 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q16787 from www.uniprot.org...
The NucPred score for your sequence is 0.74 (see score help below)
1 MAAAARPRGRALGPVLPPTPLLLLVLRVLPACGATARDPGAAAGLSLHPT 50
51 YFNLAEAARIWATATCGERGPGEGRPQPELYCKLVGGPTAPGSGHTIQGQ 100
101 FCDYCNSEDPRKAHPVTNAIDGSERWWQSPPLSSGTQYNRVNLTLDLGQL 150
151 FHVAYILIKFANSPRPDLWVLERSVDFGSTYSPWQYFAHSKVDCLKEFGR 200
201 EANMAVTRDDDVLCVTEYSRIVPLENGEVVVSLINGRPGAKNFTFSHTLR 250
251 EFTKATNIRLRFLRTNTLLGHLISKAQRDPTVTRRYYYSIKDISIGGQCV 300
301 CNGHAEVCNINNPEKLFRCECQHHTCGETCDRCCTGYNQRRWRPAAWEQS 350
351 HECEACNCHGHASNCYYDPDVERQQASLNTQGIYAGGGVCINCQHNTAGV 400
401 NCEQCAKGYYRPYGVPVDAPDGCIPCSCDPEHADGCEQGSGRCHCKPNFH 450
451 GDNCEKCAIGYYNFPFCLRIPIFPVSTPSSEDPVAGDIKGCDCNLEGVLP 500
501 EICDAHGRCLCRPGVEGPRCDTCRSGFYSFPICQACWCSALGSYQMPCSS 550
551 VTGQCECRPGVTGQRCDRCLSGAYDFPHCQGSSSACDPAGTINSNLGYCQ 600
601 CKLHVEGPTCSRCKLLYWNLDKENPSGCSECKCHKAGTVSGTGECRQGDG 650
651 DCHCKSHVGGDSCDTCEDGYFALEKSNYFGCQGCQCDIGGALSSMCSGPS 700
701 GVCQCREHVVGKVCQRPENNYYFPDLHHMKYEIEDGSTPNGRDLRFGFDP 750
751 LAFPEFSWRGYAQMTSVQNDVRITLNVGKSSGSLFRVILRYVNPGTEAVS 800
801 GHITIYPSWGAAQSKEIIFLPSKEPAFVTVPGNGFADPFSITPGIWVACI 850
851 KAEGVLLDYLVLLPRDYYEASVLQLPVTEPCAYAGPPQENCLLYQHLPVT 900
901 RFPCTLACEARHFLLDGEPRPVAVRQPTPAHPVMVDLSGREVELHLRLRI 950
951 PQVGHYVVVVEYSTEAAQLFVVDVNVKSSGSVLAGQVNIYSCNYSVLCRS 1000
1001 AVIDHMSRIAMYELLADADIQLKGHMARFLLHQVCIIPIEEFSAEYVRPQ 1050
1051 VHCIASYGRFVNQSATCVSLAHETPPTALILDVLSGRPFPHLPQQSSPSV 1100
1101 DVLPGVTLKAPQNQVTLRGRVPHLGRYVFVIHFYQAAHPTFPAQVSVDGG 1150
1151 WPRAGSFHASFCPHVLGCRDQVIAEGQIEFDISEPEVAATVKVPEGKSLV 1200
1201 LVRVLVVPAENYDYQILHKKSMDKSLEFITNCGKNSFYLDPQTASRFCKN 1250
1251 SARSLVAFYHKGALPCECHPTGATGPHCSPEGGQCPCQPNVIGRQCTRCA 1300
1301 TGHYGFPRCKPCSCGRRLCEEMTGQCRCPPRTVRPQCEVCETHSFSFHPM 1350
1351 AGCEGCNCSRRGTIEAAMPECDRDSGQCRCKPRITGRQCDRCASGFYRFP 1400
1401 ECVPCNCNRDGTEPGVCDPGTGACLCKENVEGTECNVCREGSFHLDPANL 1450
1451 KGCTSCFCFGVNNQCHSSHKRRTKFVDMLGWHLETADRVDIPVSFNPGSN 1500
1501 SMVADLQELPATIHSASWVAPTSYLGDKVSSYGGYLTYQAKSFGLPGDMV 1550
1551 LLEKKPDVQLTGQHMSIIYEETNTPRPDRLHHGRVHVVEGNFRHASSRAP 1600
1601 VSREELMTVLSRLADVRIQGLYFTETQRLTLSEVGLEEASDTGSGRIALA 1650
