 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P98160 from www.uniprot.org...
The NucPred score for your sequence is 0.46 (see score help below)
1 MGWRAAGALLLALLLHGRLLAVTHGLRAYDGLSLPEDIETVTASQMRWTH 50
51 SYLSDDEDMLADSISGDDLGSGDLGSGDFQMVYFRALVNFTRSIEYSPQL 100
101 EDAGSREFREVSEAVVDTLESEYLKIPGDQVVSVVFIKELDGWVFVELDV 150
151 GSEGNADGAQIQEMLLRVISSGSVASYVTSPQGFQFRRLGTVPQFPRACT 200
201 EAEFACHSYNECVALEYRCDRRPDCRDMSDELNCEEPVLGISPTFSLLVE 250
251 TTSLPPRPETTIMRQPPVTHAPQPLLPGSVRPLPCGPQEAACRNGHCIPR 300
301 DYLCDGQEDCEDGSDELDCGPPPPCEPNEFPCGNGHCALKLWRCDGDFDC 350
351 EDRTDEANCPTKRPEEVCGPTQFRCVSTNMCIPASFHCDEESDCPDRSDE 400
401 FGCMPPQVVTPPRESIQASRGQTVTFTCVAIGVPTPIINWRLNWGHIPSH 450
451 PRVTVTSEGGRGTLIIRDVKESDQGAYTCEAMNARGMVFGIPDGVLELVP 500
501 QRGPCPDGHFYLEHSAACLPCFCFGITSVCQSTRRFRDQIRLRFDQPDDF 550
551 KGVNVTMPAQPGTPPLSSTQLQIDPSLHEFQLVDLSRRFLVHDSFWALPE 600
601 QFLGNKVDSYGGSLRYNVRYELARGMLEPVQRPDVVLMGAGYRLLSRGHT 650
651 PTQPGALNQRQVQFSEEHWVHESGRPVQRAELLQVLQSLEAVLIQTVYNT 700
701 KMASVGLSDIAMDTTVTHATSHGRAHSVEECRCPIGYSGLSCESCDAHFT 750
751 RVPGGPYLGTCSGCNCNGHASSCDPVYGHCLNCQHNTEGPQCNKCKAGFF 800
801 GDAMKATATSCRPCPCPYIDASRRFSDTCFLDTDGQATCDACAPGYTGRR 850
851 CESCAPGYEGNPIQPGGKCRPVNQEIVRCDERGSMGTSGEACRCKNNVVG 900
901 RLCNECADGSFHLSTRNPDGCLKCFCMGVSRHCTSSSWSRAQLHGASEEP 950
951 GHFSLTNAASTHTTNEGIFSPTPGELGFSSFHRLLSGPYFWSLPSRFLGD 1000
1001 KVTSYGGELRFTVTQRSQPGSTPLHGQPLVVLQGNNIILEHHVAQEPSPG 1050
1051 QPSTFIVPFREQAWQRPDGQPATREHLLMALAGIDTLLIRASYAQQPAES 1100
1101 RVSGISMDVAVPEETGQDPALEVEQCSCPPGYRGPSCQDCDTGYTRTPSG 1150
1151 LYLGTCERCSCHGHSEACEPETGACQGCQHHTEGPRCEQCQPGYYGDAQR 1200
1201 GTPQDCQLCPCYGDPAAGQAAHTCFLDTDGHPTCDACSPGHSGRHCERCA 1250
1251 PGYYGNPSQGQPCQRDSQVPGPIGCNCDPQGSVSSQCDAAGQCQCKAQVE 1300
1301 GLTCSHCRPHHFHLSASNPDGCLPCFCMGITQQCASSAYTRHLISTHFAP 1350
1351 GDFQGFALVNPQRNSRLTGEFTVEPVPEGAQLSFGNFAQLGHESFYWQLP 1400
1401 ETYQGDKVAAYGGKLRYTLSYTAGPQGSPLSDPDVQITGNNIMLVASQPA 1450
