 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P41950 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MLAHTHRINKCLYGQNQMRNRHALLGALPPIFLLLLPLISCMKFDPERIA 50
51 ARLRIDEKWDQLDAFQSIKSRRGRQIQPKEISIQVTAPLFSSRLFDYGTT 100
101 AGDEELPQALDVGKKLDLVHPISFFGSDYKTIYILSNGAVGFEASSRSYK 150
151 SGILPSSTRFLAPFWNRNDLRNGGKVYYREVTKGRVLERGQSEIRYQYDK 200
201 NVKVKSALIITWDKMQPLNTAALPEENTNTFQAAIFITANGTFANFIYSN 250
251 IGWTQGAEAGFNAGDATNHFKLPTSGTPNIMYLEEYGNTGIPGEWMFELS 300
301 ELRVISCKSGIKGDTCDQECSNGEWGPDCAYCCHCSEGTCHPISGDCQRG 350
351 CATCWDGVACQTRQEKCATKTQCASNALSFNDYDRCGEPIQRCQCLNGYK 400
401 GDGYNNCEDVDECKTNSTICHKNAICTNTPGRYFCMCKEGFSGDGQNDCS 450
451 QSFLFQYDTHHQLPRKKNSKMEWNLKKPLKIFGETTEKLTVTSTGLIAIN 500
501 EVNRDNGRLEDMQLVGIAPFFGPIDLSRNGAVSVEEVDDVEVLRRVTRTI 550
551 GENYNDPTFVAKSALVVTFSNVTDGRQTKGNTFQALLIDGSNSKNEKMTF 600
601 VELMYRDLPWASGAEAGILSSDASSSILLPASGTEAISQLSKNSNIKQPG 650
651 TWLYRIDKAQLMPCAQPIQVPPYCDRLLSTAPRLPSKLLEEKKEGLTLPS 700
701 PGAFLVDQPSETIVPTLVRGGGTVTRGRNVLTVTTSPIGNQQRQQTTKAV 750
751 TRPRPNFSSTPHRPIVSLSDEDFELGPDAFEVTFPPFVTVQPELFRPNQR 800
801 NGVQKSTQRPLPDFSIRTPLKEEATTSVPREKTSSAAPAHSPIEEMSENE 850
851 ESPFEAGSFDGEAVKFNEELEAIDKALQTTKKQRPELSVTPQPEDLSGDA 900
901 RVIETTEEDEEEAEISTETTTEMSSTTTTTKAHTTTTTMMIPTEAPPSIF 950
951 VFTTTQKPRAQSTTQKRIIVQQPSIVVNSQPPKQRNDNQPTVNVGHAEEQ 1000
1001 SPRLAILLPVMIILAWLVILVCIGAVVCCKRRNSRESSQLRAMYGAAYGV 1050
1051 RPTAYESKRKESTYEDHLERAARLSGQPALSGQQAGKVSLYGSYWNLEPL 1100
1101 SNHSPARLSTQERQSPPSFVNNGYTNQTTRYTYAGHY 1137
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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