 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P55200 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MAHSCRWRFPARPGTTGGGGGGGRRGLGGAPRQRVPALLLPPGPQAGGGG 50
51 PGAPPSPPAVAAAAAGSSGAGVPGGAAAASAASSSSASSSSSSSSSASSG 100
101 PALLRVGPGFDAALQVSAAIGTNLRRFRAVFGESGGGGGSGEDEQFLGFG 150
151 SDEEVRVRSPTRSPSVKASPRKPRGRPRSGSDRNPAILSDPSVFSPLNKS 200
201 ETKSADKIKKKDSKSIEKKRGRPPTFPGVKIKITHGKDIAELTQGSKEDS 250
251 LKKVKRTPSAMFQQATKIKKLRAGKLSPLKSKFKTGKLQIGRKGVQIVRR 300
301 RGRPPSTERIKTPSGLLINSELEKPQKVRKDKEGTPPLTKEDKTVVRQSP 350
351 RRIKPVRIIPSCKRTDATIAKQLLQRAKKGAQKKIEKEAAQLQGRKVKTQ 400
401 VKNIRQFIMPVVSAISSRIIKTPRRFIEDEDYDPPMKIARLESTPNSRFS 450
451 ATSCGSSEKSSAASQHSSQMSSDSSRSSSPSIDTTSDSQASEEIQALPEE 500
501 RSNTPEVHTPLPISQSPENESNDRRSRRYSMSERSFGSRATKKLPTLQSA 550
551 PQQQTSSSPPPPLLTPPPPLQPASGISDHTPWLMPPTIPLASPFLPASAA 600
601 PMQGKRKSILREPTFRWTSLKHSRSEPQYFSSAKYAKEGLIRKPIFDNFR 650
651 PPPLTPEDVGFASGFSASGTAASARLFSPLHSGTRFDIHKRSPILRAPRF 700
701 TPSEAHSRIFESVTLPSNRTSSGASSSGVSNRKRKRKVFSPIRSEPRSPS 750
751 HSMRTRSGRLSTSELSPLTPPSSVSSSLSIPVSPLAASALNPTFTFPSHS 800
801 LTQSGESTEKNQRARKQTSALAEPFSSNSPALFPWFTPGSQTEKGRKKDT 850
851 APEELSKDRDADKSVEKDKSRERDREREKENKRESRKEKRKKGSDIQSSS 900
901 ALYPVGRVSKEKVAGEDVGTSSSAKKATGRKKSSSLDSGADVAPVTLGDT 950
951 TAVKAKILIKKGRGNLEKNNLDLGPAAPSLEKERTPCLSAPSSSTVKHST 1000
1001 SSIGSMLAQADKLPMTDKRVASLLKKAKAQLCKIEKSKSLKQTDQPKAQG 1050
1051 QESDSSETSVRGPRIKHVCRRAAVALGRKRAVFPDDMPTLSALPWEEREK 1100
1101 ILSSMGNDDKSSVAGSEDAEPLAPPIKPIKPVTRNKAPQEPPVKKGRRSR 1150
1151 RCGQCPGCQVPEDCGICTNCLDKPKFGGRNIKKQCCKMRKCQNLQWMPSK 1200
1201 ASLQKQTKAVKKKEKKSKTTEKKESKESTAVKSPLEPAQKAAPPPREEPA 1250
1251 PKKSSSEPPPRKPVEEKSEEGGAPAPAPAPEPKQVSAPASRKSSKQVSQP 1300
1301 AAVVPPQPPSTAPQKKEAPKAVPSEPKKKQPPPPEPGPEQSKQKKVAPRP 1350
1351 SIPVKQKPKDKEKPPPVSKQENAGTLNILNPLSNGISSKQKIPADGVHRI 1400
1401 RVDFKEDCEAENVWEMGGLGILTSVPITPRVVCFLCASSGHVEFVYCQVC 1450
1451 CEPFHKFCLEENERPLEDQLENWCCRRCKFCHVCGRQHQATKQLLECNKC 1500
1501 RNSYHPECLGPNYPTKPTKKKKVWICTKCVRCKSCGSTTPGKGWDAQWSH 1550
1551 DFSLCHDCAKLFAKGNFCPLCDKCYDDDDYESKMMQCGKCDRWVHSKCES 1600
1601 LSGTEDEMYEILSNLPESVAYTCVNCTERHPAEWRLALEKELQASLKQVL 1650
1651 TALLNSRTTSHLLRYRQAAKPPDLNPETEESIPSRSSPEGPDPPVLTEVS 1700
1701 KQDEQQPLDLEGVKKRMDQGSYVSVLEFSDDIVKIIQAAINSDGGQPEIK 1750
1751 KANSMVKSFFIRQMERVFPWFSVKKSRFWEPNKVSNNSGMLPNAVLPPSL 1800
1801 DHNYAQWQEREESSHTEQPPLMKKIIPAPKPKGPGEPDSPTPLHPPTPPI 1850
1851 LSTDRSREDSPELNPPPGIDDNRQCALCLMYGDDSANDAGRLLYIGQNEW 1900
1901 THVNCALWSAEVFEDDDGSLKNVHMAVIRGKQLRCEFCQKPGATVGCCLT 1950
1951 SCTSNYHFMCSRAKNCVFLDDKKVYCQRHRDLIKGEVVPENGFEVFRRVF 2000
