 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P97313 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MAEEGTGVRCWLLQLQEFLSAADRCSAAGASYQLIRSLGQECVLSTSSAV 50
51 QALQISLVFSRDFGLLVFIRKSLSIEDFRDCREEALKFLCVFLEKIDQKV 100
101 MHYSLDIKNTCTSVYTKDRTAKCKIPALDLLIKLLQILRSTRLMDEFKIG 150
151 ELFNKFYGELASKSKLPDTVLEKVYELLGVLGEVHPSEMINHSENLFRAF 200
201 LGELKTQMTSTVREPKFPVLAGCLKGLSSLLCNFTKSMEEDPQTSKEIFG 250
251 FTFKAIRPQIEMKRYAVPLAGLRLLTLHASQFTACLLDNYITLFEVLSKW 300
301 CSHTNVELKKAAHSALESFLRQISFTVAEDAELHKSRLKYFMEQFYGIIR 350
351 NTDSNNKELAIAIRGYGLFAGPCKVINAKDVDFMYVELIQRCKQMFLTHA 400
401 DASEDHVYQMPSFLQSIASVLLYLDTVPEVYTPVLEHLMVVQIDSFPQYS 450
451 PKMQLVCCKAIIKLFLALSEKGPVHWNCISAVVHQGLIRICSKPVVLQKD 500
501 VESRSDNRSASEEVRTGRWKVPTYKDYVDLFQHLLGCDQMEDFILGDETF 550
551 LFVNSSLKSLNHLLYDEFIRSVLKIVEKLDLTLEKQTVGEQEDGSTADVW 600
601 VIPTSDPAANLHPAKPSDFSALINLVEFCREILPRKHVGFFEPWVYSFAY 650
651 ELILQSTRLPLISGFYKLLSIAVKNARKIKYFEGISPKSLKHSPEDTEKY 700
701 SCFALFAKFGKEVSVKMKQYKDELLASCLTFVLSLPHDIIELDVRAYVPA 750
751 LQMAFKLGLSHMPLAEIGLHALKEWSVHIDKSILQPYYKDILPCLDGYLN 800
801 TSTLSDETKSHWGLSALSRAAQKGFNRHVVKHLKRTRNSSPDEALSLEEI 850
851 RIKVVQILGSLGGQINKSLVTATSGERMKKYVAWDAERRLSFAVPFREMK 900
901 PVIYLDVFLPRVTELALSASDRQTKVAACELLHSMVMFMLGRATQMPEGQ 950
951 GLPPMYQLYKHTFPVLLQLACDVDQVTRQLYEPLVMQLIHWLTNNKKFES 1000
1001 QDTVALLEAILDGIVDPVDSTLRDFCGRCVQEFLKWSIKQTTPQQQEKSP 1050
1051 VNSKSLFKRLYSLALHPNAFKRLGAALAFNHIYKEFREEGSLVEQFVFEA 1100
1101 LVTYMESLALAHEDEKSLGTVQQCCDAIDHLRRIIEKKHVSLNKAKKRRL 1150
1151 PQGFPPLTSLCLLDLVEWLLAHCGRPQTECRHKSMELFYKFVPLLPGNKS 1200
1201 PSLWLKDLIKKKGISFLINTFEGGASSSDQPAGILAQPTLVYLQGPISLR 1250
1251 GVLQWLDLLLAALECYNTFIEKETVQGQEVLGAEVQSSLLKSVAFFLESI 1300
1301 ATHSARAVEQRFGSGAPGPPSLHEEEKYNYSKCTVLVRIMEFTTTLLIAS 1350
1351 PEDCKLLEKDLCNTNLMQVLVKMICEPMSLGFNIGDVQVMNHLPSICVNL 1400
1401 LKALRKSPYRDMLETHLKEKVTVQSVEELCSINLCSSGARQERSKLLSIL 1450
1451 SACKQLHKAGFSHVISPSQSTALNHSVGMRLLSLVYKGIVPAEERQCLQS 1500
1501 LDPSCKSLANGLLELAFGFGGLCDHLVSLLLNSAMLSTQYLGSSQRNISF 1550
1551 SHGEYFYSLFSEVINSELLKNLDIAVSRLMESSSDNPKMVSTVLNGMLDT 1600
1601 SFRDRAVQKHQGLKLATAILQNWRKCDSWWAPDSAPESKTTVLSLLAKML 1650
1651 QIDSALSFDTNHSSFSEIFTTYASLLADTKLGLHLKGQAIILLPFFTSLR 1700
1701 EGSLENLKHILEKLIVCNFPMKSDEFPPDSLKYNNYVDCMKKFLDALELS 1750
1751 QSPMLFQLMTDILCREQRHIMEELFQTTFKRIARQSPCVTQLNLLESVYT 1800
1801 MFRKADLPSNVTRQAFVDRSLLTLLWHCDLDTLKEFFSRIVVDAIDVLKS 1850
1851 RFTKLNEFTFDTQITKKMCYYKMLAVMYSRLLKDDVHSKEAKINQAFHGS 1900
1901 RVAEGNELTKTLLKLCHDAFTENMVGESQLLEKRRLYHCAAYNCAISLIS 1950
1951 CVFNELKFYQGFLFNEKPEKNLFIFENLIDLKRCYTFPIEVEVPMERKKK 2000
2001 YIEIRKEARDAANGASGSPHYMSSLSYLTDSSLSEEMSQFDFSTGVQSYS 2050
2051 YSSQDRKPTTGHFQRREHQDSMTQDDIMELEMDELNQHECMAPMIALIKH 2100
