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.

We introduce a theoretical framework that exploits the ever-increasing genomic sequence information for protein structure prediction. Structure-based models are modified to incorporate constraints by a large number of non-local contacts estimated from direct coupling analysis (DCA) of co-evolving genomic sequences. A simple hybrid method, called DCA-fold, integrating DCA contacts with an accurate knowledge of local information (e.g., the local secondary structure) is sufficient to fold proteins in the range of 1–3 Å resolution.

Significance: We show that directly coupled evolutionary residue pairs provide a distinct footprint of conformational diversity in protein families. This is revealed as competing residue contacts unique to distinct configurations of proteins with multiple conformations. We demonstrate that combining this information with physical models of proteins is sufficient to uncover conformational diversity for several protein families. We discovered that such directly coupled residues not only allow us to accurately transition between apo and holo conformations but also help to uncover intermediate states that are not easily accessible to experimental or other computational methods. This enhanced sampling of the functional conformational space of proteins may have broad implications in protein structure determination and the design of ligands that can trap intermediate states.

The energy landscape used by nature over evolutionary timescales to select protein sequences is essentially the same as the one that folds these sequences into functioning proteins, sometimes in microseconds. We show that genomic data, physical coarse-grained free energy functions, and family-specific information theoretic models can be combined to give consistent estimates of energy landscape characteristics of natural proteins. One such characteristic is the effective temperature T_sel at which these foldable sequences have been selected in sequence space by evolution. T_sel quantifies the importance of folded-state energetics and structural specificity for molecular evolution. Across all protein families studied, our estimates for T_sel are well below the experimental folding temperatures, indicating that the energy landscapes of natural foldable proteins are strongly funneled toward the native state.

Significance: Our study uses amino acid coevolutionary information to better understand how bacterial two-component signaling (TCS) proteins preferentially interact with their correct partners while avoiding interactions with nonpartners. We extract coevolutionary couplings from sequences of TCS partners and study how coevolution is necessary to maintain their ability to transfer signals with high specificity. We use these coevolving couplings to devise a metric, which can predict the effects of mutations in the quality of signal transmission observed in vitro and provide support to the hypothesis that hybrid TCS proteins have reduced specificity. Our metric can potentially be used to redesign a TCS protein to preferentially interact with a nonpartner. Furthermore, our study can potentially be extended to networks of interacting proteins.