 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q96L91 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MHHGTGPQNVQHQLQRSRACPGSEGEEQPAHPNPPPSPAAPFAPSASPSA 50
51 PQSPSYQIQQLMNRSPATGQNVNITLQSVGPVVGGNQQITLAPLPLPSPT 100
101 SPGFQFSAQPRRFEHGSPSYIQVTSPLSQQVQTQSPTQPSPGPGQALQNV 150
151 RAGAPGPGLGLCSSSPTGGFVDASVLVRQISLSPSSGGHFVFQDGSGLTQ 200
201 IAQGAQVQLQHPGTPITVRERRPSQPHTQSGGTIHHLGPQSPAAAGGAGL 250
251 QPLASPSHITTANLPPQISSIIQGQLVQQQQVLQGPPLPRPLGFERTPGV 300
301 LLPGAGGAAGFGMTSPPPPTSPSRTAVPPGLSSLPLTSVGNTGMKKVPKK 350
351 LEEIPPASPEMAQMRKQCLDYHYQEMQALKEVFKEYLIELFFLQHFQGNM 400
401 MDFLAFKKKHYAPLQAYLRQNDLDIEEEEEEEEEEEEKSEVINDEVKVVT 450
451 GKDGQTGTPVAIATQLPPKVSAAFSSQQQPFQQALAGSLVAGAGSTVETD 500
501 LFKRQQAMPSTGMAEQSKRPRLEVGHQGVVFQHPGADAGVPLQQLMPTAQ 550
551 GGMPPTPQAAQLAGQRQSQQQYDPSTGPPVQNAASLHTPLPQLPGRLPPA 600
601 GVPTAALSSALQFAQQPQVVEAQTQLQIPVKTQQPNVPIPAPPSSQLPIP 650
651 PSQPAQLALHVPTPGKVQVQASQLSSLPQMVASTRLPVDPAPPCPRPLPT 700
701 SSTSSLAPVSGSGPGPSPARSSPVNRPSSATNKALSPVTSRTPGVVASAP 750
751 TKPQSPAQNATSSQDSSQDTLTEQITLENQVHQRIAELRKAGLWSQRRLP 800
801 KLQEAPRPKSHWDYLLEEMQWMATDFAQERRWKVAAAKKLVRTVVRHHEE 850
851 KQLREERGKKEEQSRLRRIAASTAREIECFWSNIEQVVEIKLRVELEEKR 900
901 KKALNLQKVSRRGKELRPKGFDALQESSLDSGMSGRKRKASISLTDDEVD 950
951 DEEETIEEEEANEGVVDHQTELSNLAKEAELPLLDLMKLYEGAFLPSSQW 1000
1001 PRPKPDGEDTSGEEDADDCPGDRESRKDLVLIDSLFIMDQFKAAERMNIG 1050
1051 KPNAKDIADVTAVAEAILPKGSARVTTSVKFNAPSLLYGALRDYQKIGLD 1100
1101 WLAKLYRKNLNGILADEAGLGKTVQIIAFFAHLACNEGNWGPHLVVVRSC 1150
1151 NILKWELELKRWCPGLKILSYIGSHRELKAKRQEWAEPNSFHVCITSYTQ 1200
1201 FFRGLTAFTRVRWKCLVIDEMQRVKGMTERHWEAVFTLQSQQRLLLIDSP 1250
1251 LHNTFLELWTMVHFLVPGISRPYLSSPLRAPSEESQDYYHKVVIRLHRVT 1300
1301 QPFILRRTKRDVEKQLTKKYEHVLKCRLSNRQKALYEDVILQPGTQEALK 1350
1351 SGHFVNVLSILVRLQRICNHPGLVEPRHPGSSYVAGPLEYPSASLILKAL 1400
1401 ERDFWKEADLSMFDLIGLENKITRHEAELLSKKKIPRKLMEEISTSAAPA 1450
1451 ARPAAAKLKASRLFQPVQYGQKPEGRTVAFPSTHPPRTAAPTTASAAPQG 1500
1501 PLRGRPPIATFSANPEAKAAAAPFQTSQASASAPRHQPASASSTAASPAH 1550
1551 PAKLRAQTTAQASTPGQPPPQPQAPSHAAGQSALPQRLVLPSQAQARLPS 1600
