Principles of sensor-effector organization in six-transmembrane ion channels

Receptor proteins sense stimuli and generate downstream signals via sensor and effector domains. Presently, the structural constraints on sensor-effector organization across receptor protein superfamilies are not clear. Here, we perform statistical coupling analysis (SCA) on the transient receptor potential (TRP) and voltage-gated potassium (Kv) ion channel superfamilies to characterize the networks of coevolving residues, or protein sectors, that mediate their receptor functions. Comparisons to structural and functional studies reveal a conserved "core" sector that extends from the pore and mediates effector functions, including pore gating and sensor-pore coupling, while sensors correspond to family-specific "accessory" sectors and localize according to three principles: Sensors (1) may emerge in any region with access to the core, (2) must maintain contact with the core, and (3) must preserve the integrity of the core. This sensor-core architecture may represent a conserved and generalizable paradigm for the structure-function relationships underlying the evolution of receptor proteins.

Coevolution underlies GPCR-G protein selectivity and functionality

G protein-coupled receptors (GPCRs) regulate diverse physiological events, which makes them as the major targets for many approved drugs. G proteins are downstream molecules that receive signals from GPCRs and trigger cell responses. The GPCR-G protein selectivity mechanism on how they properly and timely interact is still unclear. Here, we analyzed model GPCRs (i.e. HTR, DAR) and Gα proteins with a coevolutionary tool, statistical coupling analysis. The results suggested that 5-hydroxytryptamine receptors and dopamine receptors have common conserved and coevolved residues. The Gα protein also have conserved and coevolved residues. These coevolved residues were implicated in the molecular functions of the analyzed proteins. We also found specific coevolving pairs related to the selectivity between GPCR and G protein were identified. We propose that these results would contribute to better understandings of not only the functional residues of GPCRs and Gα proteins but also GPCR-G protein selectivity mechanisms.

RNA sectors and allosteric function within the ribosome

The ribosome translates the genetic code into proteins in all domains of life. Its size and complexity demand long-range interactions that regulate ribosome function. These interactions are largely unknown. Here, we apply a global coevolution method, statistical coupling analysis (SCA), to identify coevolving residue networks (sectors) within the 23S ribosomal RNA (rRNA) of the large ribosomal subunit. As in proteins, SCA reveals a hierarchical organization of evolutionary constraints with near-independent groups of nucleotides forming physically contiguous networks within the three-dimensional structure. Using a quantitative, continuous-culture-with-deep-sequencing assay, we confirm that the top two SCA-predicted sectors contribute to ribosome function. These sectors map to distinct ribosome activities, and their origins trace to phylogenetic divergences across all domains of life. These findings provide a foundation to map ribosome allostery, explore ribosome biogenesis, and engineer ribosomes for new functions. Despite differences in chemical structure, protein and RNA enzymes appear to share a common internal logic of interaction and assembly.

Coevolution-based inference of amino acid interactions underlying protein function

Protein function arises from a poorly understood pattern of energetic interactions between amino acid residues. Sequence-based strategies for deducing this pattern have been proposed, but lack of benchmark data has limited experimental verification. Here, we extend deep-mutation technologies to enable measurement of many thousands of pairwise amino acid couplings in several homologs of a protein family – a deep coupling scan (DCS). The data show that cooperative interactions between residues are loaded in a sparse, evolutionarily conserved, spatially contiguous network of amino acids. The pattern of amino acid coupling is quantitatively captured in the coevolution of amino acid positions, especially as indicated by the statistical coupling analysis (SCA), providing experimental confirmation of the key tenets of this method. This work exposes the collective nature of physical constraints on protein function and clarifies its link with sequence analysis, enabling a general practical approach for understanding the structural basis for protein function.

Molecular dynamics simulations and statistical coupling analysis of GPI12 in L. major: functional co-evolution and conservedness reveals potential drug–target sites

GPI12 represents an important enzyme in the GPI biosynthetic pathway of several parasites like ‘Leishmania’.