 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5BMR2 from www.uniprot.org...
The NucPred score for your sequence is 0.71 (see score help below)
1 MPGPDDDVREPTAAARTNNSGYGLRAAPSSITSGLDSVVSVNSYRSSELN 50
51 AEEVNVDDQECADSDILESPNDGMTIMNDDDADLSEEEEEVMENSIDFSV 100
101 NRATVTKAGVIPCCVFVGRVRWGTTDWEIYFGQKQLLRLHFNLHLYSLFN 150
151 RHKLLRGVHLPCTIWREKRAEKRVMDVYVVQDYIRQLLKDRDLRNSEPLL 200
201 SFLEVSPSRAMLRLGPSLKEGYVHMRINGPFQLPLYTCFNRTIEALYRHL 250
251 YRAWMRIAFVSAIVGFIFPICLVIVTSLPTFFSPQKELVNSDSGTKEVST 300
301 KLNTAGVFLGLAVLGGILFFAVFVYKFFQHRLGVIRRWVVLKPSCFAAYR 350
351 NRNDREPSEVFLFDKNFTARKGSYRQGVSWMPSGLVVGSKAGDIEIDTGH 400
401 YYTRLTAFVALMGVCYGIMRLSNSIYDFEYLSLDKSMGVPITNASGYENW 450
451 TESRDAEYCGYYFIVPKGTTVYVESKDDSAVISQLQMPSSNSMTANLGFF 500
501 WQSDSMGYSLNVITNAVGAGTMVSVVKPIDAKNDRFFESGTNYSMPTVGN 550
551 LTLEGVKTDIDVQSFSNVESFCAITVRIAPLTWRSYVYYILFLFAGGIIA 600
601 PVVGFLANYFVTYLGIWHPHVRRDHWFRCVRRLQKLKRQETSTRFNSFAP 650
651 QHISTIDDDESATITAKAKAAKLAIGNTPTSNINQVVDSVVKQEAETASR 700
701 MGTGNLAPASSRVDSSTQSEDSFEAPKPPPSSVSWHVDAEDTYAAMYKAI 750
751 SNAKYEILIAGWWVCPDLFLLRPGRKLPPREADEDPDGQQVNKTMLRQVL 800
801 MKKAEAGVKIYVLIYREVKLALTLNSAYTKRSLMVHPNIRVLRDPIFQIQ 850
851 SLGFWSHHEKIVCIDQSLAFVGGLDLCFGRYDHHGHPISDPSDDPVWTGK 900
901 DYSNPIIKDFVRVNKPFEDLIDRASQPRMPWHDVHCSISGPPVQDVAYHL 950
951 IQRWNFVCSKNDYQLRTGWCICFRSRRFKFLPKCLVPMDFNGWTLQYPSS 1000
1001 DPEPMRDGSTRTTIPLVREDSLSMVEPFQVVQSVNPMYPCTATGPQWPPT 1050
1051 SSLHPINPMRPPLTSASTTTGPLDDGEVLRAQRGESILQVFHPSANICNI 1100
1101 QVCRSVSMWSAGVPTEASIQAAYMDVIANSKHFLYIENQFFVSGMDGNGI 1150
1151 VRNRILQALVDRIERAVQRDEKFRVYVVMPLLPAFEGNIRSHELTNLHAV 1200
1201 MHWQFATICRGRYSLFEALKGVTNHPENYVAFFGLRKYGIMPNGCAATEQ 1250
1251 IYIHSKLMIADDRCAILGSANINDRSMNGDRDSEIALVIEDMQYEDGVMN 1300
1301 EKPYRRGVAASKLRLQLFREHLGLADDDLSVADPTSDHTWQAIKSTASSN 1350
1351 TKIFEAVFDCAPSNRMRAFVNFQSIEVTQIFENQRMNVLKVPGRSHVWDA 1400
1401 QNLKDGDYAPWTDVNGVPIAADRVDLRDFEVDNYRDKKKKLFSMDHDGWC 1450
1451 YARNFSIFQEVRTMKTDYKKREKLQHLVADRLMAQVRRRRWVKKGLLPPR 1500
1501 DPRESSFSLASDDEEHGRFYSLWRRLQQGDFSRSNSVSTPTNFSQLDTDG 1550
1551 GISGSVSVGGTNHGRRLYNSNSMPSNASILGDNPTTRPPVLGDAPSYPNS 1600
1601 PSVASFQHTPQSPAIATGARSVRSARTSSLLCTGAGVRTRGNTRSARGSF 1650
1651 YGMFSVTGNRNLDTDDESSDAGSEYGGGHGIRASLKRWYSTMDVLDFGRR 1700
1701 SKFNAEYFDTDEDHLHSDDPLLEDGRGSYHAATREGLLTEEAVEGSDDED 1750
1751 AECQIGHVQTAATVRKEDETRARAQLSEIRGHLVEFPLDFLVEEILKPSV 1800
1801 LPADIHI 1807
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.) |
Go back to the NucPred Home Page.