 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P19787 from www.uniprot.org...
The NucPred score for your sequence is 0.53 (see score help below)
1 MGPSNPAMAYFKPSTRDTMDPCSGNAADGSIRVRFRGGIERWKECVNQVP 50
51 ERCDLSGLTTDSTRYQLASTGFGDASAAYQERLMTVPVDVHAALQELCLE 100
101 RRVSVGSVINFSVHQMLKGFGNGTHTITASLHREQNLQNSSPSWVVSPTI 150
151 VTHENRDGWSVAQAVESIEAGRGSEKESVTAIDSGSSLVKMGLFDLLVSF 200
201 VDADDARIPCFDFPLAVIVRECDANLSLTLRFSDCLFNEETICNFTDALN 250
251 ILLAEAVIGRVTPVADIELLSAEQKQQLEEWNNTDGEYPSSKRLHHLIEE 300
301 VVERHEDKIAVVCDERELTYGELNAQGNSLARYLRSIGILPEQLVALFLD 350
351 KSEKLIVTILGVWKSGAAYVPIDPTYPDERVRFVLDDTKARAIIASNQHV 400
401 ERLQREVIGDRNLCIIRLEPLLASLAQDSSKFPAHNLDDLPLTSQQLAYV 450
451 TYTSGTTGFPKGIFKQHTNVVNSITDLSARYGVAGQHHEAILLFSACVFE 500
501 PFVRQTLMALVNGHLLAVINDVEKYDADTLLPFIRRHSITYLNGTASVLQ 550
551 EYDFSDCPSLNRIILVGENLTEARYLALRQRFKNRILNEYGFTESAFVTA 600
601 LKIFDPESTRKDTSLGRPVRNVKCYILNPSLKRVPIGATGELHIGGLGIS 650
651 KGYLNRPELTPHRFIPNPFQTDCEKQLGINSLMYKTGDLARWLPNGEVEY 700
701 LGRADFQIKLRGIRIEPGEIETMLAMYPRVRTSLVVSKKLRNGPEETTNE 750
751 HLVGYYVCDSASVSEADLLSFLEKKLPRYMIPTRLVQLSQIPVNVNGKAD 800
801 LRALPAVDISNSTEVRSDLRGDTEIALGEIWADVLGARQRSVSRNDNFFR 850
851 LGGHSITCIQLIARIRQRLSVSISVEDVFATRTLERMADLLQNKQQEKCD 900
901 KPHEAPTELLEENAATDNIYLANSLQQGFVYHYLKSMEQSDAYVMQSVLR 950
951 YNTTLSPDLFQRAWKHAQQSFPALRLRFSWEKEVFQLLDQDPPLDWRFLY 1000
1001 FTDVAAGAVEDRKLEDLRRQDLTERFKLDVGRLFRVYLIKHSENRFTCLF 1050
1051 SCHHAILDGWSLPLLFEKVHETYLQLLHGDNLTSSMDDPYTRTQRYLHAH 1100
1101 REDHLDFWAGVVQKINERCDMNALLNERSRYKVQLADYDQVQEQRQLTIA 1150
1151 LSGDAWLADLRQTCSAQGITLHSILQFVWHAVLHAYGGGTHTITGTTISG 1200
1201 RNLPILGIERAVGPYINTLPLVLDHSTFKDKTIMEAIEDVQAKVNVMNSR 1250
1251 GNVELGRLHKTDLKHGLFDSLFVLENYPNLDKSRTLEHQTELGYSIEGGT 1300
1301 EKLNYPLAVIAREVETTGGFTVSICYASELFEEVMISELLHMVQDTLMQV 1350
1351 ARGLNEPVGSLEYLSSIQLEQLAAWNATEAEFPDTTLHEMFENEASQKPD 1400
1401 KIAVVYEETSLTYRELNERANRMAHQLRSDVSPNPNEVIALVMDKSEHMI 1450
1451 VNILAVWKSGGAYVPIDPGYPNDRIQYILEDTQALAVIADSCYLPRIKGM 1500
1501 AASGTLLYPSVLPANPDSKWSVSNPSPLSRSTDLAYIIYTSGTTGRPKGV 1550
1551 TVEHHGVVNLQVSLSKVFGLRDTDDEVILSFSNYVFDHFVEQMTDAILNG 1600
1601 QTLLVLNDGMRGDKERLYRYIEKNRVTYLSGTPSVVSMYEFSRFKDHLRR 1650
1651 VDCVGEAFSEPVFDKIRETFHGLVINGYGPTEVSITTHKRLYPFPERRMD 1700
1701 KSIGQQVHNSTSYVLNEDMKRTPIGSVGELYLGGEGVVRGYHNRADVTAE 1750
1751 RFIPNPFQSEEDKREGRNSRLYKTGDLVRWIPGSSGEVEYLGRNDFQVKI 1800
1801 RGLRIELGEIEAILSSYHGIKQSVVIAKDCREGAQKFLVGYYVADAALPS 1850
