 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O95602 from www.uniprot.org...
The NucPred score for your sequence is 0.82 (see score help below)
1 MLISKNMPWRRLQGISFGMYSAEELKKLSVKSITNPRYLDSLGNPSANGL 50
51 YDLALGPADSKEVCSTCVQDFSNCSGHLGHIELPLTVYNPLLFDKLYLLL 100
101 RGSCLNCHMLTCPRAVIHLLLCQLRVLEVGALQAVYELERILNRFLEENP 150
151 DPSASEIREELEQYTTEIVQNNLLGSQGAHVKNVCESKSKLIALFWKAHM 200
201 NAKRCPHCKTGRSVVRKEHNSKLTITFPAMVHRTAGQKDSEPLGIEEAQI 250
251 GKRGYLTPTSAREHLSALWKNEGFFLNYLFSGMDDDGMESRFNPSVFFLD 300
301 FLVVPPSRYRPVSRLGDQMFTNGQTVNLQAVMKDVVLIRKLLALMAQEQK 350
351 LPEEVATPTTDEEKDSLIAIDRSFLSTLPGQSLIDKLYNIWIRLQSHVNI 400
401 VFDSEMDKLMMDKYPGIRQILEKKEGLFRKHMMGKRVDYAARSVICPDMY 450
451 INTNEIGIPMVFATKLTYPQPVTPWNVQELRQAVINGPNVHPGASMVINE 500
501 DGSRTALSAVDMTQREAVAKQLLTPATGAPKPQGTKIVCRHVKNGDILLL 550
551 NRQPTLHRPSIQAHRARILPEEKVLRLHYANCKAYNADFDGDEMNAHFPQ 600
601 SELGRAEAYVLACTDQQYLVPKDGQPLAGLIQDHMVSGASMTTRGCFFTR 650
651 EHYMELVYRGLTDKVGRVKLLSPSILKPFPLWTGKQVVSTLLINIIPEDH 700
701 IPLNLSGKAKITGKAWVKETPRSVPGFNPDSMCESQVIIREGELLCGVLD 750
751 KAHYGSSAYGLVHCCYEIYGGETSGKVLTCLARLFTAYLQLYRGFTLGVE 800
801 DILVKPKADVKRQRIIEESTHCGPQAVRAALNLPEAASYDEVRGKWQDAH 850
851 LGKDQRDFNMIDLKFKEEVNHYSNEINKACMPFGLHRQFPENSLQMMVQS 900
901 GAKGSTVNTMQISCLLGQIELEGRRPPLMASGKSLPCFEPYEFTPRAGGF 950
951 VTGRFLTGIKPPEFFFHCMAGREGLVDTAVKTSRSGYLQRCIIKHLEGLV 1000
1001 VQYDLTVRDSDGSVVQFLYGEDGLDIPKTQFLQPKQFPFLASNYEVIMKS 1050
1051 QHLHEVLSRADPKKALHHFRAIKKWQSKHPNTLLRRGAFLSYSQKIQEAV 1100
1101 KALKLESENRNGRSPGTQEMLRMWYELDEESRRKYQKKAAACPDPSLSVW 1150
1151 RPDIYFASVSETFETKVDDYSQEWAAQTEKSYEKSELSLDRLRTLLQLKW 1200
1201 QRSLCEPGEAVGLLAAQSIGEPSTQMTLNTFHFAGRGEMNVTLGIPRLRE 1250
1251 ILMVASANIKTPMMSVPVLNTKKALKRVKSLKKQLTRVCLGEVLQKIDVQ 1300
1301 ESFCMEEKQNKFQVYQLRFQFLPHAYYQQEKCLRPEDILRFMETRFFKLL 1350
1351 MESIKKKNNKASAFRNVNTRRATQRDLDNAGELGRSRGEQEGDEEEEGHI 1400
1401 VDAEAEEGDADASDAKRKEKQEEEVDYESEEEEEREGEENDDEDMQEERN 1450
1451 PHREGARKTQEQDEEVGLGTEEDPSLPALLTQPRKPTHSQEPQGPEAMER 1500
1501 RVQAVREIHPFIDDYQYDTEESLWCQVTVKLPLMKINFDMSSLVVSLAHG 1550
1551 AVIYATKGITRCLLNETTNNKNEKELVLNTEGINLPELFKYAEVLDLRRL 1600
1601 YSNDIHAIANTYGIEAALRVIEKEIKDVFAVYGIAVDPRHLSLVADYMCF 1650
1651 EGVYKPLNRFGIRSNSSPLQQMTFETSFQFLKQATMLGSHDELRSPSACL 1700
1701 VVGKVVRGGTGLFELKQPLR 1720
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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