 | 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 0.51 (see score help below)
1 MLRSSLLILLAIVLLGSSQAASHDQERERKLEVFEGVAVDYQIGYIGDFG 50
51 GIDSGPPYIIVAEAGVETDLAIDRATGEIRTKVKLDRETRASYSLVAIPL 100
101 SGRNIRVLVTVKDENDNAPTFPQTSMHIEFPENTPREVKRTLLPARDLDL 150
151 EPYNTQRYNIVSGNVNDAFRLSSHRERDGVLYLDLQISGFLDRETTPGYS 200
201 LLIEALDGGTPPLRGFMTVNITIQDVNDNQPIFNQSRYFATVPENATVGT 250
251 SVLQVYASDTDADENGLVEYAINRRQSDKEQMFRIDPRTGAIYINKALDF 300
301 ETKELHELVVVAKDHGEQPLETTAFVSIRVTDVNDNQPTINVIFLSDDAS 350
351 PKISESAQPGEFVARISVHDPDSKTEYANVNVTLNGGDGHFALTTRDNSI 400
401 YLVIVHLPLDREIVSNYTLSVVATDKGTPPLHASKSIFLRITDVNDNPPE 450
451 FEQDLYHANVMEVADPGTSVLQVLAHDRDEGLNSALTYSLAETPETHAQW 500
501 FQIDPQTGLITTRSHIDCETEPVPQLTVVARDGGVPPLSSTATVLVTIHD 550
551 VNDNEPIFDQSFYNVSVAENEPVGRCILKVSASDPDCGVNAMVNYTIGEG 600
601 FKHLTEFEVRSASGEICIAGELDFERRSSYEFPVLATDRGGLSTTAMIKM 650
651 QLTDVNDNRPVFYPREYKVSLRESPKASSQASSTPIVAVVATDPDYGNFG 700
701 QVSYRIVAGNEAGIFRIDRSTGEIFVVRPDMLSVRTQPMHMLNISATDGG 750
751 NLRSNADAVVFLSIIDAMQRPPIFEKARYNYYVKEDIPRGTVVGSVIAAS 800
801 GDVAHRSPVRYSIYSGDPDGYFSIETNSGNIRIAKPLDHEAKSQVLLNIQ 850
851 ATLGEPPVYGHTQVNIEVEDVNDNAPEFEASMVRISVPESAELGAPLYAA 900
901 HAHDKDSGSSGQVTYSLVKESGKGLFAIDARSGHLILSQHLDYESSQRHT 950
951 LIVTATDGGVPSLSTNLTILVDVQDVNDNPPVFEKDEYSVNVSESRSINA 1000
1001 QIIQVNASDLDTGNNARITYRIVDAGVDNVTNSISSSDVSQHFGIFPNSG 1050
1051 WIYLRAPLDRETRDRYQLTVLATDNGTPAAHAKTRVIVRVLDANDNDPKF 1100
1101 QKSKYEFRIEENLRRGSVVGVVTASDLDLGENAAIRYSLLPINSSFQVHP 1150
1151 VTGEISTREPLDRELRELYDLVVEARDQGTPVRSARVPVRIHVSDVNDNA 1200
1201 PEIADPQEDVVSVREEQPPGTEVVRVRAVDRDHGQNASITYSIVKGRDSD 1250
1251 GHGLFSIDPTSGVIRTRVVLDHEERSIYRLGVAASDGGNPPRETVRMLRV 1300
1301 EVLDLNDNRPTFTSSSLVFRVREDAALGHVVGSISPIERPADVVRNSVEE 1350
1351 SFEDLRVTYTLNPLTKDLIEAAFDIDRHSGNLVVARLLDREVQSEFRLEI 1400
1401 RALDTTASNNPQSSAITVKIEVADVNDNAPEWPQDPIDLQVSEATPVGTI 1450
1451 IHNFTATDADTGTNGDLQYRLIRYFPQLNESQEQAMSLFRMDSLTGALSL 1500
1501 QAPLDFEAVQEYLLIVQALDQSSNVTERLQTSVTVRLRILDANDHAPHFV 1550
1551 SPNSSGGKTASLFISDATRIGEVVAHIVAVDEDSGDNGQLTYEITGGNGE 1600
1601 GRFRINSQTGIIELVKSLPPATEDVEKGGRFNLIIGAKDHGQPEPKKSSL 1650
1651 NLHLIVQGSHNNPPRFLQAVYRATILENVPSGSFVLQVTAKSLHGAENAN 1700
1701 LSYEIPAGVANDLFHVDWQRGIITTRGQFDRESQASYVLPVYVRDANRQS 1750
1751 TLSSSAVRKQRSSDSIGDTSNGQHFDVATIYITVGDVNDNSPEFRPGSCY 1800
