 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P98158 from www.uniprot.org...
The NucPred score for your sequence is 0.67 (see score help below)
1 MERGAAAAAWMLLLAIAACLEPVSSQECGSGNFRCDNGYCIPASWRCDGT 50
51 RDCLDDTDEIGCPPRSCESGLFLCPAEGTCIPSSWVCDEDKDCSDGADEQ 100
101 QNCAGTTCSAQQMTCSNGQCIPSEYRCDHVSDCPDGSDERNCHYPTCDQL 150
151 TCANGACYNTSQRCDQKVDCRDSSDEANCTTLCSQKEFECGSGECILRAY 200
201 VCDHDNDCEDNSDERNCNYDTCGGHQFTCSNGQCINQNWVCDGDDDCQDS 250
251 GDEDGCESNQSHHRCYPREWACPGSGRCISIDKVCDGVPDCPEGDDENNV 300
301 TSGRTCGMGVCSVLNCEYQCHQTPFGGECFCPPGHIINSNDSRTCIDFDD 350
351 CQIWGICDQKCENRQGRHQCLCEEGYILERGQHCKSSDSFSAASVIFSNG 400
401 RDLLVGDLHGRNFRILAESKNRGMVMGVDFHYQKHRVFWTDPMQEKVFST 450
451 DINGLNTQEILNVSVDTPENLAVDWINNKLYLVETKVNRIDVVNLEGNQR 500
501 VTLITENLGHPRGIALDPTVGYLFFSDWGSLSGQPKVERAFMDGSNRKDL 550
551 VTTKVGWPAGITLDLVSKRVYWVDSRYDYIETVTYDGIQRKTVARGGSLV 600
601 PHPFGISLFEEHVFFTDWTKMAVMKASKFTETNPQVYHQSSLRPHGVTVY 650
651 HALRQPNATNPCGSNNGGCAQVCVLSHRTDNGGLGYRCKCEFGFELDDDE 700
701 HRCVAVKNFLLFSSKTAVRGIPFTLSTQEDVMVPVTGSPSFFVGIDFDAQ 750
751 HSTVFYSDLSKDIIYKQKIDGTGKEVITANRLESVECLTFDWISRNLYWT 800
801 DGGLKSVTVLRLADKSRRQIISNLNNPRSIVVHPTAGYMFLSDWFRPAKI 850
851 MRAWSDGSHLMPIVNTSLGWPNGLAIDWSASRLYWVDAFFDKIEHSTLDG 900
901 LDRKRLGHVDQMTHPFGLTVFKDNVFITDWRLGAIIRVRKSDGGDMTVIR 950
951 RGISSVMHVKAYDADLQTGSNYCSQTTHANGDCSHFCFPVPNFQRVCGCP 1000
1001 YGMKLQRDQMTCEGDPAREPPTQQCGSLSFPCNNGKCVPSFFRCDGVDDC 1050
1051 HDNSDEHQCGVFNNTCSPSAFACVRGGQCIPGQWHCDRQNDCLDGSDEQN 1100
1101 CPTHATSSTCPSTSFTCDNHVCIPKDWVCDTDNDCSDGSDEKNCQASGTC 1150
1151 QPTQFRCPDHRCISPLYVCDGDKDCADGSDEAGCVLNCTSAQFKCADGSS 1200
1201 CINSRYRCDGVYDCRDNSDEAGCPTRPPGMCHPDEFQCQGDGTCIPNTWE 1250
1251 CDGHPDCIHGSDEHTGCVPKTCSPTHFLCDNGNCIYKAWICDGDNDCRDM 1300
1301 SDEKDCPTQPFHCPSTQWQCPGYSTCINLSALCDGVFDCPNGTDESPLCN 1350
1351 QDSCSHFNGGCTHQCMQGPFGATCLCPLGYQLANDTKTCEDINECDIPGF 1400
1401 CSQHCVNMRGSFRCACDPEYTLESDGRTCKVTGSENPLLVVASRDKIIVD 1450
1451 NITAHTHNLYSLVQDVSFVVALDFDSVTGRVFWSDLLQGKTWSVFQNGTD 1500
1501 KRVVHDSGLSVTEMIAVDWIGRNLYWTDYALETIEVSKIDGSHRTVLISK 1550
