 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8CGM2 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MNSTPGDTRDAPAPSHPAPSHRQCLLPSVAHTPSVTEVTPAKKITFLKRG 50
51 DPQFAGVRLAVHQRTFKTFSSLMDELSQRMPLSFGVRSVTTPRGLHGLSA 100
101 LEQLQDGGCYLCSDRKPPKTSREPGRLQRKSPSAGQAQVFQGGHEAPETS 150
151 YSWKGPVAPRRLTLVKNGDPRRQQTVVLSHKNTRSLAAFLGKASELLRFP 200
201 VKQVYTTRGKKVDSLQTLLDGPSVLVCAGNEAFRCLEMENDRGNRTRKLS 250
251 SVTARSERGCWGPNAKQSVIHSRGRSGGKLRQVSLTSERSGLSDHPASGH 300
301 RAWAGPALDRCPQDMPVPPGSLVAADDVEKKVCMNEDGSLSVEMKVRFQL 350
351 LGEDTLRWSQRVGQASVFTAASGKGQDPREADRFCCRQEGYPWGILKPGA 400
401 QGLGSYDGGCQEAFDVGQKSQPSYDIWRNPLATPEGTGPTPRRRWGLAKL 450
451 SGCKSHWRQEANHRKGHDKDNLSRVSTPRHPRSVQPGSCCPWTPDGDTGS 500
501 DTLHPVSSASSHNETDLESGEGLCLEDTGPHGSRPETQSTERALSDTSVS 550
551 AKSREESSEGGGQLHRSSSQARVMASREQVTKGDNPCISTQSHLPLNHMG 600
601 LQTEKYRQGTRGWEVSGEPELRLALVPGHSGSQDTQRDALPAPACAPAQW 650
651 RQRKQKRPASVECLPSVSVPYQVAQKGHARQDHYYRDTQSSLDTALQMPM 700
701 PQEREQACPGSPAPQSPSNSPSAGNQASEDLRSPFSSSLDLQEPQATSKA 750
751 TTIAVSGSDCVCHSTRSVEPAGDTKCQAHSSTPTPAHRGELGCLWDKAGT 800
801 TPEPFSFSVLLDRCPEADDPRTYHDCCCLQAVPSSPLAAPSGQTQTSISE 850
851 ACLGGSSFCPTPPKEQTCFGRESASNGSTSSGHSRADGFAGPRRTLLVKS 900
901 PGVRGSLEEREADGGVTPSALPYASPDAVVREWLGNIPEKPVLMTYEMAD 950
951 ENTEVPSDGPEGPKEDSLKVLGEPSQAKQQPPEGATNEHPEPAGVLSGPG 1000
1001 SVCCRLGGDLHPDATSGERLKAPAEAGIGEGARVDHGVSLCALPTKVAAS 1050
1051 TQIMKALLGSKPGRPSSLPEVSSTVAQRLSSSAGAFIACLARLHFFDESL 1100
1101 GPLDGKVRLEESPKYQEMLRLFQTLWPGSELWQGQLDFSLRKLTSHQALL 1150
1151 GTEDFTPTSSSGVDVSSGSGGSGESSVPCVMDNTLAPEKRDLPLKIPSQR 1200
1201 PDSRNQGYPELVGHSTVSSVSQVRACATGGEETGKGGRKQTWGNAPEQSV 1250
1251 HSTMLEGDALSEETEGRVRERLQENSVHGKGLPEEGVRVCSQEMLAAGSQ 1300
1301 DGAGSPEDTRVPTDEAGADAASGGLWPLDGREEPTESPQHFSESNSRVRE 1350
1351 HQSAHKLELGLEEVSRLDARGCKQACIKASSGTMAHKGSLDPDPIWVSKL 1400
1401 LKKIEKAFMAHLADATAELRARWDLHDNHLLDQMVTELEQDVGRRLQAST 1450
1451 VMEVRKIQSRAGRMVPEPPREALRGQASLQTEQRRRRLQGLRNFSAVPGQ 1500
1501 GPLSLTLEDGPTLKTALGTKSGAEPAEDEFCPCEICLKKKRTPRFPKDAA 1550
1551 TVSGAPVRKAFDLQQILQSKKGGSSNREAMEVAPQRTGRMLSQEDLGTVQ 1600
1601 GADEKQGLGVAEGEEGEGKQRLRAEEDPEILKTEGSGCCAPEEDEATEED 1650
1651 GEICIGTAQESQQLEGTEMGKEGTLPQSFRDGGTLEAPARQGTHSVEIQE 1700
1701 ASRERQQEVEGRHQDVKEDSPWVSSGESQGRVGSENTSLDQEGRLLNHHQ 1750
1751 RPGPQSHHTACSSRALSLDNSSQVSQKGSDGDLTSGDLKCTKAKNSRVLH 1800
1801 AEKKVPVMYPERSSSEQEVPSSPRLPKQGKGEDEGSAGSLACTQVGGKVD 1850
1851 GFGQDDLDF 1859
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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