Article

Three-dimensional structures of membrane proteins from genomic sequencing.

Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
Cell (impact factor: 32.4). 05/2012; 149(7):1607-21. DOI:10.1016/j.cell.2012.04.012 pp.1607-21
Source: PubMed

ABSTRACT We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.

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Keywords

11 transmembrane proteins
 
all-atom models
 
amino acid covariation
 
comprehensive information
 
de novo computation
 
derived pairwise distance constraints
 
evolutionary sequence record
 
functional sites
 
infer evolutionary covariation
 
large transmembrane proteins
 
maximum entropy approach
 
prediction method
 
proteins
 
rapid rise
 
sequence positions
 
transmembrane protein structures
 
transmembrane proteins
 
transmembrane proteins amenable
 
unknown 3D structures
 
unprecedented accuracy