 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8BKX6 from www.uniprot.org...
The NucPred score for your sequence is 0.78 (see score help below)
1 MSRRAPGSRLSSGGGGTKYPRSWNDWQPRTDSASADPDTLKYSSSRDRGV 50
51 SSSYGLQPSNSAVVSRQRHDDTRGHADIQNDEKGGYSVNGGSGENTYGRK 100
101 SLGQELRINNVTSPEFTSVQHGSRALATKDMRKSQERSMSYSDESRLSNL 150
151 LRRITREDDRDRRLATVKQLKEFIQQPENKLVLVKQLDNILAAVHDVLNE 200
201 SSKLLQELRQEGACCLGLLCASLSYEAEKIFKWIFSKFSSSAKDEVKLLY 250
251 LCATYRALETVGEKKAFSSVMQLVMTSLQSILENVDTPELLCKCVKCILL 300
301 VARCYPHIFSTNFRDTVDILVGWHIDHTQKPSLTQQVSGWLQSLEPFWVA 350
351 DLAFSTTLLGQFLEDMEAYAEDLSHVASGESVDEDVPPPSVSLPKLAALL 400
401 RVFSTVVRSIGERFSPIRGPPITEAYVTDVLYRVMRCVTAANQVFFSEAV 450
451 LTAANECVGVLLGSLDPSMTIHCDMVITYGLDQLENCQTCGTDYIISVLN 500
501 LLTLIVEQINTKLPSSFVEKLFIPSSKLLFLRYHKEKEVVAVAHAVYQAV 550
551 LSLKNIPVLETAYKLILGEMTCALNNLLHSLQLPDACSEIKHEAFQNHVF 600
601 NIDNANFVVIFDLSALTTIGNAKNSLIGMWALSPTVFALLSKNLMIVHSD 650
651 LAVHFPAIQYAVLYTLYSHCTRHDHFISSSLSSSSPSLFDGAVISTVTTA 700
701 TKKHFSIILNLLGILLKKDNLNQDTRKLLMTWALEVAVLMKKSETYAPLF 750
751 SLPSFHKFSKGLLANTLVEDVNICLQACSSLHALSSSLPDDLLQRCVDVC 800
801 RVQLVHSGTRIRQAFGKLLKSIPLDVVLSNNNHTEIQEISLALRSHMSKA 850
851 PSNTFHPQDFSDVISFILYGNSHRTGKDNWLERLFYSCQRLDKRDQSTIP 900
901 RNLLKTDAVLWQWAIWEAAQFTVLSKLRTPLGRAQDTFQTIEGIIRSLAA 950
951 HTLNPDQDVSQWTTADNDEGHGSNQLRLVLLLQYLENLEKLMYNAYEGCA 1000
1001 NALTSPPKVIRTFFYTNRQTCQDWLTRIRLSIMRVGLLAGQPAVTVRHGF 1050
1051 DLLTEMKTNSLTQGSELEVTIMMVVEALCELHCPEAIQGIAVWSSSAVGK 1100
1101 NLLWINSVAQQAEGRFEKASVEYQEHLCAMTGVDCCISSFDKSVLTLANA 1150
1151 GRNSASPKHSLNGESRKTVLSKSIDSSPEVISYLGNKACECYISIADWAA 1200
1201 VQEWQNAVHDLKKNSSSTSLNLKADFNYIKSLSSFESGEFVECTEQLELL 1250
1251 PGENINLLAGGSKEKIDMKKLLPNMLSPDPRELQKSIEVQLLRSSVFLAT 1300
1301 ALNHMEQDQKWQSLTENVVKYLKQTSRIAIGPLRLSTLTVSQSLPVLSTL 1350
1351 QLYCSSALENTVSNRLSTEDCLIPLFSDALRSCKQHDVRPWMQALRYTMY 1400
1401 QNQLLEKIKEQTVPIRSHLMELGLTAAKFARKRGNVSLATRLLAQCSEVQ 1450
1451 LGKTTTAQDLVQHFKKLSTQGQVDEKWGPELDIEKTKLLYTAGQSTHAME 1500
1501 MLSSCAISFCKSAKAEYAVAKSILTLAKWVQAEWKEISGQLRQVYRAQQQ 1550
1551 QNLSGLSTLSRNILALIELPSANTVGEEHPRIESESTVHIGVGEPDFILG 1600
1601 QLYHLSSVQAPEVAKSWAALASWAYRWGRKVVDNASQGEGVRLLPREKSE 1650
1651 VQNLLPDTITEEEKERIYGILGQAVCRPAGIQDEDITLQITESEDNEDDD 1700
1701 MVDVIWRQLISSCPWLSELDENATEGVIKVWRKVVDRIFSLYKLSCSAYF 1750
1751 TFLKLNAGQVLLDEDDPRLHLSHRAEQSTDDVIVMATLRLLRLLVKHAGE 1800
1801 LRQYLEHGLETTPTAPWRGIIPQLFSRLNHPEVYVRQSICNLLCRVAQDS 1850
