 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q24742 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MGRSKFPGNPSKSINRKRISVLQLEDEAASAAAAAAAATAATTEQHQQSE 50
51 QSAGSSASREKGNNCDNDDDDNAPSGAATSGNRGASSGASDAAPEGGNSY 100
101 GNGSSTGSKTTNGGNVNGGSHHKSATAPAELKECKNQGNQIEPNNCIAAE 150
151 PDGTEDTNNDDDDDSSNDKKPTAAAAAAAAAAFVPGPSALQRARKGGNKK 200
201 FKNLNLARPEVMLPSTSKLKQQQQQQLQLNCPSASASSLSSSAAAAAAAA 250
251 APTTTTTTASASATLTATATSTSTSSLPGTPLSVIAGGGGGAAAAALLLA 300
301 NPFASVETKVVEVNAAATAAATAAATAAAGAGEDVGMLKASIEMANEAGL 350
351 EAPAVAVKSSGSSPNPNHNPNAVAGSTSAAAPGAPTATKQKKTVTFKNIL 400
401 ETSDDKSVVKRFYNPDNRVPLVSIMKKDSLNRPLNYCRGSEFIVRPSILS 450
451 KILNKNSNIDKLNSLKFRSVHASSNSIQESSSSTTNLFGSGLSRAFGAPI 500
501 DDEDAVSGGVTFRKQEPQHKTPEDNDDDGSASSDAIEDDEDIDDDDAEEN 550
551 EEAASEKSAETTASVDEKEADDRQLVMDKHFVLPKRSTRSSRIIKPNKRL 600
601 LEVGGICSKRSPSDANGKPKPKNYFGLATLPAKCTPRRRRSAATALSQKL 650
651 GKETFASFATAKVNSSFVLRQPRLQFQTDKSRSFVSAKPTLPTTTVLPAS 700
701 SSAITSANVLSFGALNNANSAVAAASTCAVCSAPVNNKDAPLARKYGVIA 750
751 CEVCRKFNSRMTKISKLSTPMHSNPSTSTAQSGQQLKCTDGGNCSILSLK 800
801 SQLKNFKKLYKERCKACWLKKCLATLQLPAGHRSRLSAILPASMREEVAP 850
851 KDDKCPELLSPTASLRFTAPTSSASSGTTIKWKSSAETAVNSIKSNPLAE 900
901 NNVTFGGTPLLRPAILEKPLFLKIGSDNKKAKESKEALGLSPVPSTSEAA 950
951 VAPGKTTRKAKQDKEKARELEAEKPLSPNAKKTTEANTPETQKDEQPAST 1000
1001 TTTVSAASSSTSHTSSAATNSSQLETTEAANASAVPDNLKRQRIDLKGPR 1050
1051 VKHVCRSASIVLGQPLATFGDEEEELAAAEAGPAPTTTTTTTSPEVIIKK 1100
1101 PKSPQPMQMIIDENDNCASCILTPTEATAEAQPAVKSVLESRSSKSNTQT 1150
1151 EAKKTPATSGSSKGKVTTRNATATVTSVASSLVATKKQRNIEVSSSISSS 1200
1201 QAAATQSRRALAKEVNRLKALISIDFWENYDPAEVCQTGFGLIVTETVAQ 1250
1251 RALCFLCGSTGLDPLIFCACCCEPYHQYCVLDEYNLKHSSFEDTLMTSLL 1300
1301 ETSNNACAISAATNTALNQLTQRLNWLCPRCTVCYTCNMSSGSKVKCQKC 1350
1351 QKNYHSTCLGTSKRLLGADRPLICVNCLKCKSCATTKVSKFVGNLPMCTA 1400
1401 CFKLRKKGNFCPICQKCYDDNDFDLKMMECGDCNQWVHSKCEGLSDEQYN 1450
1451 LLSTLPESIEFICKKCARRCDVSRNKADEWRQAVMEEFKSSLYSVLKLLS 1500
1501 KSRQACALLKLSPRKNWRCCSAGAQPAKAHSQGKLQPKALQFTYNGLGSD 1550
1551 GESQNSDDIYEFKEQHSTNRKPSTPVPCSCLQPLSQSPSFSLVDIKQKIA 1600
1601 SNAYVSLAEFNYDMSQVIQQSNCDELDIAYKELLSEQFPWFQNETKACTD 1650
1651 ALEEDMFESCGYEELKESPTTYAEHHTASQAPRTGLLDIPLDDVDDLGGC 1700
1701 AVKTRLDTRVCLFCRKSGEGLSGEEARLLYCGHDCWVHINCAMWSAEVFE 1750
1751 EIDGSLQNVHSAVARGRMIKCTVCGNRGATVGCNVKSCGEHYHYPCARTI 1800
1801 DCAFLTDKSMYCPAHARNALKANGSPSVTYESNFEVSRPVYVELERKRKK 1850
1851 LIVPAKVQFHIGSVAVRQLGSIVPRFSDSFEAIVPINFLCSRLYWSSKEP 1900
1901 WKIVEYTVRTTIQNSYSSTLTLDAGRNFTVDHTNPNCSLVQLGLAQIARW 1950