1601 GEVVKIAQLASITGPQSRVAQPETPVTLQFQGSKFTLSHSQLRQLTAGQP 1650
1651 LQLQGSVLQIVSAPGQPYLRAPGPVVMQTVSQAGAVHGALGSKPPAGGPS 1700
1701 PAPLTPQVGVPGRVAVNALAVGEPGTASKPASPIGGPTQEEKTRLLKERL 1750
1751 DQIYLVNERRCSQAPVYGRDLLRICALPSHGRVQWRGSLDGRRGKEAGPA 1800
1801 HSYTSSSESPSELMLTLCRCGESLQDVIDRVAFVIPPVVAAPPSLRVPRP 1850
1851 PPLYSHRMRILRQGLREHAAPYFQQLRQTTAPRLLQFPELRLVQFDSGKL 1900
1901 EALAILLQKLKSEGRRVLILSQMILMLDILEMFLNFHYLTYVRIDENASS 1950
1951 EQRQELMRSFNRDRRIFCAILSTHSRTTGINLVEADTVVFYDNDLNPVMD 2000
2001 AKAQEWCDRIGRCKDIHIYRLVSGNSIEEKLLKNGTKDLIREVAAQGNDY 2050
2051 SMAFLTQRTIQELFEVYSPMDDAGFPVKAEEFVVLSQEPSVTETIAPKIA 2100
2101 RPFIEALKSIEYLEEDAQKSAQEGVLGPHTDALSSDSENMPCDEEPSQLE 2150
2151 ELADFMEQLTPIEKYALNYLELFHTSIEQEKERNSEDAVMTAVRAWEFWN 2200
2201 LKTLQEREARLRLEQEEAELLTYTREDAYSMEYVYEDVDGQTEVMPLWTP 2250
2251 PTPPQDDSDIYLDSVMCLMYEATPIPEAKLPPVYVRKERKRHKTDPSAAG 2300
2301 RKKKQRHGEAVVPPRSLFDRATPGLLKIRREGKEQKKNILLKQQVPFAKP 2350
2351 LPTFAKPTAEPGQDNPEWLISEDWALLQAVKQLLELPLNLTIVSPAHTPN 2400
2401 WDLVSDVVNSCSRIYRSSKQCRNRYENVIIPREEGKSKNNRPLRTSQIYA 2450
2451 QDENATHTQLYTSHFDLMKMTAGKRSPPIKPLLGMNPFQKNPKHASVLAE 2500
2501 SGINYDKPLPPIQVASLRAERIAKEKKALADQQKAQQPAVAQPPPPQPQP 2550
2551 PPPPQQPPPPLPQPQAAGSQPPAGPPAVQPQPQPQPQTQPQPVQAPAKAQ 2600
2601 PAITTGGSAAVLAGTIKTSVTGTSMPTGAVSGNVIVNTIAGVPAATFQSI 2650
2651 NKRLASPVAPGALTTPGGSAPAQVVHTQPPPRAVGSPATATPDLVSMATT 2700
2701 QGVRAVTSVTASAVVTTNLTPVQTPARSLVPQVSQATGVQLPGKTITPAH 2750
2751 FQLLRQQQQQQQQQQQQQQQQQQQQQQQQQQQQQTTTTSQVQVPQIQGQA 2800
2801 QSPAQIKAVGKLTPEHLIKMQKQKLQMPPQPPPPQAQSAPPQPTAQVQVQ 2850
2851 TSQPPQQQSPQLTTVTAPRPGALLTGTTVANLQVARLTRVPTSQLQAQGQ 2900
2901 MQTQAPQPAQVALAKPPVVSVPAAVVSSPGVTTLPMNVAGISVAIGQPQK 2950
2951 AAGQTVVAQPVHMQQLLKLKQQAVQQQKAIQPQAAQGPAAVQQKITAQQI 3000
3001 TTPGAQQKVAYAAQPALKTQFLTTPISQAQKLAGAQQVQTQIQVAKLPQV 3050
3051 VQQQTPVASIQQVASASQQASPQTVALTQATAAGQQVQMIPAVTATAQVV 3100
3101 QQKLIQQQVVTTASAPLQTPGAPNPAQVPASSDSPSQQPKLQMRVPAVRL 3150
3151 KTPTKPPCQ 3159
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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