2001 VDFEGISLRRKFLNGLEPENIHMMIGSMTIDCLGILNDLSDCEDKLFPIG 2050
2051 YQCSRVYWSTTDARKRCVYTCKIMECRPPVVEPDINSTVEHDDNRTIAHS 2100
2101 PSSFIDASCKDSQSTAAILSPPSPDRPHSQTSGSCYYHVISKVPRIRTPS 2150
2151 YSPTQRSPGCRPLPSAGSPTPTTHEIVTVGDPLLSSGLRSIGSRRHSTSS 2200
2201 LSPLRSKLRIMSPVRTGSAYSRSSVSSVPSLGTATDPEASAKASDRGGLL 2250
2251 SSSANLGHSAPPSSSSQRTVGGSKTSHLDGSSPSEVKRCSASDLVPKGSL 2300
2301 VKGEKNRTSSSKSTDGSAHSTAYPGIPKLTPQVHNATPGELNISKIGSFA 2350
2351 EPSTVPFSSKDTVSYPQLHLRGQRSDRDQHMDPSQSVKPSPNEDGEIKTL 2400
2401 KLPGMGHRPSILHEHIGSSSRDRRQKGKKSSKETCKEKHSSKSYLEPGQV 2450
2451 TTGEEGNLKPEFADEVLTPGFLGQRPCNNVSSEKIGDKVLPLSGVPKGQS 2500
2501 TQVEGSSKELQAPRKCSVKVTPLKMEGENQSKNTQKESGPGSPAHIESVC 2550
2551 PAEPVSASRSPGAGPGVQPSPNNTLSQDPQSNNYQNLPEQDRNLMIPDGP 2600
2601 KPQEDGSFKRRYPRRSARARSNMFFGLTPLYGVRSYGEEDIPFYSNSTGK 2650
2651 KRGKRSAEGQVDGADDLSTSDEDDLYYYNFTRTVISSGGEERLASHNLFR 2700
2701 EEEQCDLPKISQLDGVDDGTESDTSVTATSRKSSQIPKRNGKENGTENLK 2750
2751 IDRPEDAGEKEHVIKSAVGHKNEPKLDNCHSVSRVKAQGQDSLEAQLSSL 2800
2801 ESSRRVHTSTPSDKNLLDTYNAELLKSDSDNNNSDDCGNILPSDIMDFVL 2850
2851 KNTPSMQALGESPESSSSELLTLGEGLGLDSNREKDIGLFEVFSQQLPAT 2900
2901 EPVDSSVSSSISAEEQFELPLELPSDLSVLTTRSPTVPSQNPSRLAVISD 2950
2951 SGEKRVTITEKSVASSEGDPALLSPGVDPAPEGHMTPDHFIQGHMDADHI 3000
3001 SSPPCGSVEQGHGNSQDLTRNSGTPGLQVPVSPTVPVQNQKYVPSSTDSP 3050
3051 GPSQISNAAVQTTPPHLKPATEKLIVVNQNMQPLYVLQTLPNGVTQKIQL 3100
3101 TSPVSSTPSVMETNTSVLGPMGSGLTLTTGLNPSLPPSPSLFPPASKGLL 3150
3151 SVPHHQHLHSFPAAAQSSFPPNISSPPSGLLIGVQPPPDPQLLGSEANQR 3200
3201 TDLTTTVATPSSGLKKRPISRLHTRKNKKLAPSSAPSNIAPSDVVSNMTL 3250
3251 INFTPSQLSNHPSLLDLGSLNPSSHRTVPNIIKRSKSGIMYFEQAPLLPP 3300
3301 QSVGGTAATAAGSSTISQDTSHLTSGPVSALASGSSVLNVVSMQTTAAPT 3350
3351 SSTSVPGHVTLANQRLLGTPDIGSISHLLIKASHQSLGIQDQPVALPPSS 3400
3401 GMFPQLGTSQTPSAAAMTAASSICVLPSSQTAGMTAASPPGEAEEHYKLQ 3450
3451 RGNQLLAGKTGTLTSQRDRDPDSAPGTQPSNFTQTAEAPNGVRLEQNKTL 3500
3501 PSAKPASSASPGSSPSSGQQSGSSSVPGPTKPKPKAKRIQLPLDKGSGKK 3550
3551 HKVSHLRTSSEAHIPHRDTDPAPQPSVTRTPRANREQQDAAGVEQPSQKE 3600
3601 CGQPAGPVAALPEVQATQNPANEQENAEPKAMEEEESGFSSPLMLWLQQE 3650
3651 QKRKESITERKPKKGLVFEISSDDGFQICAESIEDAWKSLTDKVQEARSN 3700
3701 ARLKQLSFAGVNGLRMLGILHDAVVFLIEQLAGAKHCRNYKFRFHKPEEA 3750
3751 NEPPLNPHGSARAEVHLRQSAFDMFNFLASKHRQPPEYNPNDEEEEEVQL 3800
3801 KSARRATSMDLPMPMRFRHLKKTSKEAVGVYRSPIHGRGLFCKRNIDAGE 3850
3851 MVIEYAGNVIRSIQTDKREKYYDSKGIGCYMFRIDDSEVVDATMHGNAAR 3900
3901 FINHSCEPNCYSRVINIDGQKHIVIFAMRKIYRGEELTYDYKFPIEDASN 3950
3951 KLPCNCGAKKCRKFLN 3966
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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