 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O62653 from www.uniprot.org...
The NucPred score for your sequence is 0.57 (see score help below)
1 MARKKSSGLKITLIVLLAIVTIIAIALVAILPTKTPAVELVSTIPGKCPS 50
51 AENDRLDEKINCIPDQFPTQALCAMQGCCWNPRNESPTPWCSFANNHGYE 100
101 FEKISNPNINFEPNLKKNSPPTLFGDNITNLLLTTQSQTANRFRFKITDP 150
151 NNQRYEVPHQFVNKDFSGPPASNPLYDVKITENPFSIKVIRKSNNKILFD 200
201 TSIGPLVYSNQYLQISTKLPSKYIYGLGEHVHKRFRHDLYWKTWPIFTRD 250
251 QLPGDNNNNLYGHQTFFMSIEDTSGKSFGVFLMNSNAMEVFIQPTPIVTY 300
301 RVIGGILDFYIFLGDTPGQVVQQYQELTGRPAMPSYWSLGFQLSRWNYGS 350
351 LDAVKEVVKRNRDARIPFDAQVTDIDYMEDKKDFTYNNKTFYGLPEFVKD 400
401 LHDHGQKYIIILDPAISITSLANGNHYKTYERGNEQKVWVYQSDGTTPLI 450
451 GEVWPGLTVYPDFTNPKCLDWWTNECSIFHEEIKYDGLWIDMNEVSSFVH 500
501 GSTKGCSDNKLNYPPFIPDILDKLMYAKTICMDAIQHWGKQYDVHSLYGY 550
551 SMAIATEKAIEKVFPNKRSFILTRSTFAGTGKHATHWLGDNTPSWEHMEW 600
601 SITPMLEFGLFGMPFIGADICGFVVDTTEELCRRWMQIGAFYPYFRDHNA 650
651 GGYMPQDPAYFGQDSLLVNTSRHYLDIWYTLLPYLYNLLYKAYVYGETVA 700
701 RPFLYEFYEDTNSWIEDLQFLWGSALLITPVLRQGADRMSAYIPDATWYD 750
751 YETGGKRTWRKQRVEMYLPGDKIGLHVRGGYIIPTQQPAVNTTASRKNPL 800
801 GLIIALDNNAAKGDFFWDDGESKDSIEKGKYILYTFSVLNNELDIICTHS 850
851 SYQEGTTLAFETIKILGLANTVTQVQVAENNQQTIIHNSFTYHASNQSLI 900
901 IDNLKLNLGKNFTVQWNQVSLDSEKIDCFPDNNPENKQNCEERGCLWEPN 950
951 SAAEGPRCYFPKQYNPYLVKSTQYSSMGITVDLELNTATARIKMPSNPIS 1000
1001 VLRLEVKYHKNDMLQFKIYDPQNKRYEVPIPMDIPTTPTSTYENRLYDVN 1050
1051 IKGNPFGIQIRRRSTGRIFWDSCLPWGLLLMNQFIQISTRLPSEYVYGFG 1100
1101 GVGHRQFKQDLNWHKWGMFNRDQPSGYKISSYGFQPYIYMALGDGGNAHG 1150
1151 VFLLNSNAMDVTFQPNPALTYRTIGGILDFYMFLGPNPEVATKQYHEVIG 1200
1201 RPVKPPYWALGFHLCRYGYENTSEIRQLYEDMVSAQIPYDVQYTDIDYME 1250
1251 RQLDFTIGKGFQDLPEFVDKIRDEGMKYIIILDPAISGNETQDYLAFQRG 1300
1301 IEKDVFVKWPNTQDICWAKVWPDLPNITIDDSLTEDEAVNASRAHVAFPD 1350
1351 FLKTSTAEWWATEIEDFYNTYMKFDGLWIDMNEPSSFVHGSVDNKCRNEI 1400
1401 LNYPPYMPALTKRNEGLHFRTMCMETQQTLSNGSSVLHYDVHNLYGWSQA 1450
1451 KPTYDALQKTTGKRGIVISRSTYPSAGRWAGHWLGDNYANWDKIGKSIIG 1500
1501 MMEFSLFGISFTGADICGFFNNSDYELCARWMQVGAFYPYSRNHNITDTR 1550
1551 RQDPVSWNETFASMSTDILNIRYNLLPYFYTQMHDIHANGGTVIRPLLHE 1600
1601 FFSETGTWDIYKQFLWGPAFMVTPVVEPYSESVTGYVPDGRWLDYHTGQD 1650
1651 IGLRKRLHTLDAPLYKINLHVCGGHILPCQEPAQNTYFSRQNYMKLIVAA 1700
1701 DDNQTAQGYLFWDDGESIDTYEKGQYLLVQFNLNKATLTSTILKNGYINT 1750
1751 REMRLGFINVWGKGNTVVQEVNITYKGNKESVKFSQEANKQILNIDLTAN 1800
1801 NIVLDEPIEISWT 1813
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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