1551 NVTKPRGLALDPRMGDNVMFWSDWGHHPRIERASMDGTMRTVIVQEKIYW 1600
1601 PCGLSIDYPNRLIYFMDAYLDYIEFCDYDGHNRRQVIASDLVLHHPHALT 1650
1651 LFEDFVYWTDRGTRQVMQANKWHGGNQSVVMYSVHQPLGITAIHPSRQPP 1700
1701 SRNPCASASCSHLCLLSAQAPRHYSCACPSGWNLSDDSVNCVRGDQPFLM 1750
1751 SVRDNIIFGISLDPEVKSNDAMVPISGIQHGYDVEFDDSEQFIYWVENPG 1800
1801 EIHRVKTDGSNRTVFAPLSLLGSSLGLALDWVSRNIYYTTPASRSIEVLT 1850
1851 LKGDTRYGKTLIANDGTPLGVGFPVGIAVDPARGKLYWSDHGTDSGVPAK 1900
1901 IASANMDGTSLKILFTGNLQHLEVVTLDIQEQKLYWAVTSRGVIERGNVD 1950
1951 GTERMILVHHLAHPWGLVVYGSFLYYSDEQYEVIERVDKSSGNNKVVLRD 2000
2001 NVPYLRGLRVYHRRNAADSSNGCSNNPNACQQICLPVPGGMFSCACASGF 2050
2051 KLSPDGRSCSPYNSFMVVSMLPAVRGFSLELSDHSEAMVPVAGQGRNVLH 2100
2101 ADVDVANGFIYWCDFSSSVRSSNGIRRIKPDGSNFTNVVTYGIGANGIRG 2150
2151 VALDWAAGNLYFTNAFVYETLIEVLRINTTYRRVLLKVSVDMPRHIIVDP 2200
2201 KHRYLFWADYGQKPKIERSFLDCTNRTVLVSEGIVTPRGLAMDHDTGYIY 2250
2251 WVDDSLDLIARIHLDGGESQVVRYGSRYPTPYGITVFGESIIWVDRNLKK 2300
2301 VFQASKQPGNTDPPVVIRDKINLLRDVTIFDEHAQPLSPAELNNNPCLQS 2350
2351 NGGCSHFCFALPELPTPRCGCAFGTLGNDGKSCATSQEDFLIYSLNNSLR 2400
2401 SLHFDPRDHSLPFQVISVAGTAIALDYDRRNNRIFFTQKLNSLRGQISYV 2450
2451 SLYSGSSSPTVLLSNIGVTDGIAFDWINRRIYYSDFSNQTINSMAEDGSN 2500
2501 RAVIARVSKPRAIVLDPCRGYMYWTDWGTNAKIERATLGGNFRVPIVNTS 2550
2551 LVWPNGLALDLETDLLYWADASLQKIERSTLTGTNREVVVSTAFHSFGLT 2600
2601 VYGQYIYWTDLYTRKIYRANKYDGSDLVAMTTRLPTQPSGISTVVKTQRQ 2650
2651 QCSNPCDQFNGGCSHICAPGPNGAECQCPHEGNWYLANDNKYCVVDTGTR 2700
2701 CNQLQFTCLNGHCINQDWKCDNDNDCGDGSDELPTVCAFHTCRSTAFTCG 2750
2751 NGRCVPYHYRCDYYNDCGDNSDEAGCLFRNCNSTTEFTCSNGRCIPLSYV 2800
2801 CNGINNCHDNDTSDEKNCPPHTCPPDFTKCQTTNICVPRAFLCDGDNDCG 2850
2851 DGSDENPIYCASHTCRSNEFQCLSPQRCIPSYWFCDGEADCADGSDEPDT 2900
2901 CGHSVNTCRASQFQCDNGRCISGNWVCDGDNDCGDMSDEDQRHHCELQNC 2950
2951 SSTQFTCVNSRPPNRRCIPQYWVCDGDADCSDALDELQNCTMRTCSAGEF 3000
3001 SCANGRCVRQSFRCDRRNDCGDYSDERGCSYPPCHANQFTCQNGRCIPRF 3050
3051 FVCDEDNDCGDGSDEQEHLCHTPEPTCPLHQFRCDNGHCIEMGRVCNHVD 3100
3101 DCSDNSDEKGCGINECLDSSISRCDHNCTDTITSFYCSCLPGYKLMSDKR 3150
