title: All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences.
abstract: Transmembrane β-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 α-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 β-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 β-strands at an accuracy of ∼70%. 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 β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.
authors: Department of Systems Biology, Harvard Medical School, Boston, 02115 MA; Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, 10065 NY; and.
authors: Sikander
authors: S
authors: Hayat
authors: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, 10065 NY; and arne@bioinfo.se debbie@hms.harvard.edu cccsander@gmail.com.
authors: Chris
authors: C
authors: Sander
authors: Department of Systems Biology, Harvard Medical School, Boston, 02115 MA; arne@bioinfo.se debbie@hms.harvard.edu cccsander@gmail.com.
authors: Debora S
authors: DS
authors: Marks
authors: 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.
authors: Arne
authors: http://orcid.org/0000-0002-7115-9751
authors: A
authors: Elofsson
mesh_terms: D001185:Artificial Intelligence; D004926:Escherichia coli / Q000737:chemistry* / Q000235:genetics; D029968:Escherichia coli Proteins / Q000737:chemistry* / Q000235:genetics; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011956:Receptors, Cell Surface / Q000737:chemistry* / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D029968:Escherichia coli Proteins; C028589:FecA protein, E coli; D011956:Receptors, Cell Surface
keywords: de novo 3D structure prediction; evolutionary couplings; hydrogen bonding; maximum-entropy analysis; transmembrane β-barrels