 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q96RW7 from www.uniprot.org...
The NucPred score for your sequence is 0.46 (see score help below)
1 MISWEVVHTVFLFALLYSSLAQDASPQSEIRAEEIPEGASTLAFVFDVTG 50
51 SMYDDLVQVIEGASKILETSLKRPKRPLFNFALVPFHDPEIGPVTITTDP 100
101 KKFQYELRELYVQGGGDCPEMSIGAIKIALEISLPGSFIYVFTDARSKDY 150
151 RLTHEVLQLIQQKQSQVVFVLTGDCDDRTHIGYKVYEEIASTSSGQVFHL 200
201 DKKQVNEVLKWVEEAVQASKVHLLSTDHLEQAVNTWRIPFDPSLKEVTVS 250
251 LSGPSPMIEIRNPLGKLIKKGFGLHELLNIHNSAKVVNVKEPEAGMWTVK 300
301 TSSSGRHSVRITGLSTIDFRAGFSRKPTLDFKKTVSRPVQGIPTYVLLNT 350
351 SGISTPARIDLLELLSISGSSLKTIPVKYYPHRKPYGIWNISDFVPPNEA 400
401 FFLKVTGYDKDDYLFQRVSSVSFSSIVPDAPKVTMPEKTPGYYLQPGQIP 450
451 CSVDSLLPFTLSFVRNGVTLGVDQYLKESASVNLDIAKVTLSDEGFYECI 500
501 AVSSAGTGRAQTFFDVSEPPPVIQVPNNVTVTPGERAVLTCLIISAVDYN 550
551 LTWQRNDRDVRLAEPARIRTLANLSLELKSVKFNDAGEYHCMVSSEGGSS 600
601 AASVFLTVQEPPKVTVMPKNQSFTGGSEVSIMCSATGYPKPKIAWTVNDM 650
651 FIVGSHRYRMTSDGTLFIKNAAPKDAGIYGCLASNSAGTDKQNSTLRYIE 700
701 APKLMVVQSELLVALGDITVMECKTSGIPPPQVKWFKGDLELRPSTFLII 750
751 DPLLGLLKIQETQDLDAGDYTCVAINEAGRATGKITLDVGSPPVFIQEPA 800
801 DVSMEIGSNVTLPCYVQGYPEPTIKWRRLDNMPIFSRPFSVSSISQLRTG 850
851 ALFILNLWASDKGTYICEAENQFGKIQSETTVTVTGLVAPLIGISPSVAN 900
901 VIEGQQLTLPCTLLAGNPIPERRWIKNSAMLLQNPYITVRSDGSLHIERV 950
951 QLQDGGEYTCVASNVAGTNNKTTSVVVHVLPTIQHGQQILSTIEGIPVTL 1000
1001 PCKASGNPKPSVIWSKKGELISTSSAKFSAGADGSLYVVSPGGEESGEYV 1050
1051 CTATNTAGYAKRKVQLTVYVRPRVFGDQRGLSQDKPVEISVLAGEEVTLP 1100
1101 CEVKSLPPPIITWAKETQLISPFSPRHTFLPSGSMKITETRTSDSGMYLC 1150
1151 VATNIAGNVTQAVKLNVHVPPKIQRGPKHLKVQVGQRVDIPCNAQGTPLP 1200
1201 VITWSKGGSTMLVDGEHHVSNPDGTLSIDQATPSDAGIYTCVATNIAGTD 1250
1251 ETEITLHVQEPPTVEDLEPPYNTTFQERVANQRIEFPCPAKGTPKPTIKW 1300
1301 LHNGRELTGREPGISILEDGTLLVIASVTPYDNGEYICVAVNEAGTTERK 1350
1351 YNLKVHVPPVIKDKEQVTNVSVLLNQLTNLFCEVEGTPSPIIMWYKDNVQ 1400
1401 VTESSTIQTVNNGKILKLFRATPEDAGRYSCKAINIAGTSQKYFNIDVLV 1450
1451 PPTIIGTNFPNEVSVVLNRDVALECQVKGTPFPDIHWFKDGKPLFLGDPN 1500
1501 VELLDRGQVLHLKNARRNDKGRYQCTVSNAAGKQAKDIKLTIYIPPSIKG 1550
1551 GNVTTDISVLINSLIKLECETRGLPMPAITWYKDGQPIMSSSQALYIDKG 1600
1601 QYLHIPRAQVSDSATYTCHVANVAGTAEKSFHVDVYVPPMIEGNLATPLN 1650
1651 KQVVIAHSLTLECKAAGNPSPILTWLKDGVPVKANDNIRIEAGGKKLEIM 1700
1701 SAQEIDRGQYICVATSVAGEKEIKYEVDVLVPPAIEGGDETSYFIVMVNN 1750
1751 LLELDCHVTGSPPPTIMWLKDGQLIDERDGFKILLNGRKLVIAQAQVSNT 1800
1801 GLYRCMAANTAGDHKKEFEVTVHVPPTIKSSGLSERVVVKYKPVALQCIA 1850
