On the Specificity and Permanence of Electroencephalography Functional Connectivity

Functional connectivity, representing a statistical coupling relationship between different brain regions or electrodes, is an influential concept in clinical medicine and cognitive neuroscience. Electroencephalography-derived functional connectivity (EEG-FC) provides relevant characteristic information about individual differences in cognitive tasks and personality traits. However, it remains unclear whether these individual-dependent EEG-FCs remain relatively permanent across long-term sessions. This manuscript utilizes machine learning algorithms to explore the individual specificity and permanence of resting-state EEG connectivity patterns. We performed six recordings at different intervals during a six-month period to examine the variation and permanence of resting-state EEG-FC over a long period. The results indicated that the EEG-FC networks are quite subject-specific with a high-precision identification accuracy of greater than 90%. Meanwhile, the individual specificity remained stable and only varied slightly after six months. Furthermore, the specificity is mainly derived from the internal connectivity of the frontal lobe. Our work demonstrates the existence of specific and permanent EEG-FC patterns in the brain, providing potential information for biometric applications.

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.

Extending the application of the SCA/Sectors method for the identification of domain boundaries and subtype specific residues in multi-domain biosynthetic proteins: Application to Polyketide Synthases

Polypeptides with multiple enzyme domains, such as type I polyketide synthases, produce chemically complex compounds that are difficult to produce via conventional chemical synthesis and are often pharmaceutically or otherwise commercially valuable. Engineering polyketide synthases, via domain swapping and/or site directed mutagenesis, in order to generate novel polyketides, has tended to produce either low yields of product or no product at all. The success of such experiments may be limited by our inability to predict the key functional residues and boundaries of protein domains. Computational tools to identify the boundaries and the residues determining the substrate specificity of domains could reduce the trial and error involved in engineering multi-domain proteins. In this study we use statistical coupling analysis to identify networks of co-evolving residues in type I polyketide synthases, thereby predicting domain boundaries. We extend the method to predicting key residues for enzyme substrate specificity. We introduce bootstrapping calculations to test the relationship between sequence length and the number of sequences needed for a robust analysis. Our results show no simple predictor of the number of sequences needed for an analysis, which can be as few as a hundred and as many as a few thousand. We find that polyketide synthases contain multiple networks of co-substituting residues: some are intradomain but most multiple domains. Some networks of coupled residues correlate with specific functions such as the substrate specificity of the acyl transferase domain, the stereo chemistry of the ketoreductase domain, or domain boundaries that are consistent with experimental data. Our extension of the method provides a ranking of the likely importance of these residues to enzyme substrate specificity, allowing us to propose residues for further mutagenesis work. We conclude that analysis of co-evolving networks of residues is likely to be an important tool for re-engineering multi-domain 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.

Extracting the phylogenetic dimension of coevolution reveals hidden functional signal

Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein’s phylogenetic history and gives rise to concurrent variation between protein sequences that is not driven by selection for function. Here, we introduce a method for explicitly defining a phylogenetic dimension of coevolution signal, and demonstrate that coevolution can occur on multiple phylogenetic timescales within a single protein. Our method, Nested Coevolution (NC), can be applied as an extension to any coevolution metric. We use NC to demonstrate that poorly conserved residues can nonetheless have important roles in protein function. Moreover, NC improved structural-contact prediction over gold-standard coevolution-based methods, particularly in subsampled alignments with fewer sequences. NC also lowered the noise in detecting functional sectors of collectively coevolving residues. Sectors of coevolving residues identified after NC correction were more spatially compact and phylogenetically distinct from the rest of the protein, and strongly enriched for mutations that disrupt protein activity. Our conceptualization of the phylogenetic separation of coevolution represents an advance from previous pragmatic attempts to reduce phylogenetic artifacts in measurements of coevolution. Application of NC broadens the application of protein coevolution measurements, particularly to eukaryotic proteins with fewer naturally available sequences, and further elucidates relationships among protein evolution and genetic diseases.

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.

Adaption of human antibody λ and κ light chain architectures to CDR repertoires

Monoclonal antibodies bind with high specificity to a wide range of diverse antigens, primarily mediated by their hypervariable complementarity determining regions (CDRs). The defined antigen binding loops are supported by the structurally conserved β-sandwich framework of the light chain (LC) and heavy chain (HC) variable regions. The LC genes are encoded by two separate loci, subdividing the entity of antibodies into kappa (LCκ) and lambda (LCλ) isotypes that exhibit distinct sequence and conformational preferences. In this work, a diverse set of techniques were employed including machine learning, force field analysis, statistical coupling analysis and mutual information analysis of a non-redundant antibody structure collection. Thereby, it was revealed how subtle changes between the structures of LCκ and LCλ isotypes increase the diversity of antibodies, extending the predetermined restrictions of the general antibody fold and expanding the diversity of antigen binding. Interestingly, it was found that the characteristic framework scaffolds of κ and λ are stabilized by diverse amino acid clusters that determine the interplay between the respective fold and the embedded CDR loops. In conclusion, this work reveals how antibodies use the remarkable plasticity of the beta-sandwich Ig fold to incorporate a large diversity of CDR loops.