Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis

Clustered protocadherin cis-interactions are required for combinatorial cell-cell recognition underlying neuronal self-avoidance

In the developing human brain, only 53 stochastically expressed clustered protocadherin (cPcdh) isoforms enable neurites from individual neurons to recognize and self-avoid while simultaneously maintaining contact with neurites from other neurons. Cell assays have demonstrated that self-recognition occurs only when all cPcdh isoforms perfectly match across the cell boundary, with a single mismatch in the cPcdh expression profile interfering with recognition. It remains unclear, however, how a single mismatched isoform between neighboring cells is sufficient to block erroneous recognitions. Using systematic cell aggregation experiments, we show that abolishing cPcdh interactions on the same membrane (cis) results in a complete loss of specific combinatorial binding between cells (trans). Our computer simulations demonstrate that the organization of cPcdh in linear array oligomers, composed of cis and trans interactions, enhances self-recognition by increasing the concentration and stability of cPcdh trans complexes between the homotypic membranes. Importantly, we show that the presence of mismatched isoforms between cells drastically diminishes the concentration and stability of the trans complexes. Overall, we provide an explanation for the role of the cPcdh assembly arrangements in neuronal self/non-self-discrimination underlying neuronal self-avoidance.

Characterization of transcriptional profiles associated with stress-induced neuronal activation in Arc-GFP mice

Chronic stress has become a predominant factor associated with a variety of psychiatric disorders, such as depression and anxiety, in both human and animal models. Although multiple studies have looked at transcriptional changes after social defeat stress, these studies primarily focus on bulk tissues, which might dilute important molecular signatures of social interaction in activated cells. In this study, we employed the Arc-GFP mouse model in conjunction with chronic social defeat (CSD) to selectively isolate activated nuclei (AN) populations in the ventral hippocampus (vHIP) and prefrontal cortex (PFC) of resilient and susceptible animals. Nuclear RNA-seq of susceptible vs. resilient populations revealed distinct transcriptional profiles linked predominantly with neuronal and synaptic regulation mechanisms. In the vHIP, susceptible AN exhibited increased expression of genes related to the cytoskeleton and synaptic organization. At the same time, resilient AN showed upregulation of cell adhesion genes and differential expression of major glutamatergic subunits. In the PFC, susceptible mice exhibited upregulation of synaptotagmins and immediate early genes (IEGs), suggesting a potentially over-amplified neuronal activity state. Our findings provide a novel view of stress-exposed neuronal activation and the molecular response mechanisms in stress-susceptible vs. resilient animals, which may have important implications for understanding mental resilience.

Comparative analysis of structural dynamics and allosteric mechanisms of RecA/Rad51 family proteins: Integrated atomistic MD simulation and network-based analysis

Homologous recombination plays a key role in double-strand break repair, stalled replication fork repair, and meiosis. The RecA/Rad51 family recombinases catalyze the DNA strand invasion reaction that occurs during homologous recombination. However, the high sequence differences between homologous groups have hindered the thoroughly studies of this ancient protein family. The dynamic mechanisms of the family, particularly at the residual level, remain poorly understood. In this work, five representative RecA/Rad51 recombinase family members from all major kingdoms of living organisms: prokaryotes, eukaryotes, archaea, and viruses, were selected to explore the molecular mechanisms behind their conserved biological significance. A variety of techniques, including all-atom molecular dynamics simulation, perturbation response scanning, and protein structure network analysis, were used to examine the flexibility and correlation of protein domains, distribution of sensors and effectors and conserved hub residues. Furthermore, the potential communication routes between the ATP-binding region and the DNA-binding region of each recombinase were identified. Our results demonstrate the conserved molecular dynamics of these recombinases in the early stage of homologous recombination, including cooperative motions between regions, conserved sensing and effecting functional residue distribution, and conserved hub residues. Meanwhile, the unique ATP-DNA communication routes of each recombinase was also revealed. These results provide new insights into the mechanism of RecA/Rad51 family proteins, and provide new theoretical guidance for the development of allosteric inhibitors and the application of RecA/Rad51 family proteins.

