SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching O88277 from www.uniprot.org...

The NucPred score for your sequence is 0.55 (see score help below)

   1  MTLVLLGLAILLLHRAACEKSLEETIPPLSWRFTHSLYNATIYENSAPKT    50
  51  YVESPVKMGMYLAEPHWVVKYRIISGDAAGVFKTEEHVVGNFCFLRIRTK   100
 101  SSNTALLNREVRDSYTLIVQASDKSLEFEALTQVVVHILDQNDLKPLFSP   150
 151  PSYRVTISEDRPLKSPICKVTATDADLGQNAEFYYAFNARSEVFAIHPTS   200
 201  GVVTVAGKLNVTRRGKYELQVLAVDRMRKISEGNGFGNLASLVIRVEPVH   250
 251  RKPPAINLVVLNPPEGDEGDIYAIVTVDTNGSGAEVDSLEVVGGDPGKYF   300
 301  KVLRSYAQGNEFNLVAVRDINWAEHPHGFNISLQTHSWSRFPPHSIIRAF   350
 351  HLPSWKLANLRFEKAVYRVKLSEFSPPGSRVALVKVTTALPNLRYSLKPS   400
 401  SRNTAFKLNARTGLITTTKLVDFHEQNQYQLHVKTSLGQATTTVIIDIVD   450
 451  CNNHAPVFNRSSYEGTLDENIPPGTSVLTVTATDQDHGDNGHITYSIAGP   500
 501  KAVPFSIDPLLGVISTTKPMDYELMKRIYTFRVRASDWGSPFRQEKEVSV   550
 551  SLRLKNLNDNQPMFEEVNCTVSLRQDVPVGKSIMAVSAIDMDELQNLKYE   600
 601  IVSGNEQDYFHLNHFSGVISLKRSFMNLTAVRPTIYSLKITASDGKNYAS   650
 651  PTTLKVTVVKDPHSEVPVQCDKTGVLTHITKTILQSAGLQSQELGEEEFT   700
 701  SLSNYQINHHSPQFEDHFPQSIDILEQVPINTPLARLAATDPDTGFHGKL   750
 751  VYVISDGNEEGCFDIELETGLLMVAAALDYETTSFYVLNVTVYDLGTPPK   800
 801  SSWKLLTVTVKDWNDNPPRFPPGGYQLTISEDTEVGTTIAELKTEDADSE   850
 851  DNRRVRYTLLTPTEKFSLHPFTGELVVTGHLDRESESQYILKAEARDQPT   900
 901  KGHQLFSVTDLIVTLEDINDNPPQCITEHRRLKVPEDMPLGTVLTFLDAS   950
 951  DPDLGPAGEVKYILVEDAHGTFQVHPMTGALSLEKELDFERRAGYNLSFW  1000
1001  ASDSGKPLSRRTLCHVEVLVMDVNENLHSPHFSSFVYQGQVQENSPAGTP  1050
1051  VMVVTAQDDDSGLDGELQYFLRAGTGLETFSINQDTGMLETLAPLDREFT  1100
1101  PYYWLTVLAVDRGSVPLSAVTEVYIEVTDINDNIPSMSRPVFYPSVLEDA  1150
1151  PLGTSVLQLEAWDPDSSSQGKLTFNLTSGNHLGHFIVHPFTGLLTTAKQL  1200
1201  DRENKDEYVLEVTVQDNGDPSLRSTSRVVVCILDVNDNPPMFSHKLFNVR  1250
1251  LSERLSPLSPEPVYRLVASDPDEGLNGSVTYSIEESDEESFRIDPVTGVV  1300
1301  SSSSTFAAGEYNILTIKATDSGQPALSTSVRLHIEWIPQPRPSSIPLSFD  1350
1351  ESYYSFTVMETDPVNHMVGVISVEGRPGLFWFHISDGDKDMDFDIEKTTG  1400