1451 LQGPERRSYEIMFREEFWRRPDGQPATREHLLMALADLDELLIRATFSSV 1500
1501 PLAASISAVSLEVAQPGPSNRPRALEVEECRCPPGYIGLSCQDCAPGYTR 1550
1551 TGSGLYLGHCELCECNGHSDLCHPETGACSQCQHNAAGEFCELCAPGYYG 1600
1601 DATAGTPEDCQPCACPLTNPENMFSRTCESLGAGGYRCTACEPGYTGQYC 1650
1651 EQCGPGYVGNPSVQGGQCLPETNQAPLVVEVHPARSIVPQGGSHSLRCQV 1700
1701 SGSPPHYFYWSREDGRPVPSGTQQRHQGSELHFPSVQPSDAGVYICTCRN 1750
1751 LHQSNTSRAELLVTEAPSKPITVTVEEQRSQSVRPGADVTFICTAKSKSP 1800
1801 AYTLVWTRLHNGKLPTRAMDFNGILTIRNVQLSDAGTYVCTGSNMFAMDQ 1850
1851 GTATLHVQASGTLSAPVVSIHPPQLTVQPGQLAEFRCSATGSPTPTLEWT 1900
1901 GGPGGQLPAKAQIHGGILRLPAVEPTDQAQYLCRAHSSAGQQVARAVLHV 1950
1951 HGGGGPRVQVSPERTQVHAGRTVRLYCRAAGVPSATITWRKEGGSLPPQA 2000
2001 RSERTDIATLLIPAITTADAGFYLCVATSPAGTAQARIQVVVLSASDASP 2050
2051 PPVKIESSSPSVTEGQTLDLNCVVAGSAHAQVTWYRRGGSLPPHTQVHGS 2100
2101 RLRLPQVSPADSGEYVCRVENGSGPKEASITVSVLHGTHSGPSYTPVPGS 2150
2151 TRPIRIEPSSSHVAEGQTLDLNCVVPGQAHAQVTWHKRGGSLPARHQTHG 2200
2201 SLLRLHQVTPADSGEYVCHVVGTSGPLEASVLVTIEASVIPGPIPPVRIE 2250
2251 SSSSTVAEGQTLDLSCVVAGQAHAQVTWYKRGGSLPARHQVRGSRLYIFQ 2300
2301 ASPADAGQYVCRASNGMEASITVTVTGTQGANLAYPAGSTQPIRIEPSSS 2350
2351 QVAEGQTLDLNCVVPGQSHAQVTWHKRGGSLPVRHQTHGSLLRLYQASPA 2400
2401 DSGEYVCRVLGSSVPLEASVLVTIEPAGSVPALGVTPTVRIESSSSQVAE 2450
2451 GQTLDLNCLVAGQAHAQVTWHKRGGSLPARHQVHGSRLRLLQVTPADSGE 2500
2501 YVCRVVGSSGTQEASVLVTIQQRLSGSHSQGVAYPVRIESSSASLANGHT 2550
2551 LDLNCLVASQAPHTITWYKRGGSLPSRHQIVGSRLRIPQVTPADSGEYVC 2600
2601 HVSNGAGSRETSLIVTIQGSGSSHVPSVSPPIRIESSSPTVVEGQTLDLN 2650
2651 CVVARQPQAIITWYKRGGSLPSRHQTHGSHLRLHQMSVADSGEYVCRANN 2700
2701 NIDALEASIVISVSPSAGSPSAPGSSMPIRIESSSSHVAEGETLDLNCVV 2750
2751 PGQAHAQVTWHKRGGSLPSHHQTRGSRLRLHHVSPADSGEYVCRVMGSSG 2800
2801 PLEASVLVTIEASGSSAVHVPAPGGAPPIRIEPSSSRVAEGQTLDLKCVV 2850
2851 PGQAHAQVTWHKRGGNLPARHQVHGPLLRLNQVSPADSGEYSCQVTGSSG 2900
2901 TLEASVLVTIEPSSPGPIPAPGLAQPIYIEASSSHVTEGQTLDLNCVVPG 2950