3151 SCVDIDECKESPQLCSQKCENVVGSYICKCAPGYIREPDGKSCRQNSNIE 3200
3201 PYLIFSNRYYIRNLTTDGSSYSLILQGLGNVVALDFDRVEKRLYWIDAEK 3250
3251 QIIERMFLNKTNRETIINHRLRRAESLAVDWVSRKLYWLDAILDCLFVSD 3300
3301 LEGRHRKMIAQHCVDANNTFCFEHPRGIVLHPQRGHVYWADWGVHAYIGR 3350
3351 IGMDGTNKSVIISTKIEWPNAITIDYTNDLLYWADAHLGYIEFSDLEGHH 3400
3401 RHTVYDGSLPHPFALTIFEDTVFWTDWNTRTVEKGNKYDGSGRVVLVNTT 3450
3451 HKPFDIHVYHPYRQPIMSNPCGTNNGGCSHLCLIKAGGRGFTCACPDDFQ 3500
3501 TVQLRDRTLCMPMCSSTQFLCGNNEKCIPIWWKCDGQKDCSDGSDEPDLC 3550
3551 PHRFCRLGQFQCRDGNCTSPQALCNARQDCADGSDEDRVLCEHHRCESNE 3600
3601 WQCANKRCIPQSWQCDSVNDCLDNSDEDTSHCASRTCRPGQFKCNNGRCI 3650
3651 PQSWKCDVDNDCGDYSDEPIDECTTAAYNCDNHTEFSCKTNYRCIPQWAV 3700
3701 CNGFDDCRDNSDEQGCESVPCHPSGDFRCANHHCIPLRWKCDGTDDCGDN 3750
3751 SDEENCVPRECSESEFRCADQQCIPSRWVCDQENDCGDNSDERDCEMKTC 3800
3801 HPEHFQCTSGHCVPKALACDGRADCLDASDESACPTRFPNGTYCPAAMFE 3850
3851 CKNHVCIQSFWICDGENDCVDGSDEEIHLCFNIPCESPQRFRCDNSRCVY 3900
3901 GHQLCNGVDDCGDGSDEKEEHCRKPTHKPCTDTEYKCSNGNCISQHYVCD 3950
3951 NVNDCGDLSDETGCNLGDNRTCAENICEQNCTQLSSGGFICSCRPGFKPS 4000
4001 TSDKNSCQDINECEEFGICPQSCRNSKGSYECFCVDGFKSMSTHYGERCA 4050
4051 ADGSPPLLLLPENVRIRKYNTSSEKFSEYLEEEEHIQTIDYDWDPEHIGL 4100
4101 SVVYYTVLAQGSQFGAIKRAYIPNFESGSNNPIREVDLGLKYLMQPDGLA 4150
4151 VDWVGRHIYWSDAKSQRIEVATLDGRYRKWLITTQLDQPAAIAVNPKLGL 4200
4201 MFWTDQGKQPKIESAWMNGEHRSVLVSENLGWPNGLSIDYLNDDRVYWSD 4250
4251 SKEDVIEAIKYDGTDRRLIINEAMKPFSLDIFEDKLYWVAKEKGEVWRQN 4300
4301 KFGKENKEKVLVVNPWLTQVRIFHQLRYNQSVSNPCKQVCSHLCLLRPGG 4350
4351 YSCACPQGSDFVTGSTVQCDAASELPVTMPPPCRCMHGGNCYFDENELPK 4400
4401 CKCSSGYSGEYCEVGLSRGIPPGTTMAVLLTFVIVIIVGALVLVGLFHYR 4450
4451 KTGSLLPTLPKLPSLSSLAKPSENGNGVTFRSGADVNMDIGVSPFGPETI 4500
4501 IDRSMAMNEHFVMEVGKQPVIFENPMYAAKDNTSKVALAVQGPSTGAQVT 4550
4551 VPENVENQNYGRPIDPSEIVPEPKPASPGADEIQGKKWNIFKRKPKQTTN 4600
4601 FENPIYAEMDSEVKDAVAVAPPPSPSLPAKASKRNLTPGYTATEDTFKDT 4650
4651 ANLVKEDSDV 4660
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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