1851 NGIPNPSITWLKDDQPVNTAQGNLKIQSSGRVLQIAKTLLEDAGRYTCVA 1900
1901 TNAAGETQQHIQLHVHEPPSLEDAGKMLNETVLVSNPVQLECKAAGNPVP 1950
1951 VITWYKDNRLLSGSTSMTFLNRGQIIDIESAQISDAGIYKCVAINSAGAT 2000
2001 ELFYSLQVHVAPSISGSNNMVAVVVNNPVRLECEARGIPAPSLTWLKDGS 2050
2051 PVSSFSNGLQVLSGGRILALTSAQISDTGRYTCVAVNAAGEKQRDIDLRV 2100
2101 YVPPNIMGEEQNVSVLISQAVELLCQSDAIPPPTLTWLKDGHPLLKKPGL 2150
2151 SISENRSVLKIEDAQVQDTGRYTCEATNVAGKTEKNYNVNIWVPPNIGGS 2200
2201 DELTQLTVIEGNLISLLCESSGIPPPNLIWKKKGSPVLTDSMGRVRILSG 2250
2251 GRQLQISIAEKSDAALYSCVASNVAGTAKKEYNLQVYIRPTITNSGSHPT 2300
2301 EIIVTRGKSISLECEVQGIPPPTVTWMKDGHPLIKAKGVEILDEGHILQL 2350
2351 KNIHVSDTGRYVCVAVNVAGMTDKKYDLSVHAPPSIIGNHRSPENISVVE 2400
2401 KNSVSLTCEASGIPLPSITWFKDGWPVSLSNSVRILSGGRMLRLMQTTME 2450
2451 DAGQYTCVVRNAAGEERKIFGLSVLVPPHIVGENTLEDVKVKEKQSVTLT 2500
2501 CEVTGNPVPEITWHKDGQPLQEDEAHHIISGGRFLQITNVQVPHTGRYTC 2550
2551 LASSPAGHKSRSFSLNVFVSPTIAGVGSDGNPEDVTVILNSPTSLVCEAY 2600
2601 SYPPATITWFKDGTPLESNRNIRILPGGRTLQILNAQEDNAGRYSCVATN 2650
2651 EAGEMIKHYEVKVYIPPIINKGDLWGPGLSPKEVKIKVNNTLTLECEAYA 2700
2701 IPSASLSWYKDGQPLKSDDHVNIAANGHTLQIKEAQISDTGRYTCVASNI 2750
2751 AGEDELDFDVNIQVPPSFQKLWEIGNMLDTGRNGEAKDVIINNPISLYCE 2800
2801 TNAAPPPTLTWYKDGHPLTSSDKVLILPGGRVLQIPRAKVEDAGRYTCVA 2850
2851 VNEAGEDSLQYDVRVLVPPIIKGANSDLPEEVTVLVNKSALIECLSSGSP 2900
2901 APRNSWQKDGQPLLEDDHHKFLSNGRILQILNTQITDIGRYVCVAENTAG 2950
2951 SAKKYFNLNVHVPPSVIGPKSENLTVVVNNFISLTCEVSGFPPPDLSWLK 3000
3001 NEQPIKLNTNTLIVPGGRTLQIIRAKVSDGGEYTCIAINQAGESKKKFSL 3050
3051 TVYVPPSIKDHDSESLSVVNVREGTSVSLECESNAVPPPVITWYKNGRMI 3100
3101 TESTHVEILADGQMLHIKKAEVSDTGQYVCRAINVAGRDDKNFHLNVYVP 3150
3151 PSIEGPEREVIVETISNPVTLTCDATGIPPPTIAWLKNHKRIENSDSLEV 3200
3201 RILSGGSKLQIARSQHSDSGNYTCIASNMEGKAQKYYFLSIQVPPSVAGA 3250
3251 EIPSDVSVLLGENVELVCNANGIPTPLIQWLKDGKPIASGETERIRVSAN 3300
3301 GSTLNIYGALTSDTGKYTCVATNPAGEEDRIFNLNVYVTPTIRGNKDEAE 3350
3351 KLMTLVDTSINIECRATGTPPPQINWLKNGLPLPLSSHIRLLAAGQVIRI 3400
3401 VRAQVSDVAVYTCVASNRAGVDNKHYNLQVFAPPNMDNSMGTEEITVLKG 3450
3451 SSTSMACITDGTPAPSMAWLRDGQPLGLDAHLTVSTHGMVLQLLKAETED 3500
3501 SGKYTCIASNEAGEVSKHFILKVLEPPHINGSEEHEEISVIVNNPLELTC 3550
3551 IASGIPAPKMTWMKDGRPLPQTDQVQTLGGGEVLRISTAQVEDTGRYTCL 3600
3601 ASSPAGDDDKEYLVRVHVPPNIAGTDEPRDITVLRNRQVTLECKSDAVPP 3650
3651 PVITWLRNGERLQATPRVRILSGGRYLQINNADLGDTANYTCVASNIAGK 3700
3701 TTREFILTVNVPPNIKGGPQSLVILLNKSTVLECIAEGVPTPRITWRKDG 3750
3751 AVLAGNHARYSILENGFLHIQSAHVTDTGRYLCMATNAAGTDRRRIDLQV 3800