1401  SIVIARPLDTRRKSSYNLTVEVTDGFHTIATQVHIFMIANINHHRPQFLQ  1450
1451  DHYEIRVPQDTLPGVELLRVQATDQDHGKGLIYTILSSQDPGSANLFQLD  1500
1501  PSSGVLVTVGTLELHSGPSQHILTVMVRDQEMPIKRNFVWVTIHVEDGNL  1550
1551  HSPHFTQLRYEANVPDTTAPGTELLQVRAVDADRGANAEIHYSFLKGNSD  1600
1601  GFFNIDSLLGIITVAQRLYHVHLTRHALTVKAEDQGSPRRHDLALVVIHV  1650
1651  HPSDSSAPVFSKDEYFIEIPESVPIGSPILLLSAGSSSEVTYELREGNKD  1700
1701  SVFSMNSYSGLISTQKRLDHEKVPSYRLRIRGSNMAGVFTEVVALVYIID  1750
1751  ENDNPPAFGKPTFLGHISEAAPLHSLILGEDNSPLVVRASDSDREANSLL  1800
1801  VYKILEPEALKFFKIDPSMGTLTTTSELDFEDTPLFQFNIYVHDQGTPIL  1850
1851  FAPRSAKVIIHVRDVNDSPPRFSEQIYEVAVVEPIHPGMGLLTVQAEDND  1900
1901  SRVTYSIKTSNADEAVTIHPTTGQISVVNPATLRLFQKFSIRASDGLYHD  1950
1951  TAVVKISLTQVLDKSLQFDQDVYRARVTENTPHRKALVILGVHGNHLNDT  2000
2001  LSYFLLNGTDLFHMIESAGVLQTRGGTFDREQQDTHEVAVEVRDNRVPQR  2050
2051  VAQALVRVSVEDVNDNIPEFQHLPYYTVIQDGTEPGDVLFQVSATDKDLG  2100
2101  ANGSVTYGFAEDYAYFRIDPYVGDISLKKPFDYQALNKYHLRVIARDSGI  2150
2151  PPLQTEVEVHVTVRNKSNPLFQSPYYKVKVPENITLYTPILHTQARSPEG  2200
2201  LRLIYNIVEEEPLMLFTTDFKTGVLTVTGPLDYESKNKHVFTVRATDTAL  2250
2251  GSFSEATVEVLVEDINDNPPTFSQLVYTTSVSEGSPAQTPVIQLLASDQD  2300
2301  SGQNQDVSYQIVEDGSDVSKFFRINGSTGEIFTIQELDYETHQHFRVKVR  2350
2351  AMDKGDPPLTGETLVVVNVSDINDNPPKFREPQYEANVSELATCGHLVLK  2400
2401  VQALDPDIGDTSRLEYLILSGNQDRHFSINSTSGIISMFNLCKKQLDSSY  2450
2451  NLRVGASDGVFRATVPVYINTTNANKYSPEFQQNVYEAELAENAKVGTKV  2500
2501  IELLAIDKDSGPYGTVDYTIINKLAGERFFINPRGQITTLQKLDRENSTE  2550
2551  RVIAIKVMARDGGGKVAFCTVKIILTDENDNAPQFKASGYTVSIPSNVSR  2600
2601  DSPIIQVLAYDADEGRNADVTYSVDSTEDLAEEIIEVNPTTGVVKVKESL  2650
2651  VGLENRAVDFNIKAQDGGPPHWDSLVPVRLQVVPNEIPLPKFSEPLYTFS  2700
2701  APEDLPEGSEIGSVKAVAAQDPIIYSLVQGTTPESNSDDVFSLDQDTGVL  2750
2751  KVRKAMDHESTKWYQIDLMAHCPHEDTDLVSLVSVSIQVEDVNDNRPVFE  2800
2801  ADPYKAFLTENMPGGTTVIQVTANDQDTGSDGQVSYRLSVEPGSNIHELF  2850
2851  AVDSESGWITTLQELDCETQQTYRFYVVAFDHGQTIQLSSQALVEVSITD  2900
2901  ENDNPPRFASEDYRGSVVENNEPGELVATLKTLDADVSDQNRQVTCYITE  2950