Confluence and convergence of Dscam and Pcdh cell-recognition codes

The ability of neurites of the same neuron to avoid each other (self-avoidance) is a conserved feature in both invertebrates and vertebrates. The key to self-avoidance is the generation of a unique subset of cell-surface proteins in individual neurons engaging in isoform-specific homophilic interactions that drive neurite repulsion rather than adhesion. Among these cell-surface proteins are fly Dscam1 and vertebrate clustered protocadherins (cPcdhs), as well as the recently characterized shortened Dscam (sDscam) in the Chelicerata. Herein, we review recent advances in our understanding of how cPcdh, Dscam, and sDscam cell-surface recognition codes are expressed and translated into cellular functions essential for neural wiring.

Marginal specificity in protein interactions constrains evolution of a paralogous family

The evolution of novel functions in biology relies heavily on gene duplication and divergence, creating large paralogous protein families. Selective pressure to avoid detrimental cross-talk often results in paralogs that exhibit exquisite specificity for their interaction partners. But how robust or sensitive is this specificity to mutation? Here, using deep mutational scanning, we demonstrate that a paralogous family of bacterial signaling proteins exhibits marginal specificity, such that many individual substitutions give rise to substantial cross-talk between normally insulated pathways. Our results indicate that sequence space is locally crowded despite overall sparseness, and we provide evidence that this crowding has constrained the evolution of bacterial signaling proteins. These findings underscore how evolution selects for "good enough" rather than optimized phenotypes, leading to restrictions on the subsequent evolution of paralogs.

Structure of the planar cell polarity cadherins Fat4 and Dachsous1

The atypical cadherins Fat and Dachsous are key regulators of cell growth and animal development. In contrast to classical cadherins, which form homophilic interactions to segregate cells, Fat and Dachsous cadherins form heterophilic interactions to induce cell polarity within tissues. Here, we determine the co-crystal structure of the human homologs Fat4 and Dachsous1 (Dchs1) to establish the molecular basis for Fat-Dachsous interactions. The binding domains of Fat4 and Dchs1 form an extended interface along extracellular cadherin (EC) domains 1-4 of each protein. Biophysical measurements indicate that Fat4-Dchs1 affinity is among the highest reported for cadherin superfamily members, which is attributed to an extensive network of salt bridges not present in structurally similar protocadherin homodimers. Furthermore, modeling suggests that unusual extracellular phosphorylation modifications directly modulate Fat-Dachsous binding by introducing charged contacts across the interface. Collectively, our analyses reveal how the molecular architecture of Fat4-Dchs1 enables them to form long-range, high-affinity interactions to maintain planar cell polarity.

General strategies for using amino acid sequence data to guide biochemical investigation of protein function

The rapid increase of '-omics' data warrants the reconsideration of experimental strategies to investigate general protein function. Studying individual members of a protein family is likely insufficient to provide a complete mechanistic understanding of family functions, especially for diverse families with thousands of known members. Strategies that exploit large amounts of available amino acid sequence data can inspire and guide biochemical experiments, generating broadly applicable insights into a given family. Here we review several methods that utilize abundant sequence data to focus experimental efforts and identify features truly representative of a protein family or domain. First, coevolutionary relationships between residues within primary sequences can be successfully exploited to identify structurally and/or functionally important positions for experimental investigation. Second, functionally important variable residue positions typically occupy a limited sequence space, a property useful for guiding biochemical characterization of the effects of the most physiologically and evolutionarily relevant amino acids. Third, amino acid sequence variation within domains shared between different protein families can be used to sort a particular domain into multiple subtypes, inspiring further experimental designs. Although generally applicable to any kind of protein domain because they depend solely on amino acid sequences, the second and third approaches are reviewed in detail because they appear to have been used infrequently and offer immediate opportunities for new advances. Finally, we speculate that future technologies capable of analyzing and manipulating conserved and variable aspects of the three-dimensional structures of a protein family could lead to broad insights not attainable by current methods.

