 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q24292 from www.uniprot.org...
The NucPred score for your sequence is 0.63 (see score help below)
1 MKATLDSVVASETMTRVTFTGNHESKDFAIFHRCRRRSNIKSLYSPPALH 50
51 PSHMLRSSLLILLAIVLLGSSQAASHDQERERKLEVFEGVAVDYQIGYIG 100
101 DFGGIDSGPPYIIVAEAGVETDLAIDRATGEIRTKVKLDRETRASYSLVA 150
151 IPLSGRNIRVLVTVKDENDNAPTFPQTSMHIEFPENTPREVKRTLLPARD 200
201 LDLEPYNTQRYNIVSGNVNDAFRLSSHRERDGVLYLDLQISGFLDRETTP 250
251 GYSLLIEALDGGTPPLRGFMTVNITIQDVNDNQPIFNQSRYFATVPENAT 300
301 VGTSVLQVYASDTDADENGLVEYAINRRQSDKEQMFRIDPRTGAIYINKA 350
351 LDFETKELHELVVVAKDHGEQPLETTAFVSIRVTDVNDNQPTINVIFLSD 400
401 DASPKISESAQPGEFVARISVHDPDSKTEYANVNVTLNGGDGHFALTTRD 450
451 NSIYLVIVHLPLDREIVSNYTLSVVATDKGTPPLHASKSIFLRITDVNDN 500
501 PPEFEQDLYHANVMEVADPGTSVLQVLAHDRDEGLNSALTYSLAETPETH 550
551 AQWFQIDPQTGLITTRSHIDCETEPVPQLTVVARDGGVPPLSSTATVLVT 600
601 IHDVNDNEPIFDQSFYNVSVAENEPVGRCILKVSASDPDCGVNAMVNYTI 650
651 GEGFKHLTEFEVRSASGEICIAGELDFERRSSYEFPVLATDRGGLSTTAM 700
701 IKMQLTDVNDNRPVFYPREYKVSLRESPKASSQASSTPIVAVVATDPDYG 750
751 NFGQVSYRIVAGNEAGIFRIDRSTGEIFVVRPDMLSVRTQPMHMLNISAT 800
801 DGGNLRSNADAVVFLSIIDAMQRPPIFEKARYNYYVKEDIPRGTVVGSVI 850
851 AASGDVAHRSPVRYSIYSGDPDGYFSIETNSGNIRIAKPLDHEAKSQVLL 900
901 NIQATLGEPPVYGHTQVNIEVEDVNDNAPEFEASMVRISVPESAELGAPL 950
951 YAAHAHDKDSGSSGQVTYSLVKESGKGLFAIDARSGHLILSQHLDYESSQ 1000
1001 RHTLIVTATDGGVPSLSTNLTILVDVQDVNDNPPVFEKDEYSVNVSESRS 1050
1051 INAQIIQVNASDLDTGNNARITYRIVDAGVDNVTNSISSSDVSQHFGIFP 1100
1101 NSGWIYLRAPLDRETRDRYQLTVLATDNGTPAAHAKTRVIVRVLDANDND 1150
1151 PKFQKSKYEFRIEENLRRGSVVGVVTASDLDLGENAAIRYSLLPINSSFQ 1200
1201 VHPVTGEISTREPLDRELRELYDLVVEARDQGTPVRSARVPVRIHVSDVN 1250
1251 DNAPEIADPQEDVVSVREEQPPGTEVVRVRAVDRDHGQNASITYSIVKGR 1300
1301 DSDGHGLFSIDPTSGVIRTRVVLDHEERSIYRLGVAASDGGNPPRETVRM 1350
1351 LRVEVLDLNDNRPTFTSSSLVFRVREDAALGHVVGSISPIERPADVVRNS 1400
1401 VEESFEDLRVTYTLNPLTKDLIEAAFDIDRHSGNLVVARLLDREVQSEFR 1450
1451 LEIRALDTTASNNPQSSAITVKIEVADVNDNAPEWPQDPIDLQVSEATPV 1500
1501 GTIIHNFTATDADTGTNGDLQYRLIRYFPQLNESQEQAMSLFRMDSLTGA 1550
1551 LSLQAPLDFEAVQEYLLIVQALDQSSNVTERLQTSVTVRLRILDANDHAP 1600
1601 HFVSPNSSGGKTASLFISDATRIGEVVAHIVAVDEDSGDNGQLTYEITGG 1650
1651 NGEGRFRINSQTGIIELVKSLPPATEDVEKGGRFNLIIGAKDHGQPEPKK 1700
1701 SSLNLHLIVQGSHNNPPRFLQAVYRATILENVPSGSFVLQVTAKSLHGAE 1750
1751 NANLSYEIPAGVANDLFHVDWQRGIITTRGQFDRESQASYVLPVYVRDAN 1800