1951 HSSLARSDLLDTDWAEFPNSYVPADENTEEEPQQNADLLPPEIKDAIFED 2000
2001 LPHELLDGISMLDIFMYEDLGDKTELFAMSEQSKDGTTATSQAGGASVII 2050
2051 CDEDTRNSNSLNKHLVLSNCCTASNPVDDAMLCAARSSSQEKECGDVLKK 2100
2101 TDTAPTRSWPKLDGGSVAAFKRRKLSKNIAEGVLLSLNQRSKKEMATVAG 2150
2151 ITRRQSVCGSSELPAEGSATMRTKSFTWSAAKCLFEKNESREEPAKLTIM 2200
2201 QMDGVDDSITEYRIIGSDGNLSTAQFTGQVKCERCQCTYRNYDSFQRHLG 2250
2251 SCEPMSTSESESETATGTAQLSAESLNELQKQALAAATLSNTGGLNYLQT 2300
2301 SFPQVQNLATLGQFGVQGLQGLQTLQLQPQSLGNGFFLSQPNAAQATSNG 2350
2351 NDVLQLYANSLQNLAANLGGGFTLTQPTMSTQAQPQLIALSTNPDGTQQF 2400
2401 IQLPQSNGATTQLLQTAAPLRCNATYQTLQATNSDKKIVLLFLEAGDPLQ 2450
2451 EVVTQAAQQATAAAHQKQLKSGHGVKPIQAKLQGQQQQQRHQQHQQHQQH 2500
2501 QQQQQQQQQQQQQQQTPITVAQHGGTTQLLGQNLLQPQLLFQSNAQPQTQ 2550
2551 QLLLPQTQAQNIISFVTGDGSQNQPLQYISIPTTNDFKPQQTTSTPTFLT 2600
2601 APGGGATFLQTDASGNLMLTTAPANSGLQMLTGQLQTQPQVIGTLIQPQT 2650
2651 LQLTTGADGTQTATAQQPLILGGATGGGTTGLEFATAPQVILATQPMYYG 2700
2701 LETIVQNTVMSSQQFVSTAMPGVLSQNSSFSATTTQVFQASKIEPIVDLP 2750
2751 AGYVVLNNAVDASGNTSWLQQSQTQATDDATAQLLQNAGFQFQTTPTTST 2800
2801 QQTMSTDYAPPLVVTAKVPPVAQMKRNTNANKSPISVLSKVQPQPQQSQV 2850
2851 VNKVLPTNVIQQQQQQQQQQQQQQQQMQPKQQLAGNANLKLTSQFQRQQQ 2900
2901 ANELKNKQAAGQQTGSTCGAPPSIASKPLQKKTNLIRPIHKVEVKPKIMK 2950
2951 QAPKLATSAASMQHHQQQQSPAAINQVAKVALLQQRLAPAPQPQQQEPQE 3000
3001 EQQHLHQQQQQQQQQQQHMQQHQQQQQQLSMPQLLRAQQPIISIVNTAEP 3050
3051 QAATQFVIRPALQAQAQPIQLQEQQSQQQQQQPAEQLINGKAARLQRYAS 3100
3101 NSLPTNVVNPLQQQRCASANNSSNSNVTQQNSTITINSRPTNRVLPMQQR 3150
3151 QEPTPLSNDVVVQSPTPPKPIEEPVPAGASTQKPIVKCYAQLEQKSPGYE 3200
3201 TELKTNITLDNLEQTNSITTMQLQQPQQGPIYGEQIFEKQSEAQVQLEKP 3250
3251 KHNDLMLLEATSCQQQQQQQQHMEMVVDNGFQLTSNESCLLEKHGFNVEA 3300
3301 VPMDTEDHYASMKNGSGGGAAEGIGQVDDAEEDEDDDDDFSLKMATSACN 3350
3351 DHEMSDSEEPAVKEKISKILDNLTNDDCSDSIATATTVEASAGYQQMVED 3400
3401 VLATTAAGSVSTDDETFTATAEAVEAAASYINEMAEAHELQLKQLQAGVE 3450
3451 LDLKKPKLDVPQQQPDTVPPNVVPTAAAPQQPPPMRDPKKISGPHLLYEI 3500
3501 QSEDGFTYKSSSIAEIWEKVFEAVQVARRAHGLTPLPEGPLADMSGVQMI 3550
3551 GLKTNALKYLIEQLPGVEKCVKYTPKYHKRNGNVSTAAGGGHARTAGSNP 3600
3601 AALAAGAESLIDYGSDQEELQENAYECARCEPYVSRSEYDMFSWLASRHR 3650
3651 KQPIQVFVQPSDNELVPRRGTGSNLPMAMKYRTLKETYKDYVGVFRSHIH 3700
3701 GRGLYCTKDIEAGEMVIEYAGELIRSTLTDKRERYYDSRGIGCYMFKIDD 3750
3751 NLVVDATMRGNAARFINHSCEPNCYSKVVDILGHKHIIIFALRRIVQGEE 3800
3801 LTYDYKFPFEDEKIPCSCGSKRCRKYLN 3828
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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