1851 AAIRRFMQSRLPGYMVPSRLILVSKFPVTPSGKLDTKALPPAEEESEIDV 1900
1901 VPPRSEIERSLCDIWAELLEMHPEEIGIYSDFFSLGGDSLKSTKLSFMIH 1950
1951 ESFNRAVSVSALFCHRTVEAQTHLILNDAADVHEITPIDCNDTQMIPVSR 2000
2001 AQERLLFIHEFENGSNAYNIDAAFELPGSVDASLLEQALRGNLARHEALR 2050
2051 TLLVKDHATGIYLQKVLSPDEAQGMFSVNVDTAKQVERLDQEIASLSQHV 2100
2101 FRLDDELPWEARILKLESGGLYLILAFHHTCFDAWSLKVFEQELRALYAA 2150
2151 LQKTKSAANLPALKAQYKEYALYHRRQLSGDRMRNLSDFWLRKLIGLEPL 2200
2201 QLITDRPRPVQFKYDGDDLSIELSKKETENLRGVAKRCKSSLYVVLVSVY 2250
2251 CVMLASYANQSDVSVGIPVSHRTHPQFQSVIGFFVNLVVLRVDISQSAIC 2300
2301 GLIRRVMKELVDAQLHQDMPFQEVTKLLQVDNDPSRHPLVQNVFNFESRA 2350
2351 NGEHDARSEDEGSLAFNQYRPVQPVDSVAKFDLNATVTELESGLRVNFNY 2400
2401 ATSLFNKSTIQGFLHTYEYLLRQLSELSAEGINEDTQLSLVRPTENGDLH 2450
2451 LPLAQSPLATTAEEQKVASLNQAFEREAFLAAEKIAVVQGDRALSYADLN 2500
2501 GQANQLARYIQSVSCIGADDGIALMLEKSIDTIICILAIWKAGAAYVPLD 2550
2551 PTYPPGRVQLILEEIKAKAVLVHSSHASKCERHGAKVIAVDSPAIETAVS 2600
2601 QQSAADLPTIASLGNLAYIIFTSGTSGKPKGVLVEQKAVLLLRDALRERY 2650
2651 FGRDCTKHHGVLFLSNYVFDFSVEQLVLSVLSGHKLIVPPAEFVADDEFY 2700
2701 RMASTHGLSYLSGTPSLLQKIDLARLDHLQVVTAAGEELHATQYEKMRRR 2750
2751 FNGPIYNAYGVTETTVYNIIAEFTTNSIFENALREVLPGTRAYVLNAALQ 2800
2801 PVPFDAVGELYLAGDSVTRGYLNQPLLTDQRFIPNPFCKEEDIAMGRFAR 2850
2851 LYKTGDLVRSRFNRQQQPQLEYLGRGDLQIKMRGYRIEISEVQNVLTSSP 2900
2901 GVREGAVVAKYENNDTYSRTAHSLVGYYTTDNETVSEADILTFMKARLPT 2950
2951 YMVPSHLCCLEGALPVTINGKLDVRRLPEIINDSAQSSYSPPRNIIEAKM 3000
3001 CRLWESALGMERCGIDDDLFKLGGDSITSLHLVAQIHNQVGCKITVRDIF 3050
3051 EHRTARALHDHVFMKDSDRSNVTQFRTEQGPVIGEAPLLPIQDWFLSKAL 3100
3101 QHPMYWNHTFYVRTPELDVDSLSAAVRDLQQYHDVFRMRLKREEVGFVQS 3150
3151 FAEDFSPAQLRVLNVKDVDGSAAVNEILDGWQSGFNLENGPIGSIGYLHG 3200
3201 YEDRSARVWFSVHHMAIDTVSWQILVRDLQTLYRNGSLGSKGSSFRQWAE 3250
3251 AIQNYKASDSERNHWNKLVMETASSISALPTSTGSRVRLSRSLSPEKTAS 3300
3301 LIQGGIDRQDVSVYDSLLTSVGLALQHIAPTGPSMVTIEGHGREEVDQTL 3350
3351 DVSRTMGWFTTMYPFEIPRLSTENIVQGVVAVSERFRQVPARGVGYGTLY 3400
3401 GYTQHPLPQVTVNYLGQLARKQSKPKEWVLAVGDNEFEYGLMTSPEDKDR 3450
3451 SSSAVDVTAVCIDGTMIIDVDSAWSLEESEQFISSIEEGLNKILDGRASQ 3500
3501 QTSRFPDVPQPAETYTPYFEYLEPPRQGPTLFLLPPGEGGAESYFNNIVK 3550
3551 RLRQTNMVVFNNYYLHSKRLRTFEELAEMYLDQVRGIQPHGPYHFIGWSF 3600
3601 GGILAMEMSRRLVASDEKIGFLGIIDTYFNVRGATRTIGLGDTEILDPIH 3650
3651 HIYNPDPANFQRLPSATDRIVLFKAMRPNNKYESENQRRLYEYYDGTRLN 3700
3701 GLDSLLPSDSDVQLVPLTDDTHFSWVGNPQQVEQMCATIKEHLARY 3746
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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