2951 QAHAQVTWYKRGGSLPARHQTHGSQLRLHLVSPADSGEYVCRAASGPGPE 3000
3001 QEASFTVTVPPSEGSSYRLRSPVISIDPPSSTVQQGQDASFKCLIHDGAA 3050
3051 PISLEWKTRNQELEDNVHISPNGSIITIVGTRPSNHGTYRCVASNAYGVA 3100
3101 QSVVNLSVHGPPTVSVLPEGPVWVKVGKAVTLECVSAGEPRSSARWTRIS 3150
3151 STPAKLEQRTYGLMDSHAVLQISSAKPSDAGTYVCLAQNALGTAQKQVEV 3200
3201 IVDTGAMAPGAPQVQAEEAELTVEAGHTATLRCSATGSPAPTIHWSKLRS 3250
3251 PLPWQHRLEGDTLIIPRVAQQDSGQYICNATSPAGHAEATIILHVESPPY 3300
3301 ATTVPEHASVQAGETVQLQCLAHGTPPLTFQWSRVGSSLPGRATARNELL 3350
3351 HFERAAPEDSGRYRCRVTNKVGSAEAFAQLLVQGPPGSLPATSIPAGSTP 3400
3401 TVQVTPQLETKSIGASVEFHCAVPSDRGTQLRWFKEGGQLPPGHSVQDGV 3450
3451 LRIQNLDQSCQGTYICQAHGPWGKAQASAQLVIQALPSVLINIRTSVQTV 3500
3501 VVGHAVEFECLALGDPKPQVTWSKVGGHLRPGIVQSGGVVRIAHVELADA 3550
3551 GQYRCTATNAAGTTQSHVLLLVQALPQISMPQEVRVPAGSAAVFPCIASG 3600
3601 YPTPDISWSKLDGSLPPDSRLENNMLMLPSVRPQDAGTYVCTATNRQGKV 3650
3651 KAFAHLQVPERVVPYFTQTPYSFLPLPTIKDAYRKFEIKITFRPDSADGM 3700
3701 LLYNGQKRVPGSPTNLANRQPDFISFGLVGGRPEFRFDAGSGMATIRHPT 3750
3751 PLALGHFHTVTLLRSLTQGSLIVGDLAPVNGTSQGKFQGLDLNEELYLGG 3800
3801 YPDYGAIPKAGLSSGFIGCVRELRIQGEEIVFHDLNLTAHGISHCPTCRD 3850
3851 RPCQNGGQCHDSESSSYVCVCPAGFTGSRCEHSQALHCHPEACGPDATCV 3900
3901 NRPDGRGYTCRCHLGRSGLRCEEGVTVTTPSLSGAGSYLALPALTNTHHE 3950
3951 LRLDVEFKPLAPDGVLLFSGGKSGPVEDFVSLAMVGGHLEFRYELGSGLA 4000
4001 VLRSAEPLALGRWHRVSAERLNKDGSLRVNGGRPVLRSSPGKSQGLNLHT 4050
4051 LLYLGGVEPSVPLSPATNMSAHFRGCVGEVSVNGKRLDLTYSFLGSQGIG 4100
4101 QCYDSSPCERQPCQHGATCMPAGEYEFQCLCRDGFKGDLCEHEENPCQLR 4150
4151 EPCLHGGTCQGTRCLCLPGFSGPRCQQGSGHGIAESDWHLEGSGGNDAPG 4200
4201 QYGAYFHDDGFLAFPGHVFSRSLPEVPETIELEVRTSTASGLLLWQGVEV 4250
4251 GEAGQGKDFISLGLQDGHLVFRYQLGSGEARLVSEDPINDGEWHRVTALR 4300
4301 EGRRGSIQVDGEELVSGRSPGPNVAVNAKGSVYIGGAPDVATLTGGRFSS 4350
4351 GITGCVKNLVLHSARPGAPPPQPLDLQHRAQAGANTRPCPS 4391
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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