3801 HVPPSIAPGPTNMTVIVNVQTTLACEATGIPKPSINWRKNGHLLNVDQNQ 3850
3851 NSYRLLSSGSLVIISPSVDDTATYECTVTNGAGDDKRTVDLTVQVPPSIA 3900
3901 DEPTDFLVTKHAPAVITCTASGVPFPSIHWTKNGIRLLPRGDGYRILSSG 3950
3951 AIEILATQLNHAGRYTCVARNAAGSAHRHVTLHVHEPPVIQPQPSELHVI 4000
4001 LNNPILLPCEATGTPSPFITWQKEGINVNTSGRNHAVLPSGGLQISRAVR 4050
4051 EDAGTYMCVAQNPAGTALGKIKLNVQVPPVISPHLKEYVIAVDKPITLSC 4100
4101 EADGLPPPDITWHKDGRAIVESIRQRVLSSGSLQIAFVQPGDAGHYTCMA 4150
4151 ANVAGSSSTSTKLTVHVPPRIRSTEGHYTVNENSQAILPCVADGIPTPAI 4200
4201 NWKKDNVLLANLLGKYTAEPYGELILENVVLEDSGFYTCVANNAAGEDTH 4250
4251 TVSLTVHVLPTFTELPGDVSLNKGEQLRLSCKATGIPLPKLTWTFNNNII 4300
4301 PAHFDSVNGHSELVIERVSKEDSGTYVCTAENSVGFVKAIGFVYVKEPPV 4350
4351 FKGDYPSNWIEPLGGNAILNCEVKGDPTPTIQWNRKGVDIEISHRIRQLG 4400
4401 NGSLAIYGTVNEDAGDYTCVATNEAGVVERSMSLTLQSPPIITLEPVETV 4450
4451 INAGGKIILNCQATGEPQPTITWSRQGHSISWDDRVNVLSNNSLYIADAQ 4500
4501 KEDTSEFECVARNLMGSVLVRVPVIVQVHGGFSQWSAWRACSVTCGKGIQ 4550
4551 KRSRLCNQPLPANGGKPCQGSDLEMRNCQNKPCPVDGSWSEWSLWEECTR 4600
4601 SCGRGNQTRTRTCNNPSVQHGGRPCEGNAVEIIMCNIRPCPVHGAWSAWQ 4650
4651 PWGTCSESCGKGTQTRARLCNNPPPAFGGSYCDGAETQMQVCNERNCPIH 4700
4701 GKWATWASWSACSVSCGGGARQRTRGCSDPVPQYGGRKCEGSDVQSDFCN 4750
4751 SDPCPTHGNWSPWSGWGTCSRTCNGGQMRRYRTCDNPPPSNGGRACGGPD 4800
4801 SQIQRCNTDMCPVDGSWGSWHSWSQCSASCGGGEKTRKRLCDHPVPVKGG 4850
4851 RPCPGDTTQVTRCNVQACPGGPQRARGSVIGNINDVEFGIAFLNATITDS 4900
4901 PNSDTRIIRAKITNVPRSLGSAMRKIVSILNPIYWTTAKEIGEAVNGFTL 4950
4951 TNAVFKRETQVEFATGEILQMSHIARGLDSDGSLLLDIVVSGYVLQLQSP 5000
5001 AEVTVKDYTEDYIQTGPGQLYAYSTRLFTIDGISIPYTWNHTVFYDQAQG 5050
5051 RMPFLVETLHASSVESDYNQIEETLGFKIHASISKGDRSNQCPSGFTLDS 5100
5101 VGPFCADEDECAAGNPCSHSCHNAMGTYYCSCPKGLTIAADGRTCQDIDE 5150
5151 CALGRHTCHAGQDCDNTIGSYRCVVRCGSGFRRTSDGLSCQDINECQESS 5200
5201 PCHQRCFNAIGSFHCGCEPGYQLKGRKCMDVNECRQNVCRPDQHCKNTRG 5250
5251 GYKCIDLCPNGMTKAENGTCIDIDECKDGTHQCRYNQICENTRGSYRCVC 5300
5301 PRGYRSQGVGRPCMDINECEQVPKPCAHQCSNTPGSFKCICPPGQHLLGD 5350
5351 GKSCAGLERLPNYGTQYSSYNLARFSPVRNNYQPQQHYRQYSHLYSSYSE 5400
5401 YRNSRTSLSRTRRTIRKTCPEGSEASHDTCVDIDECENTDACQHECKNTF 5450
5451 GSYQCICPPGYQLTHNGKTCQDIDECLEQNVHCGPNRMCFNMRGSYQCID 5500
5501 TPCPPNYQRDPVSGFCLKNCPPNDLECALSPYALEYKLVSLPFGIATNQD 5550
5551 LIRLVAYTQDGVMHPRTTFLMVDEEQTVPFALRDENLKGVVYTTRPLREA 5600
5601 ETYRMRVRASSYSANGTIEYQTTFIVYIAVSAYPY 5635
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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