2951  GDPLGQFSISQVGDEWRISSRKTLDREHIAKYLLRVTASDGKFQASVPVE  3000
3001  VFVVDINDNSPQCSQLLYTGKVREDVTPGHFILKVSAIDVDMDTNAQITY  3050
3051  SLHGPGAQEFKLDPHTGELTTLTVLDRERKDVYNLVAKATDGGGQSCQAE  3100
3101  VTLHIEDVNDNAPRFFPSHCDVAVFDNTTVKTPVAVVFARDPDQGANAQV  3150
3151  VYSLTDSADGQFSIDATSGVIRLEKPLQVRASSAVELTVRASDLGTPIPL  3200
3201  STLGTVTVSVVGLEDYLPIFLNAEHSTQVPEDAPIDMEVLHLATLTRPGS  3250
3251  EKTGYHITGGNEQGKFRLDAHTGILYVNGSLDFETNPKYFLSIECSRKSS  3300
3301  SSLSDVTTIVINVTDVNEHHPRFTHDLYTVRVLENAVVGDVILTVSASDD  3350
3351  DGPVNSAITYSLVGGNQLGHFTINPKKGKLQVAKALDWEQTPSYSLRLRA  3400
3401  TDSGQPPLHEDTEVAVEVVDVNDNPPRFFQLNYSTSVQENSPIGIKVLQL  3450
3451  ILDDPDSPQNGPPYFFRITEGNTGSVFRVTPDGWLVTAASLSKKAREWYQ  3500
3501  LHIEVSDSGLPPLSSSTLVRVQVTEQSRYPPSTLPLEISITKGEEEFQGG  3550
3551  MIGKIHATDRDPQDTLTYSLEQEGGLDRYFTVGASDGKIIASQGLPHGRY  3600
3601  SFNVTVSDGTFTTTTGVHVHVWHMEPEVPQQAVWLGFHQLTPEELVSDHW  3650
3651  RNLQRFLSNLLDVKRANIHLASLQPAEVTAGVDVLLVFERHSGTSYDLQE  3700
3701  LASAIAHSVREIEHSVGIRMRSALPVVPCQGQSCQDQTCQETVSLEPRVG  3750
3751  PSYSTARLSILTPRHHLGRNCSCNGTTLRFSGQSYVQYRPLEAQNWQIHF  3800
3801  YLKTLQPWALLMFTNETASISLKLANGFSHLEYHCPGGFYGNLSSRYPVN  3850
3851  DGQWHSMLLEERDTSVHLLVDITDNASLVIPEECQGLRTERQLLLGGLVP  3900
3901  SNPSSNVSLGFEGCLDAVVVNGERLELLGREKKMEGRLETWALSQCCWPG  3950
3951  TACSQSPCLNGGSCSPALGSGYLCRCPPPFSGRNCELGRENCTSAPCQEG  4000
4001  GTCVSSPEGTSCNCPHPYTGDRCEMEARGCSGGHCLITPEIKRGDWGQQE  4050
4051  FLVITVALPLVIIATVGLLLYCRRRKSHKPVTMEDPDLLARSIGVDTQAS  4100
4101  PAIELDPLNTSSCNNLNQPEPSKTSVPNELVTFGPSSKQRPMVCSVPPRL  4150
4151  PPAAVSSHPGHEPIIKRTWSGEELVYPSGAAVWPPTYSRKKHWEYPHPET  4200
4201  MQGTLPPSPRRHVGPAVMPDPTGLYGGFPFPLELENKRAPLPPRYSNQNL  4250
4251  EDLMPPRPPSPREHLLAPCLNEYTAISYYHSQFRQGGGGPCLAEGGYKGV  4300
4301  SMRLSRAGPSYADCEVNGGPATGRSQPRAPPNYEGSDMVESDYGSCEEVM  4350
4351  F                                                   4351

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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