1651 VEICACPPAYAGDSCQGCSPGYYRDHKGLYTGRCVPCNCNGHSNQCQDGS 1700
1701 GICVNCQHNTAGEHCERCQEGYYGNAVHGSCRACPCPHTNSFATGCVVNG 1750
1751 GDVRCSCKAGYTGTQCERCAPGYFGNPQKFGGSCQPCSCNSNGQLGSCHP 1800
1801 LTGDCINQEPKDSSPAEECDDCDSCVMTLLNDLATMGEQLRLVKSQLQGL 1850
1851 SASAGLLEQMRHMETQAKDLRNQLLNYRSAISNHGSKIEGLERELTDLNQ 1900
1901 EFETLQEKAQVNSRKAQTLNNNVNRATQSAKELDVKIKNVIRNVHILLKQ 1950
1951 ISGTDGEGNNVPSGDFSREWAEAQRMMRELRNRNFGKHLREAEADKRESQ 2000
2001 LLLNRIRTWQKTHQGENNGLANSIRDSLNEYEAKLSDLRARLQEAAAQAK 2050
2051 QANGLNQENERALGAIQRQVKEINSLQSDFTKYLTTADSSLLQTNIALQL 2100
2101 MEKSQKEYEKLAASLNEARQELSDKVRELSRSAGKTSLVEEAEKHARSLQ 2150
2151 ELAKQLEEIKRNASGDELVRCAVDAATAYENILNAIKAAEDAANRAASAS 2200
2201 ESALQTVIKEDLPRKAKTLSSNSDKLLNEAKMTQKKLKQEVSPALNNLQQ 2250
2251 TLNIVTVQKEVIDTNLTTLRDGLHGIQRGDIDAMISSAKSMVRKANDITD 2300
2301 EVLDGLNPIQTDVERIKDTYGRTQNEDFKKALTDADNSVNKLTNKLPDLW 2350
2351 RKIESINQQLLPLGNISDNMDRIRELIQQARDAASKVAVPMRFNGKSGVE 2400
2401 VRLPNDLEDLKGYTSLSLFLQRPNSRENGGTENMFVMYLGNKDASRDYIG 2450
2451 MAVVDGQLTCVYNLGDREAELQVDQILTKSETKEAVMDRVKFQRIYQFAR 2500
2501 LNYTKGATSSKPETPGVYDMDGRNSNTLLNLDPENVVFYVGGYPPDFKLP 2550
2551 SRLSFPPYKGCIELDDLNENVLSLYNFKKTFNLNTTEVEPCRRRKEESDK 2600
2601 NYFEGTGYARVPTQPHAPIPTFGQTIQTTVDRGLLFFAENGDRFISLNIE 2650
2651 DGKLMVRYKLNSELPKERGVGDAINNGRDHSIQIKIGKLQKRMWINVDVQ 2700
2701 NTIIDGEVFDFSTYYLGGIPIAIRERFNISTPAFRGCMKNLKKTSGVVRL 2750
2751 NDTVGVTKKCSEDWKLVRSASFSRGGQLSFTDLGLPPTDHLQASFGFQTF 2800
2801 QPSGILLDHQTWTRNLQVTLEDGYIELSTSDSGGPIFKSPQTYMDGLLHY 2850
2851 VSVISDNSGLRLLIDDQLLRNSKRLKHISSSRQSLRLGGSNFEGCISNVF 2900
2901 VQRLSLSPEVLDLTSNSLKRDVSLGGCSLNKPPFLMLLKGSTRFNKTKTF 2950
2951 RINQLLQDTPVASPRSVKVWQDACSPLPKTQANHGALQFGDIPTSHLLFK 3000
3001 LPQELLKPRSQFAVDMQTTSSRGLVFHTGTKNSFMALYLSKGRLVFALGT 3050
3051 DGKKLRIKSKEKCNDGKWHTVVFGHDGEKGRLVVDGLRAREGSLPGNSTI 3100
3101 SIRAPVYLGSPPSGKPKSLPTNSFVGCLKNFQLDSKPLYTPSSSFGVSSC 3150
3151 LGGPLEKGIYFSEEGGHVVLAHSVLLGPEFKLVFSIRPRSLTGILIHIGS 3200
3201 QPGKHLCVYLEAGKVTASMDSGAGGTSTSVTPKQSLCDGQWHSVAVTIKQ 3250
3251 HILHLELDTDSSYTAGQIPFPPASTQEPLHLGGAPANLTTLRIPVWKSFF 3300
3301 GCLRNIHVNHIPVPVTEALEVQGPVSLNGCPDQ 3333
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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