Assessment of data-assisted prediction by inclusion of crosslinking/mass-spectrometry and small angle X-ray scattering data in the 12th Critical Assessment of protein Structure Prediction experiment

The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.

Nuclear pore complexes (NPCs) are proteinaceous assemblies of approximately 50 MDa that selectively transport cargoes across the nuclear envelope. To determine the molecular architecture of the yeast NPC, we collected a diverse set of biophysical and proteomic data, and developed a method for using these data to localize the NPC's 456 constituent proteins (see the accompanying paper). Our structure reveals that half of the NPC is made up of a core scaffold, which is structurally analogous to vesicle-coating complexes. This scaffold forms an interlaced network that coats the entire curved surface of the nuclear envelope membrane within which the NPC is embedded. The selective barrier for transport is formed by large numbers of proteins with disordered regions that line the inner face of the scaffold. The NPC consists of only a few structural modules that resemble each other in terms of the configuration of their homologous constituents, the most striking of these being a 16-fold repetition of 'columns'. These findings provide clues to the evolutionary origins of the NPC.

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. Copyright © 2012 Elsevier Inc. All rights reserved.