1801 RQSTLSSSAVRKQRSSDSIGDTSNGQHFDVATIYITVGDVNDNSPEFRPG 1850
1851 SCYGLSVPENSEPGVIHTVVASDLDEGPNADLIYSITGGNLGNKFSIDSS 1900
1901 SGELSARPLDREQHSRYTLQIQASDRGQPKSRQGHCNITIFVEDQNDNAP 1950
1951 RFKLSKYTGSVQEDAPLGTSVVQISAVDADLGVNARLVYSLANETQWQFA 2000
2001 IDGQSGLITTVGKLDRELQASYNFMVLATDGGRYEVRSATVPVQINVLDI 2050
2051 NDNRPIFERYPYIGQVPALIQPGQTLLKVQALDADLGANAEIVYSLNAEN 2100
2101 SAVSAKFRINPSTGALSASQSLASESGKLLHLEVVARDKGNPPQSSLGLI 2150
2151 ELLIGEAPQGTPVLRFQNETYRVMLKENSPSGTRLLQVVALRSDGRRQKV 2200
2201 QFSFGAGNEDGILSLDSLSGEIRVNKPHLLDYDRFSTPSMSALSRGRALH 2250
2251 YEEEIDESSEEDPNNSTRSQRALTSSSFALTNSQPNEIRVVLVARTADAP 2300
2301 FLASYAELVIELEDENDNSPKFSQKQFVATVSEGNNKGTFVAQVHAFDSD 2350
2351 AGSNARLRYHIVDGNHDNAFVIEPAFSGIVRTNIVLDREIRDIYKLKIIA 2400
2401 TDEGVPQMTGTATIRVQIVDVNDNQPTFPPNNLVTVSEATELGAVITSIS 2450
2451 ANDVDTYPALTYRLGAESTVDIENMSIFALDRYSGKLVLKRRLDYELQQE 2500
2501 YELDVIASDAAHEARTVLTVRVNDENDNAPVFLAQQPPAYFAILPAISEI 2550
2551 SESLSVDFDLLTVNATDADSEGNNSKVIYIIEPAQEGFSVHPSNGVVSVN 2600
2601 MSRLQPAVSSSGDYFVRIIAKDAGKPALKSSTLLRVQANDNGSGRSQFLQ 2650
2651 NQYRAQISEAAPLGSVVLQLGQDALDQSLAIIAGNEESAFELLQSKAIVL 2700
2701 VKPLDRERNDLYKLRLVLSHPHGPPLISSLNSSSGISVIITILDANDNFP 2750
2751 IFDRSAKYEAEISELAPLRYSIAQLQAIDADQENTPNSEVVYDITSGNDE 2800
2801 HMFTIDLVTGVLFVNNRLDYDSGAKSYELIIRACDSHHQRPLCSLQPFRL 2850
2851 ELHDENDNEPKFPLTEYVHFLAENEPVGSSVFRAHASDLDKGPFGQLNYS 2900
2901 IGPAPSDESSWKMFRVDSESGLVTSAFVFDYEQRQRYDMELLASDMGGKK 2950
2951 ASVAVRVEIESRDEFTPQFTERTYRFVLPAAVALPQGYVVGQVTATDSDS 3000
3001 GPDGRVVYQLSAPHSHFKVNRSSGAVLIKRKLKLDGDGDGNLYMDGRDIS 3050
3051 LVISASSGRHNSLSSMAVVEIALDPLAHPGTNLASAGGSSSGSIGDWAIG 3100
3101 LLVAFLLVLCAAAGIFLFIHMRSRKPRNAVKPHLATDNAGVGNTNSYVDP 3150
3151 SAFDTIPIRGSISGGAAGAASGQFAPPKYDEIPPFGAHAGSSGAATTSEL 3200
3201 SGSEQSGSSGRGSAEDDGEDEEIRMINEGPLHHRNGGAGAGSDDGRISDI 3250
3251 SVQNTQEYLARLGIVDHDPSGAGGGASSMAGSSHPMHLYHDDDATARSDI 3300
3301 TNLIYAKLNDVTGAGSEIGSSADDAGTTAGSIGTIGTAITHGHGVMSSYG 3350
3351 EVPVPVPVVVGGSNVGGSLSSIVHSEEELTGSYNWDYLLDWGPQYQPLAH 3400
3401 VFSEIARLKDDTLSEHSGSGASSSAKSKHSSSHSSAGAGSVVLKPPPSAP 3450
3451 PTHIPPPLLTNVAPRAINLPMRLPPHLSLAPAHLPRSPIGHEASGSFSTS 3500
3501 SAMSPSFSPSLSPLATRSPSISPLGAGPPTHLPHVSLPRHGHAPQPSQRG 3550
3551 NVGTRM 3556
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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