 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9VLT5 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MSAHSGGTDWNSVVKALILNRTGALNKNEVVNLLKAITRCEHDFFEEESN 50
51 FTQFYTAFAALAADKLMQIKTICQTQICQLHDATAVLIRFIIYRLPRVSV 100
101 YETKWLLGALKMLCEGRECPASASSSMFDYNAVANVLKSCKHPESTTKSI 150
151 MPSSSSSGSGASNDKESPKSEIKRSRSDLSSVILQQLIAPLEPGKMTWVP 200
201 LSEEVTDCTEQILAANVEYFQEQNGVDTLLDVCVSLPILNRYRSKYMETI 250
251 NGGKSLYLPLTQVEATAVKSSMNHMLTDLTILSQAQALIEMQPLTPSRIE 300
301 RLSMCGIAALYNAVLTSIATSVLGMSQASSSQKQTASTSQGSGVGGSSGG 350
351 QSNKDHDDFEDQACSIVNKALEIYSNIGHMFKTSARIHVYQNHLCYGSWL 400
401 LISGIQGAMGASGSGGSSSDSASKSASKATKSGSEAGTAPTTPIARVNLF 450
451 KVQQGFGELNAAIANHSIKLLSELIEDLKVEAACGQSLESTELPEPAQFD 500
501 ILQNYSSLERIVRVLNTATLHQLFTFLATVAYRKACTLKRASAKDRTECE 550
551 PISYSDSTTYFNDSLSCSDNSEEDDSESYLGHWFKETLSPETHDDNANTS 600
601 TQERAEQKSALVPKLDEPHEYLDLAADIFCFLDQFLANRHAYMQRYVKAG 650
651 VSDQQMLLMANIIKDFDRDVMRNESDQGSGNAPAASAGAGTSAGASTKWQ 700
701 TSMIRFSGAAGRYIHNLISTSLLSEQLQSNLLQHLSISPWSTDTNTWPLQ 750
751 VYPSTLSVLVQILLLKPTQEKEAACLSVWHRLINTLVEGVCSSNTASDSD 800
801 YEDLNIEHAQLLLFLFHSLNLMQKKSILLLTAGGVIRCAEVCRGISEDRP 850
851 VKNSQIMLLSRLLLFLEYLMKHLYNAPPELLDQVRWNLFSVSSMPDTQKI 900
901 TDLLNCRTKLNSYCRHDIEENFRKSAGEYGSSIRPTFYSLVMGDPEISYW 950
951 AQEFKLDGLAWNFILCTPDKLKYPLLVDALTDILSITDMSMYSKEKDKEA 1000
1001 SMHNLCAIQYCFTIAWKLNLGLPPSTSHVESLKAERSPNLHSLMWSIRLP 1050
1051 LASSHYLVVSSLIKQGMYTQYAETLWTHVGDIGADIKYSLKQTILGVEAF 1100
1101 NSQMNGNGTPRLSDLILFDSLVAHMQAVAWANKEGLKWPRKESEDAAGEQ 1150
1151 SAGTSLTSSSNDPELYSSNESIDDQKLKQDDDGKLSSDLQKYNQLVNELM 1200
1201 VKLMNSYQLLSVIVRGQMLKQLSSSTPEKALNLIVPIVSDKPAIMLELHA 1250
1251 AFLKLLPNEDKQLIANEWPKCLMVNDFAFNGKQHPVEPYILNVIDAHIIE 1300
1301 LTRGSNYSTLHTLKHCLKSILQLFELLLPHRTANAEVETQLKQLLISSML 1350
1351 DMRTDYLQGHSEHCLREILSGLTQEAQKLLLYEHMVGYCYRMLKEFAADL 1400
1401 RQPQSAGPSGGAPLDQDRAMFNESMLFAVLKTMFKMLEKPVAVQAMRQFF 1450
1451 KDQRSGSLTTLLLSFTGTSLPVSYARKMLQFVNRLFQLSLQADSQFQHED 1500
1501 LVECFSELATVDVARLKQWLGHIIYGPNVSTDVSTSEALDTTCRMLTSIL 1550
1551 QPSSSSSSNAQTPTNMATVSAMPSISDQLDPMEIEYDCGTAAGGGESNPG 1600
1601 TGGAAANTSQILSLWQAAQPNPSEESSQACDHSDSERNGALLLSFVKSLV 1650
1651 KDQSKASQIAPPLFQALLQLGQTLISPPQEGCDFADVLQIMITLADASPA 1700
1701 RGHVALFNTTLLWLELAKLQLPDKHLKHAENVSAQLRYLSELLQSIGFRG 1750
