 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q7S133 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MTTMMTDSGTASDSGAGLVDSTRNDTTTTTTTTTTPGDNDADDDNTNGTT 50
51 TGHHDNNETDNNSKSYSSTHHVPAIDNTSTTNANDNEDAAGFLDRDQSPL 100
101 SSISSPLSEPDNFEFDTEPIPSILSPKSTTSDNHARDGETPELDEDGQPP 150
151 AKRRRVRETTPHNHSRKPKPESPPWKKFEAEGPTTIITEDGRRKSGRVNP 200
201 VLLGMKPSDKKVTRKAIQTSPVSNKSSASTSRKPAPASSSNSKHAPAKMP 250
251 PPPPPPKAPARKSATTHETRPSASRSRRRSPSPRRPATPPKPAAVGTRRS 300
301 TRQALAHSRASYDDGQLSPGASRATPRIKLKVRPPLTVIPLVHPNQANVR 350
351 PKLGPTFEEYFARAHEIPVEEGGQHIPDEEPKYTDEMALQDAKVILRVEK 400
401 EVEPGGLLAPDRCSAFEPEAEEEPPRQWAHLDHLTKAMTNFRKLMYREQQ 450
451 LHMQAAKRIAIACEAEWRRRNPQPKTAEEIELEEMEASKAKWRQVIRAFA 500
501 GTWENVRVEVNRRRLVEWEAEEQRRVKAALNQAVNLSEQKLQQRQAQVDG 550
551 DEITDEDEDEDDEDLASGMPSVMGDEKESDEHSDQGSDEMSDENDEDEDE 600
601 DNMSSSEDDDKKSTASDEGLTQEELRAKYANLPTLGTTDETEETTKDVEM 650
651 ADAPAAMDGSVANDDDTSDESVDMDDDLGSSEESDDDEEEEEDDEEEEES 700
701 DDEPAGLLGLFFGKSELKKLKEEAVTEEPSVEPAGDVEMTDASAALRPEL 750
751 QVNGHAHEAPITNGTHTNEQLASSQTEGADDEVSLLVIPNDEIAAENTQT 800
801 AAEPGSGVVEKDFLTNDSSLKYPNEIVQSENQTLPAKESVEAGGDLPMVD 850
851 APSTDDLQRETAAAPSLEAPPNTRHDSQETVAATDMQSQSQTQSPKTTDT 900
901 KPTDVDTPHSELAVSVQKPDSRQSSPQPTTPTVKTEIPFLLRGTLREYQH 950
951 HGLDWLAGLYANNTNGILADEMGLGKTIQTIALLAHLACHHEVWGPHLVI 1000
1001 VPTSVMLNWEMEFKKWCPGFKILTYYGNQEERKRKRQGWNNDDVWNVCIT 1050
1051 SYQMVLQDQQVFRRRRWHYMILDEAHNIKNFKSQRWQTLLGFNTQARLLL 1100
1101 TGTPLQNNLTELWSLLYFLAPPENGEGGFVDLTEFHNWFARPESQILESG 1150
1151 REQLDDEARAIIAKLHKVLRPYLLRRLKADVEKQMPAKYEHVEFCRLSKR 1200
1201 QRELYDGFLSRADTRETLQSGNYMSIINCLMQLRKVCNHPDLFVDRPIMT 1250
1251 SFRMSRSVPADYEWKEKFIRNRLLATKPMTTVNLSFLNMIPTEYEDLSKT 1300
1301 HTDRIAQLSSHRILLDLREAQKVRANNAYTALDPSSVKSNLVYLESAARW 1350
1351 GRFEELQHCVYINALRRQQRPIYGKRLTEFLTLDTHLRPYKPRPRVPAKI 1400
1401 MSWFEEDSFLLHNAIPTLQQRAESMEMTITKFACVTPAVVTGPEMNRFLL 1450
1451 GERGIQLFEDLDLKLSAPVKYAPYMPPQPPPDPWHEARMRLTIQFPDKRL 1500
1501 LQYDCGKLQALDKLLRKLQAGGHRALIFTQMTKVLDILEQFLNIHGHKYL 1550
1551 RLDGATKVEQRQILTDRFNHDPRILCFILSTRSGGLGINLTGADTVIFYD 1600
1601 QDWNPAMDKQCQDRCHRIGQTRDVHIYRLVSEHTIEANILRKASQKQMLD 1650
1651 DVVIQEGEFTTDYFNRLSVRDVLGSNGEVIASNEDDVAANLAMDRVLGGP 1700
1701 STTGAGGYDGTADGGGGASQPPVRNVGRVLEMAEDREDVDAAKAAEKEIM 1750
1751 QDEADFGEAGSTRPGTPGDGLADLDGQLLGGEENKEVEDEVIEYNAWGER 1800
1801 MHTIDEYMLGFMAKALEGTPLELPRDRKKGRDRNRNRKGKDSRKR 1845
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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