1851 PHLILYPAIVGTISLSSESQASGNKYSSAIPTLLGNIQGEELLVSECEGG 1900
1901 SPPASQDSNKDEPKSGLNEDQAMMQDCYSKIVDKLSSANPTMVLQVQMLV 1950
1951 AELRRVTVLWDELWLGVLLQQHMYVLRRIQQLEDEVKRVQNNNTLRKEEK 2000
2001 IAIMREKHTALMKPIVFALEHVRSITAAPAETPHEKWFQDNYGDAIDNAL 2050
2051 EKLKTPSNPAKPGSSWIPFKEIMLSLQQRAQKRASYILRLDEISPWLAAM 2100
2101 TNTEIALPGEVSARDTVTIHSVGGTITILPTKTKPKKLLFLGSDGKSYPY 2150
2151 LFKGLEDLHLDERIMQFLSIVNTMFATINRQETPRFHARHYSVTPLGTRS 2200
2201 GLIQWVDGATPLFGLYKRWQQREAALQAQKAQDSYQTPQNPSIVPRPSEL 2250
2251 YYSKIGPALKTVGLSLDVSRRDWPLHVMKAVLEELMEATPPNLLAKELWS 2300
2301 SCTTPDEWWRVTQSYARSTAVMSMVGYIIGLGDRHLDNVLIDMTTGEVVH 2350
2351 IDYNVCFEKGKSLRVPEKVPFRMTQNIETALGVTGVEGVFRLSCEQVLHI 2400
2401 MRRGRETLLTLLEAFVYDPLVDWTAGGEAGFAGAVYGGGGQQAESKQSKR 2450
2451 EMEREITRSLFSSRVAEIKVNWFKNRDEMLVVLPKLDSSLDEYLSLQEQL 2500
2501 TDVEKLQGKLLEEIEFLEGAEGVDHPSHTLQHRYSEHTQLQTQQRAVQEA 2550
2551 IQVKLNEFEQWITHYQAAFNNLEATQLASLLQEISTQMDLGPPSYVPATA 2600
2601 FLQNAGQAHLISQCEQLEGEVGALLQQRRSVLRGCLEQLHHYATVALQYP 2650
2651 KAIFQKHRIEQWKAWMEELICNTTVERCQELYRKYEMQYAPQPPPTVCQF 2700
2701 ITATEMTLQRYAADINSRLIRQVERLKQEAVTVPVCEDQLKEIERCIKVF 2750
2751 LHENGEEGSLSLASVIISALCTLTRRNLMMEGAASSAGEQLVDLTSRDGA 2800
2801 WFLEELCSMSGNVTCLVQLLKQCHLVPQDLDIPNPVEASEAVHLANGVYT 2850
2851 SLQELNSNFRQIIFPEALRCLMKGECTLESMLHELDSLIEQTTDGVPLQT 2900
2901 LVESLQAYLRNTAMGLEEETHAHYIDVARMLHAQYGELIQPRNGSVDETP 2950
2951 KMSAGQMLLVAFDGMFAQVETAFGLLVEKLNKMEIPVAWRKIDIIREARS 3000
3001 TQVNFFDDDNHRQVLEEIFFLKRLQTIKEFFRLCGTFSKTLSGSSSLEDQ 3050
3051 NTVNGPVQIVNVKTLFRNSCFSEDQMAKPIKAFTADFVRQLLIGLPNQAL 3100
3101 GLTLCSFISALGVDIIAQVEAKDFGAESKVSVDDLCKKAVEHNIQVGKFS 3150
3151 QLVMNRATVLASSYDTAWKKHDLVRRLETSISSCKTSLQRVQLHIAMFQW 3200
3201 QHEDLLISRPQAMSVTPPRSAILTSMKKKLHALSQIETSIGTVQEKLAAL 3250
3251 EASIEQRLKWAGGANPALAPVLQDFEATIAERRNLVLKESQRANQVTFLC 3300
3301 SNIIHFESLRTRTAEALSLDAALFELIKRCQQMCSFASQFNSSVSELELR 3350
3351 LLQRVDTTLEHPIGSSEWLLSAHKQLTQDMSTQRAVQTEKEQQIETVCET 3400
3401 IQSLVDSVKTVLTGHNRQLGDVKHLLKAMAKDEEAALADAEDIPYESSVR 3450
3451 QFLAEYKSWQDNIQTVLFTLVQAMGQVRSQEHVEMLQEITPTLKELKTQS 3500
3501 QSIYNNLVSFASPLVTDAANECSSPTSSATYQPSFAAAVRSNTGQKTQPD 3550
3551 VMSQNAKKLIQKNLATSADTPPSTIPGTGKSIACSPKKAVRDPKTGKAVQ 3600
3601 ERNSYAVSVWKRVKAKLEGRDVDPNRRMSVAEQVDYVIKEATNLDNLAQL 3650
3651 YEGWTAWV 3658
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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