1751 SRQHNPPWDDELQTDIDELYDELAEEGEQDSLLDDSDEDTLNNKLCTFSQ 1800
1801 TQKEFMNQHWYHCHTCNMINTVGVCSVCARVCHKGHDVSYAKYGNFFCDC 1850
1851 GAKEDGSCQALSRRIGSNEVRDSAGIGSYLPSHMSLLAGKKRSSLPVGQP 1900
1901 VLTRKDSLTNERIAVLTKLLEPYRETLQHQDQWLLVVRCILEYFDLLLPS 1950
1951 INENCMLYSIVGCHKRATAALERLHLLEQSFQVTDQLMFATLGSQEGAFE 2000
2001 NVRMNYSGDQGQTIKHLLSSGVVRRVAFCCLSSPHGRRQQLAVSHEKGKV 2050
2051 TILQLSALLKQADASKRKLTLTQLSSAPIACTVISLAANPCNEDCLAVCG 2100
2101 LKECHILTFSSSGSTNEHIVVNPQLENGNYIKKAVWLPGSQTLLAVVTSD 2150
2151 YVKIYDLAVDTYSPKYYYLVAVGKIRDCTFVYQDGNYNMLTFASSGYIYT 2200
2201 QQLDQQSLAVHGDFYVTNTLELSHQHIKDIAGHIGGGGVSIYYSHTLQLL 2250
2251 FYSYSCGRSFFSPLTNVSEGVKGIYHLDTNSASKSASKGPLQPLVQWTEV 2300
2301 TGHPGLVYASMQTSNNPIILMITPERIYLQEIKAQSAKSRIMDVVGIRHA 2350
2351 VAGVEKTTLLLLCEDGSLRIFSAQPEYTSFWLSPQVQPFGNQLYSSTLMA 2400
2401 KGGGSGSTSKSKSNTASGKMTSRKASQQQKQPTAGGQPVFPIDFFEHCNM 2450
2451 LADVEFGGNDLLQIYNKQKLKTRLFSTGMFVASTRSNGFTLEVINNDPNV 2500
2501 VIVGIRVLIGTQDVQRAPQSVTILGRTIPTPVRRARWFDIPLTREEMLQS 2550
2551 DKLLKVVFAKAPDPEHVTLLDCIEVYGKSKELVGWPDESEDVTVPGSSAP 2600
2601 AVSSSQASSANFGEGFNCITQLDRMANHLLEVMDCALHLLGSGVPASMRQ 2650
2651 KAVKTASALLLLPTPNPVQTQARYVLATLYGTRALYHNYKDGVLLQFVNR 2700
2701 ELQSMQPKLEKLETLREIDPEAFYRLVLMVRGIANARPQSLAKICVENNY 2750
2751 DIVPTLMGIVLELHKVTPTLDEPVNIVKRGLCQPETIVHCLVEIMYGFAL 2800
2801 ADPGQVGRMTKYFIDLLKHDASVISHSAKEALILLLSPRMKRRKVAIVTP 2850
2851 PACSTPTPSTSTMQALQAAASSAASDIIEEAAGVVVDGSVGGGGLPEPNA 2900
2901 DAEGAAVGVGGVGQQQMLNLEAFMGGGFPRLLGLPEDADDEAIMDIAIAL 2950
2951 SLQQHGGDANALQSLQQGLANLQGIRQATAMAAAVNAAANVSLGGSDDDE 3000
3001 GSNVATDGSTLRTSPAEPAGSGGSESGGSGVESIGGTSARSSNFGDHANA 3050
3051 SPPRQGSTKDDQEQPGPSGVAGSGGVAVLSAMSSSEDNEANEDDKLSKLH 3100
3101 DLRIAVLESIIQHLGTFDLCNGLQAIPLIQVILMLTTDLNGNNERDQQVL 3150
3151 HDLLTALVDYVEIGKRGAAARMETKCPGNEVRLALLSLFGVLMGKTKSKQ 3200
3201 TGTTSPPHQFKDNSSFVASTTANVLSKSGAFVYALEALNTLLVHWKNVLG 3250
3251 DPYAAGGGLASQSAQASGGASGPGVQLLKPIKHGPKPDISILIPHNYLKN 3300
3301 YPDIFESYDGLLTEIIVRLPYQILRLSSAHPDNYDSGFCEAMTFTLCEYM 3350
3351 MLNLNTLLRRQVRKLLMYICGSKEKFRMYRDGHSLDAHFRVVKRVCNIVS 3400
3401 SKTGAPYNANPPMLSYDALVELTEHLRTCQEISQMRTGNWQKFCVVHEDA 3450
3451 LAMLMEIACYQLDDGVSPIIIQLLQAAVCNLPPPSGSKQAQPQPSTSSAS 3500
3501 GKLRTDREKSEDTDAYYSKFDPAQCGTFVHQIFRYACDALIIRFVRIFLL 3550
3551 ENNITQLRWQAHSFMTGLFEHANERQREKLLNIFWNLWPLVPTYGRRTAQ 3600