2101 MQRNVIAPKGEEGSIPKDLPPWMKFLHDKLGNASVSLNIRLFLAKLVINT 2150
2151 EEVFRPYAKHWLSPLLQLAVCENNREGIHYMMVEIVATILSWTGLATPTG 2200
2201 VPKDEVLANRLLRFLMKHVFHPKRAVFRHNLEIIKTLVECWKECLSIPYR 2250
2251 LIFEKFSHKDPNSKDNSVGIQLLGIVIANNLPPYDPNCDITSAMYFEALV 2300
2301 NNMSFVKYKEVYAAAAEVLGLILQYITERKHVIAELVCELVIKQLKQHQN 2350
2351 TMEDKFIVCLNKIAKGFPPLADRFLNALFFLLPKFHGVMKTLCLEVVLCR 2400
2401 AEEITGLYLQLKSKDFLQVMRHRDDERQKVCLDIVYKMVAKLKPIELREL 2450
2451 LNPVVEFVSHPSPTCREQMYNILMWIHDNYRDQESQNDEDSQEIFKLAKD 2500
2501 VLIQGLIDENVGLQLIIRNFWSHETRLPSNTLDRLLALNSLYSPKIEVHF 2550
2551 LSLATNFLLEMTRMSPDYLNPIFEHPLSECEFQEYTIDPDWRFRSTVLTP 2600
2601 MFIETQASPSILHTQTQEGPLSDQRQKPGQVRATQQQYDFTPTQASVERS 2650
2651 SFDWLTGSSIDLLADHTVFSSETLSSSLLFSHKRTEKSQRMSCKSVGPDF 2700
2701 GTKKLGLPDDEVDNQVKSGTPSQADILRLRRRFLKDREKLSLLYAKRGLM 2750
2751 EQKLEKDIKSEFKMKQDAQVVLYRSYRHGDLPDIQIQHSGLITPLQAVAQ 2800
2801 KDPIIAKQLFSSLFSGILKEMNKFKTTSEKNIITQNLLQDFNRFLNTTFL 2850
2851 FFPPFVSCIQEISCQHPDFLTLDPASVRVGCLASLQQPGGIRLLEEALLR 2900
2901 LMPKEPPTKRVRGKTCLPPDVLRWMELAKLYRSIGEYDVLRGIFSSELGT 2950
2951 TQDTQNALLAEARSDYCQAAKLYDEALNKLEWVDGEPTEAEKEFWELASL 3000
3001 DCYNNLSKWKELEYCSTVNIVSENSLDLSKMWSEPFYQETYLPYVIRSKL 3050
3051 KLLLQGEGNQSLLTFVDEAMNKELQKTVLELQYSQELSLLYILQDDIDRA 3100
3101 TYYIKNGIQIFMQNYSSIDVLLYRSRLAKLQSVQTLAEIEEFLSFICKHG 3150
3151 DLSSLGPLRRLLKTWTSRYPDVVTDPMHIWDDIITNRCFFLSKIEERLTA 3200
3201 PSGDHSMSVDEDEESIDREVYEPKEDVRCMLQSCRFTMKMKMIESAWKQS 3250
3251 NFSLSMKLLKEMHKESKTREIWRVQWLHSYSQLNHCRSHTQSPREQVLNM 3300
3301 LKTITLLDESDISNYLNKNIQASCDQSILLGTTCRIMADALSREPACLSD 3350
3351 LEENKVNSILTLSGSNAENTETVITGLYQRAFHHLSKAVQSAEEETQLSC 3400
3401 WGHEAAAERAHAYMTLVGFCDQQLRKVEESASQKTSAEMEAYPALVVEKM 3450
3451 LRALKLNSSEARLKFPRLLQIIEQYSEETLNIMTKEISSIPCWQFIGWIS 3500
3501 HMMALLDKEEAIAVQHTVEEIADNYPQAIIYPFIISSESYSFKNTSSGHN 3550
3551 NKAFVERIKSKLDHGEVIHSFINALDQLSNPDLLFKDWVSDTKDELGKNP 3600
3601 VNKKNIEKLYERMYAALGDLRAPGLGPFRRRFIQAFGKEFVKSFGNGGSK 3650
3651 LLTMKVDDFCKITGSLLVRMKKDSKLPGNLKEYSPWMSEFKAQFLKNELE 3700
3701 IPGQYDGKSKPLPEYHVRISGFDERVKVMLSLRKPKRIVIRGHDEKEYPF 3750
3751 LVKGGEDLRQDQRIEQIFEVMNAILSQDAACSQRNMQLRTYRVVPMTSRL 3800
3801 GLIEWIENTMTLKDLLLSNMSQEEKVANNSDPKAPIRDYKDWLMKVSGKS 3850
3851 DAGAYVLMYSRANRTETVVAFRRRESQVPPDLLKRAFVKMSTSPEAFLAL 3900
3901 RSHFASSHALLCISHWLLGIGDRHLNNFMVAMETGSVIGIDFGHAFGSAT 3950
3951 QFLPVPELMPFRLTRQFVSLMLPMKETGLMCTVMVHALRAFRSCAGLLTD 4000
4001 TMEIFVKEPSFDWKSFEQTMLRKGGSWIQEINVTEKNWYPQHKIRYAKRK 4050
4051 LAGANPAVITCDELYLGHEASSAFRSYTAVARGNRDYNIRAQEPESGLSE 4100
4101 ETQVKCLVDQATDPNILGRTWEGWEPWM 4128
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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