 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P97927 from www.uniprot.org...
The NucPred score for your sequence is 0.48 (see score help below)
1 MGWSTAWCSVLALWLLWCAVCSNAASGDGNAFPFDIEGSAVVGRQDPSET 50
51 SDSGVTLGRLPPAAERCDAGFFRTLSGECAPCDCNGNSHECLDGSGFCLH 100
101 CQRNTTGEHCEKCLDGYIGDSIRGTPRFCQPCPCPLPHLANFAESCYRKN 150
151 GAVRCICKENYVGPNCERCAPGYYGNPLLIGSTCKKCDCSGNSDPNLIFE 200
201 DCDEITGQCRNCLRNTTGFKCERCAPGYYGDARTAKNCAVCNCGGGPCDS 250
251 VTGECLEEGFEVPTGCDKCVWDLTDDLRLAALSIEESKSGLLSVSSGAAA 300
301 HRHVTDMNSTIHLLRTRLSERENQYTLRKIQINNSENTLRSLLPDVEGLH 350
351 EKGSQASRKGMLVEKESMDTIDQATHLVEQAHNMRDKIQEINSKMLYYGE 400
401 NQELGPEEIAEKLVLAQKMLEEIRSRQPFLTHRELVDEEADEAQELLSQA 450
451 ENWQRLHNDTRSLFPVVLEQLDDYNAKLSDLQESINQALDHVRDAEDMNR 500
501 AITFKQRDHEKQHERVKEQMEVVGASLSMSADSLTIPQLTLEELDEIIKN 550
551 ASGIYAEIDGAKNELQGKLSNLSNLSHDLVQEATDHAYNLQQEADELSRN 600
601 LHSSDMNGLVQKALDASNVYENIANYVSEANETAELALNITDRIYDAVSG 650
651 IDTQIIYHKDESDNLLNQARELQAKADSSNDEAVADTSRRVGGALWRKGA 700
701 LRDRLNDAVKQLQAAERGDAHQRLGQSKLFIEEANKTTAAVQQVTTPMAN 750
751 NLSNWSQNLQTFDSSAYNTAVDSARDAVRNLTEVVPQLLDQLRTVEQKRP 800
801 ASNISASIQRIRELIAQTRSVASKIQVSMMFDGQSAVEVHPKVSVDDLKA 850
851 FTSISLYMKPPPKPAEPTGAWVADQFVLYLGSKNAKKEYMGLAIKNDNLV 900
901 YVYNLGMKDVEILLDSKPVSSWPAYFSIVKIERVGKHGKVFLTVPSLSST 950
951 AEEKFIKKGEFAGDDSLLDLTPEDTVFYVGGVPANFKLPASLNLPSYSGC 1000
1001 LELATLNNDVISLYNFKHIYNMDPSKSVPCARDKLAFTQSRAASYFFDGS 1050
1051 SYAVVRDITRRGKFGQVTRFDIEIRTPADNGLVLLMVNGSMFFSLEMRNG 1100
1101 YLHVFYDFGFSNGPVHLEDTLKKAQINDAKYHEISIIYHNDKKMILVVDR 1150
1151 RHVKSTDNEKKKIPFTDIYIGGAPQEVLQSRTLRAHLPLDINFRGCMKGF 1200
1201 QFQKKDFNLLEQTETLGVGYGCPEDSLISRRAYFNGQSFIASIQKISFFD 1250
1251 GFEGGFNFRTLQPNGLLFYYTSGSDVFSISLDNGTVVMDVKGIKVMSTDK 1300
1301 QYHDGLPHFVVTSISDTRYELVVDKSRLRGKNPTKGKAEQTQTTEKKFYF 1350
1351 GGSPISPQYANFTGCISNAYFTRLDRDVEVEDFQRYSEKVHTSLYECPIE 1400
1401 SSPLFLLHKKGKNSSKPKTNKQGEKSKDAPSWDPIGLKFLEQKAPRDSHC 1450
1451 HLSSSPRAIEHAYQYGGTANSRQEFEHEQGDFGEKSQFAIRLKTRSSHGM 1500
1501 IFYVSDQEENDFMTLFLAHGRLVFMFNVGHKKLKIRSQEKYNDGLWHDVI 1550
1551 FIREKSSGRLVIDGLRVLEERLPPSGAAWKIKGPIYLGGVAPGRAVKNVQ 1600
1601 ITSVYSFSGCLGNLQLNGASITSASQTFSVTPCFEGPMETGTYFSTEGGY 1650
1651 VVLDESFNIGLKFEIAFEVRPRSSSGTLVHGHSVNGEYLNVHMRNGQVIV 1700
1701 KVNNGVRDFSTSVTPKQNLCDGRWHRITVIRDSNVVQLDVDSEVNHVVGP 1750
1751 LNPKPVDHREPVFVGGVPESLLTPRLAPSKPFTGCIRHFVIDSRPVSFSK 1800
1801 AALVSGAVSINSCPTA 1816
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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