3601 FVDLLGYLTLSTRSITERLPEFVSRAVDVLRQQNELLCKHPNAPIYTTLE 3650
3651 SILQVNGYYLESEPCLVCNNPEVPMANIKLPSVKSDSKYTTTTMIYKLVQ 3700
3701 CHTISKLIVRIADLKRTKMVRTINVYYNNRSVQAVVELKNRPALWHKARS 3750
3751 VSLQSAQTELKIDFPLPITACNLMIEFADFFETVSGSSENLQCPRCSAAV 3800
3801 PAYPGVCGNCGENVFQCHKCRAINYDEKDPFLCHSCGFCKYAKFDFSMYA 3850
3851 RVCCAVDPIESAEDRVKTVSLIHSSLERADRNYRQLLTNKQMLELLIQKV 3900
3901 AEHRSSDRMVEDNMASVHSTSQVNKIIQLLAQKYCVESRTSFEELSKIVQ 3950
3951 KVKACRSELVAYDRQQQDQPPVNPGSTTGAENPTTNRCYGCALASTEQCL 4000
4001 TLLRAMAYNYDCRVCLYSQGLVSELAEHNLRRGTPLIQEEVRNLLVVLTK 4050
4051 DNAEACMHLLQLVTTRVKNALMGSIPLISLEAAVHQEMTLLEVLLGQDDI 4100
4101 CWEYKLKVIFELFISNCRLPRGPVTAVLHPCLRIMQNLICPVLPGSKPNQ 4150
4151 KVATTDLCSMKMFEGNTVDYRAWLNSDRNHEYAAWSKRMPSNNQAKLKNA 4200
4201 KDQNVAASGGSDAPPKSRREVRVAFLSEKYGKRWRERVLDKQRVIKPLVF 4250
4251 NAKWIQPLLFNANSRFGRQLACSLLSSLSRTNERRQQALNMLTSFLKHVG 4300
4301 EAGEASAEYLMLYKNMATEQPWLQYLVLKGVLSQISQLLAIEISKVHRME 4350
4351 EYSLSSDLSLGYALRQYVELLWLLLECPNIRRTYKTRMLGPVLESYLALR 4400
4401 SLVVQRTRLIDDAQEKLLEMLEDMTSGTEEETRAFMEILIDTVEKTRMND 4450
4451 IKTPVFVFERLYSIIHPEEHDESEFYMTLEKDPQQEDFLQGRMLGNPYPS 4500
4501 SEMGLGPLMRDVKNKICTDCELIALLEDDNGMELLVNNKIISLDLPVKDV 4550
4551 YKKVWLAEGGDRDAMRIVYRMRGLLGDATEEFVETLNNKSQEQVDTEQLY 4600
4601 RMANVLADCNGLRVMLERIGSLQRISRNRELIQVLLKLFLICVKVRRCQE 4650
4651 VLCQPEIGAINTLLKVLQMCLQSENDSIQSAVTEQLLEIMETILSKAASD 4700
4701 TLDSFLQFSLTFGGPEYVSALISCTDCPNVRNNPSVLRHLIRVLAALVYG 4750
4751 NEVKMALLCEHFKDTLNFNRFDNERTPEEEFKLELFCVLTNQIEHNCIGG 4800
4801 TLKDYIVSLGIVERSLAYITEHAPCVKPTLLRTDSDELKEFISRPSLKYI 4850
4851 LRFLTGLSNHHEATQVAISKDIIPIIHRLEQVSSDEHVGSLAENLLEALS 4900
4901 TDSATAARVQQVRDFTRAEKKRLAMATREKQLDALGMRTNEKGQVTAKGS 4950
4951 ILQKIEKLRDETGLTCFICREGYACQPDKVLGIYTFTKRCNVEEFELKSR 5000
5001 KTIGYTTVTHFNVVHVDCHTSAIRLTRGRDEWERASLQNANTRCNGLLPL 5050
5051 WGPAVGEAAFSACMTRHSSYMQESTQRCDISYTSSVHDLKLLLVRFAWER 5100
5101 SFHDDAGGGGPQSNMHFVPYLLFYSVYLLLSSRSAARDSKTLLTYLQAPP 5150
5151 SEKWLECGYEVDGPLFMATISLSLHSRELWNKHKLAHLKRMIAVAQGRHV 5200
5201 SPAVLCKALLAPADRQVKDYTVYKPFLMMWALVDLIYDNLFKTVSTPKEE 5250
5251 DWPISLFDYLRKNDEALLKSTDSILQTLTEEFLPCTSFVEFCDVAGLLHL 5300
5301 IEHPDNFIEEILAALPSTSSSN 5322
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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