1801 GLSVPENSEPGVIHTVVASDLDEGPNADLIYSITGGNLGNKFSIDSSSGE 1850
1851 LSARPLDREQHSRYTLQIQASDRGQPKSRQGHCNITIFVEDQNDNAPRFK 1900
1901 LSKYTGSVQEDAPLGTSVVQISAVDADLGVNARLVYSLANETQWQFAIDG 1950
1951 QSGLITTVGKLDRELQASYNFMVLATDGGRYEVRSATVPVQINVLDINDN 2000
2001 RPIFERYPYIGQVPALIQPGQTLLKVQALDADLGANAEIVYSLNAENSAV 2050
2051 SAKFRINPSTGALSASQSLASESGKLLHLEVVARDKGNPPQSSLGLIELL 2100
2101 IGEAPQGTPVLRFQNETYRVMLKENSPSGTRLLQVVALRSDGRRQKVQFS 2150
2151 FGAGNEDGILSLDSLSGEIRVNKPHLLDYDRFSTPSMSALSRGRALHYEE 2200
2201 EIDESSEEDPNNSTRSQRALTSSSFALTNSQPNEIRVVLVARTADAPFLA 2250
2251 SYAELVIELEDENDNSPKFSQKQFVATVSEGNNKGTFVAQVHAFDSDAGS 2300
2301 NARLRYHIVDGNHDNAFVIEPAFSGIVRTNIVLDREIRDIYKLKIIATDE 2350
2351 GVPQMTGTATIRVQIVDVNDNQPTFPPNNLVTVSEATELGAVITSISAND 2400
2401 VDTYPALTYRLGAESTVDIENMSIFALDRYSGKLVLKRRLDYELQQEYEL 2450
2451 DVIASDAAHEARTVLTVRVNDENDNAPVFLAQQPPAYFAILPAISEISES 2500
2501 LSVDFDLLTVNATDADSEGNNSKVIYIIEPAQEGFSVHPSNGVVSVNMSR 2550
2551 LQPAVSSSGDYFVRIIAKDAGKPALKSSTLLRVQANDNGSGRSQFLQNQY 2600
2601 RAQISEAAPLGSVVLQLGQDALDQSLAIIAGNEESAFELLQSKAIVLVKP 2650
2651 LDRERNDLYKLRLVLSHPHGPPLISSLNSSSGISVIITILDANDNFPIFD 2700
2701 RSAKYEAEISELAPLRYSIAQLQAIDADQENTPNSEVVYDITSGNDEHMF 2750
2751 TIDLVTGVLFVNNRLDYDSGAKSYELIIRACDSHHQRPLCSLQPFRLELH 2800
2801 DENDNEPKFPLTEYVHFLAENEPVGSSVFRAHASDLDKGPFGQLNYSIGP 2850
2851 APSDESSWKMFRVDSESGLVTSAFVFDYEQRQRYDMELLASDMGGKKASV 2900
2901 AVRVEIESRDEFTPQFTERTYRFVLPAAVALPQGYVVGQVTATDSDSGPD 2950
2951 GRVVYQLSAPHSHFKVNRSSGAVLIKRKLKLDGDGDGNLYMDGRDISLVI 3000
3001 SASSGRHNSLSSMAVVEIALDPLAHPGTNLASAGGSSSGSIGDWAIGLLV 3050
3051 AFLLVLCAAAGIFLFIHMRSRKPRNAVKPHLATDNAGVGNTNSYVDPSAF 3100
3101 DTIPIRGSISGGAAGAASGQFAPPKYDEIPPFGAHAGSSGAATTSELSGS 3150
3151 EQSGSSGRGSAEDDGEDEEIRMINEGPLHHRNGGAGAGSDDGRISDISVQ 3200
3201 NTQEYLARLGIVDHDPSGAGGGASSMAGSSHPMHLYHDDDATARSDITNL 3250
3251 IYAKLNDVTGAGSEIGSSADDAGTTAGSIGTIGTAITHGHGVMSSYGEVP 3300
3301 VPVPVVVGGSNVGGSLSSIVHSEEELTGSYNWDYLLDWGPQYQPLAHVFS 3350
3351 EIARLKDDTLSEHSGSGASSSAKSKHSSSHSSAGAGSVVLKPPPSAPPTH 3400
3401 IPPPLLTNVAPRAINLPMRLPPHLSLAPAHLPRSPIGHEASGSFSTSSAM 3450
3451 SPSFSPSLSPLATRSPSISPLGAGPPTHLPHVSLPRHGHAPQPSQRGNVG 3500
3501 TRM 3503
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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