEPGTFNVFPCYQLHKDALVSQPTRYLHPEGLPSDYTISFLFRILPDTPQ 1300
1301 EPFALWEILNKNSDPLVGVILDNGGKTLTYFNYDQSGDFQTVTFEGPEIR 1350
1351 KIFYGSFHKLHIVVSETLVKVVIDCKQVGEKAMNASANITSDGVEVLGKM 1400
1401 VRSRGPGGNSAPFQLQMFDIVCSTSWANTDKCCELPGLRDDESCPDLPHS 1450
1451 CSCSETNEVALGPAGPPGGPGLRGPKGQQGEPGPKGPDGPRGEIGLPGPQ 1500
1501 GPPGPQGPSGLSIQGMPGMPGEKGEKGDTGLPGPQGIPGGVGSPGRDGSP 1550
1551 GQRGLPGKDGSSGPPGPPGPIGIPGTPGVPGITGSMGPQGALGPPGVPGA 1600
1601 KGERGERGDLQSQAMVRSVARQVCEQLIQSHMARYTAILNQIPSHSSSIR 1650
1651 TVQGPPGEPGRPGSPGAPGEQGPPGTPGFPGNAGVPGTPGERGLTGIKGE 1700
1701 KGNPGVGTQGPRGPPGPAGPSGESRPGSPGPPGSPGPRGPPGHLGVPGPQ 1750
1751 GPSGQPGYCDPSSCSAYGVRAPHPDQPEFTPVQDELEAMELWGPGV 1796
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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