Elastic versus brittle mechanical responses predicted for dimeric cadherin complexes

Cadherins are a superfamily of adhesion proteins involved in a variety of biological processes that include the formation of intercellular contacts, the maintenance of tissue integrity, and the development of neuronal circuits. These transmembrane proteins are characterized by ectodomains composed of a variable number of extracellular cadherin (EC) repeats that are similar but not identical in sequence and fold. E-cadherin, along with desmoglein and desmocollin proteins, are three classical-type cadherins that have slightly curved ectodomains and engage in homophilic and heterophilic interactions through an exchange of conserved tryptophan residues in their N-terminal EC1 repeat. In contrast, clustered protocadherins are straighter than classical cadherins and interact through an antiparallel homophilic binding interface that involves overlapped EC1 to EC4 repeats. Here we present molecular dynamics simulations that model the adhesive domains of these cadherins using available crystal structures, with systems encompassing up to 2.8 million atoms. Simulations of complete classical cadherin ectodomain dimers predict a two-phased elastic response to force in which these complexes first softly unbend and then stiffen to unbind without unfolding. Simulated α, β, and γ clustered protocadherin homodimers lack a two-phased elastic response, are brittle and stiffer than classical cadherins and exhibit complex unbinding pathways that in some cases involve transient intermediates. We propose that these distinct mechanical responses are important for function, with classical cadherin ectodomains acting as molecular shock absorbers and with stiffer clustered protocadherin ectodomains facilitating overlap that favors binding specificity over mechanical resilience. Overall, our simulations provide insights into the molecular mechanics of single cadherin dimers relevant in the formation of cellular junctions essential for tissue function.

Modeling the Dynamics of Protein–Protein Interfaces, How and Why?

Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.

How clustered protocadherin binding specificity is tuned for neuronal self-/nonself-recognition

The stochastic expression of fewer than 60 clustered protocadherin (cPcdh) isoforms provides diverse identities to individual vertebrate neurons and a molecular basis for self-/nonself-discrimination. cPcdhs form chains mediated by alternating cis and trans interactions between apposed membranes, which has been suggested to signal self-recognition. Such a mechanism requires that cPcdh cis dimers form promiscuously to generate diverse recognition units, and that trans interactions have precise specificity so that isoform mismatches terminate chain growth. However, the extent to which cPcdh interactions fulfill these requirements has not been definitively demonstrated. Here, we report biophysical experiments showing that cPcdh cis interactions are promiscuous, but with preferences favoring formation of heterologous cis dimers. Trans homophilic interactions are remarkably precise, with no evidence for heterophilic interactions between different isoforms. A new C-type cPcdh crystal structure and mutagenesis data help to explain these observations. Overall, the interaction characteristics we report for cPcdhs help explain their function in neuronal self-/nonself-discrimination.

Heterophilic and homophilic cadherin interactions in intestinal intermicrovillar links are species dependent

Enterocytes are specialized epithelial cells lining the luminal surface of the small intestine that build densely packed arrays of microvilli known as brush borders. These microvilli drive nutrient absorption and are arranged in a hexagonal pattern maintained by intermicrovillar links formed by 2 nonclassical members of the cadherin superfamily of calcium-dependent cell adhesion proteins: protocadherin-24 (PCDH24, also known as CDHR2) and the mucin-like protocadherin (CDHR5). The extracellular domains of these proteins are involved in heterophilic and homophilic interactions important for intermicrovillar function, yet the structural determinants of these interactions remain unresolved. Here, we present X-ray crystal structures of the PCDH24 and CDHR5 extracellular tips and analyze their species-specific features relevant for adhesive interactions. In parallel, we use binding assays to identify the PCDH24 and CDHR5 domains involved in both heterophilic and homophilic adhesion for human and mouse proteins. Our results suggest that homophilic and heterophilic interactions involving PCDH24 and CDHR5 are species dependent with unique and distinct minimal adhesive units.

