Evolutionary-guided de novo structure prediction of self-associated transmembrane helical proteins with near-atomic accuracy

Y. Wang and P. Barth ()
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Y. Wang: Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA
P. Barth: Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA

Nature Communications, 2015, vol. 6, issue 1, 1-12

Abstract: Abstract How specific protein associations regulate the function of membrane receptors remains poorly understood. Conformational flexibility currently hinders the structure determination of several classes of membrane receptors and associated oligomers. Here we develop EFDOCK-TM, a general method to predict self-associated transmembrane protein helical (TMH) structures from sequence guided by co-evolutionary information. We show that accurate intermolecular contacts can be identified using a combination of protein sequence covariation and TMH binding surfaces predicted from sequence. When applied to diverse TMH oligomers, including receptors characterized in multiple conformational and functional states, the method reaches unprecedented near-atomic accuracy for most targets. Blind predictions of structurally uncharacterized receptor tyrosine kinase TMH oligomers provide a plausible hypothesis on the molecular mechanisms of disease-associated point mutations and binding surfaces for the rational design of selective inhibitors. The method sets the stage for uncovering novel determinants of molecular recognition and signalling in single-spanning eukaryotic membrane receptors.

Date: 2015
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DOI: 10.1038/ncomms8196

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