>Q6P1K8 (150 residues) ECSLIRMGFPQHTIASLSDQDAKPSFSMAHLDGNTEPGLTLGGYFCPQCRAKYCELPVEC KICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQN VFCVDCDVFVHDSLHCCPGCIHKIPAPSGV |
Sequence |
20 40 60 80 100 120 140 | | | | | | | ECSLIRMGFPQHTIASLSDQDAKPSFSMAHLDGNTEPGLTLGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIHKIPAPSGV |
Prediction | CCCSSSSCCCCCCCCCCCCCCCCCSSSSCCCCCCCCCCCCCCCSSCCCCCCSCCCCCCCCCCCCCSSCCCHHHHHHHCCCCCCCCCCSCCCCCCCCCCCCSCCCCCCCCCCSSSCCCCCCCCCCCHHHHHHHHHCCCCCCCCCCCCCCCC |
Confidence | 986356368875556666667886156602677777764458634588676326899746666857626367788633568988630336444688884342355478886588699896113543489997511598974677787789 |
H:Helix; S:Strand; C:Coil | |
Sequence |
20 40 60 80 100 120 140 | | | | | | | ECSLIRMGFPQHTIASLSDQDAKPSFSMAHLDGNTEPGLTLGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIHKIPAPSGV |
Prediction | 834234020023334546666442201001255665651445212013042431513241410312013233035224432416416515476376542031034414663203044056310140122014213303203354645758 |
Values range from 0 (buried residue) to 8 (highly exposed residue) | |
Rank | PDB hit | ID1 | ID2 | Cov | Norm. Zscore | Downloadalignment | 20 40 60 80 100 120 140 | | | | | | | | |||||||||||||
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SS Seq | CCCSSSSCCCCCCCCCCCCCCCCCSSSSCCCCCCCCCCCCCCCSSCCCCCCSCCCCCCCCCCCCCSSCCCHHHHHHHCCCCCCCCCCSCCCCCCCCCCCCSCCCCCCCCCCSSSCCCCCCCCCCCHHHHHHHHHCCCCCCCCCCCCCCCC ECSLIRMGFPQHTIASLSDQDAKPSFSMAHLDGNTEPGLTLGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIHKIPAPSGV | |||||||||||||||||||
1 | 6nmiE | 0.61 | 0.53 | 15.28 | 0.83 | DEthreader | VRLMMPIGFPQHTIGGGGGGG-GGGGG--GGGGG-GG-GGGGY-FCPQCRAKYCELPVECCGLTLVSAPH--AR-SYHHLFPLDAFQEILEEYNGER-FCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCC-PGCIH------- | |||||||||||||
2 | 6nmiE | 0.85 | 0.77 | 21.79 | 2.56 | SPARKS-K | ECSLIRMGFPQHTIGGGGGGGGGGGGGGGG------GGGGGGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIH-------- | |||||||||||||
3 | 6nmiE | 0.85 | 0.75 | 21.04 | 1.39 | MapAlign | ----IRMGFPQHTIGGGGGGGGGGGGGGGGG------GGGGGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIH-------- | |||||||||||||
4 | 6nmiE | 0.85 | 0.77 | 21.79 | 1.49 | CEthreader | ECSLIRMGFPQHTIGGGGGGGGGGGGGGGG------GGGGGGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIH-------- | |||||||||||||
5 | 6nmiE | 0.85 | 0.77 | 21.79 | 1.87 | MUSTER | ECSLIRMGFPQHTIGGGGGGGGGGGGGGGG------GGGGGGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIH-------- | |||||||||||||
6 | 6nmiE | 0.85 | 0.77 | 21.79 | 4.86 | HHsearch | ECSLIRMGFPQHTIGGGGGGGGGGGGGGGGGGG------GGGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIH-------- | |||||||||||||
7 | 6nmiE | 0.85 | 0.77 | 21.61 | 1.37 | FFAS-3D | ECSLIRMGFPQHTIGGGGGGGGGGGGGGGGGG------GGGGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHCCPGCIH-------- | |||||||||||||
8 | 6nmiE | 0.60 | 0.53 | 15.10 | 1.02 | EigenThreader | CSLIRMGFPQ-HTIGGGGGGGGGG------GGGGGGGGGGGGGYFCPQCRAKYCELPVECKICGLTLVSAPHLARSYHHLFPLDFQEIPLEE---YNGERFCYGCQGELKDQYVCAVCQNVFCVDCDVFVHDSLHCCPGCIH-------- | |||||||||||||
9 | 1z60A | 0.98 | 0.39 | 10.83 | 1.95 | CNFpred | ----------------------------------------------------------------------------------LDAFQEIPLEEYNGERFCYGCQGELKDQHVYVCAVCQNVFCVDCDVFVHDSLHSCPGCI--------- | |||||||||||||
10 | 6gym6 | 0.26 | 0.21 | 6.59 | 0.83 | DEthreader | GGG-G-GGGPTRIFEDTP-----T-FC---SC-HSKL--VYGGYFCPNCHSKVCSLPTVCDL--MLILGG-GGGGGGGGGGGGKTFAEVPTTKFRSED-CFS-CQSRFPGSRYRCEDCKQEFCVDCDVFIH-EILHNCPGCESK------ | |||||||||||||
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Top 10 structural analogs in PDB (as identified by
TM-align)
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Top 5 enzyme homologs in PDB
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Template proteins with similar binding site:
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References: | |
1. | Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, Yang Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. In preparation, 2020. |