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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Sander, Chris Hayat, Sikander Marks, Debora S. Elofsson, Arne |
| Description | Author Affiliation: Hayat S ( Department of Systems Biology, Harvard Medical School, Boston, 02115 MA); Sander C ( Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, 10065 NY); Marks DS ( Department of Systems Biology, Harvard Medical School, Boston, 02115 MA); Elofsson A ( Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden arne@bioinfo.se debbie@hms.harvard.edu cccsander@gmail.com.); |
| Abstract | Transmembrane ß-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and -helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting ß-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent ß-strands at an accuracy of â ¼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of ß-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases. |
| ISSN | 00278424 |
| e-ISSN | 10916490 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Issue Number | 17 |
| Volume Number | 112 |
| Language | English |
| Publisher | National Academy of Sciences |
| Publisher Date | 2015-04-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Artificial Intelligence Escherichia Coli Proteins Chemistry Escherichia Coli Protein Structure, Secondary Receptors, Cell Surface Sequence Analysis, Protein Genetics Models, Molecular Protein Structure, Tertiary Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Multidisciplinary |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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