The conserved serine transporter SdaC moonlights to enable self recognition

Cells can use self recognition to achieve cooperative behaviors. Self-recognition genes are thought to principally evolve in tandem with partner self-recognition alleles. However, other constraints on protein evolution could exist. Here, we have identified an interaction outside of self-recognition loci that could constrain the sequence variation of a self-recognition protein. We show that during collective swarm expansion in Proteus mirabilis, self-recognition signaling co-opts SdaC, a serine transporter. Serine uptake is crucial for bacterial survival and colonization. Single-residue variants of SdaC reveal that self recognition requires an open conformation of the protein; serine transport is dispensable. A distant ortholog from Escherichia coli is sufficient for self recognition; however, a paralogous serine transporter, YhaO, is not. Thus, SdaC couples self recognition and serine transport, likely through a shared molecular interface. Self recognition proteins may follow the framework of a complex interaction network rather than an isolated two-protein system. Understanding molecular and ecological constraints on self-recognition proteins lays the groundwork for insights into the evolution of self recognition and emergent collective behaviors. Importance Bacteria can receive secret messages from kin during migration. For Proteus mirabilis, these messages are necessary for virulence in multi-species infections. We show that a serine transporter-conserved among gamma-enterobacteria- enables self recognition. Molecular co-option of nutrient uptake could limit the sequence variation of these message proteins. SdaC is the primary transporter for L-serine, a vital metabolite for colonization during disease. Unlike many self-recognition receptors, SdaC is sufficiently conserved between species to achieve recognition. The predicted open conformation is shared by transport and recognition. SdaC reveals the interdependence of communication and nutrient acquisition. As the broader interactions of self-recognition proteins are studied, features shared among microbial self-recognition systems, such as Dictyostelium spp. and Neurospora spp., could emerge.

Constraints on the expansion of paralogous protein families

Duplication and divergence is a major mechanism by which new proteins and functions emerge in biology. Consequently, most organisms, in all domains of life, have genomes that encode large paralogous families of proteins. For recently duplicated pathways to acquire different, independent functions, the two paralogs must acquire mutations that effectively insulate them from one another. For instance, paralogous signaling proteins must acquire mutations that endow them with different interaction specificities such that they can participate in different signaling pathways without disruptive cross-talk. Although duplicated genes undoubtedly shape each other's evolution as they diverge and attain new functions, it is less clear how other paralogs impact or constrain gene duplication. Does the establishment of a new pathway by duplication and divergence require the system-wide optimization of all paralogs? The answer has profound implications for molecular evolution and our ability to engineer biological systems. Here, we discuss models, experiments, and approaches for tackling this question, and for understanding how new proteins and pathways are born.

Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl

Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the partner-selecting amino acid positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods’ ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune system diseases. For sequence-based predictions, we used SDPpred, Multi-RELIEF, and Sequence Harmony. For structure-based predictions, we utilized HADDOCK refinement and molecular dynamics simulations. As a result, we observed that (i) sequence-based methods overpredict partner-selecting residues of Axl and that (ii) combining Multi-RELIEF with HADDOCK-based predictions provides the key Axl residues, covered by the extensive molecular dynamics simulations. Expanding on these results, we propose that a sequence-structure-based approach is necessary to determine specificity-determining positions of Axl, which can guide the development of therapeutic molecules to combat Axl misregulation.

Moving pictures: Reassessing docking experiments with a dynamic view of protein interfaces

The modeling of protein assemblies at the atomic level remains a central issue in structural biology, as protein interactions play a key role in numerous cellular processes. This problem is traditionally addressed using docking tools, where the quality of the models is based on their similarity to a single reference experimental structure. However, using a static reference does not take into account the dynamic quality of the protein interface. Here, we used all-atom classical Molecular Dynamics simulations to investigate the stability of the reference interface for three complexes that previously served as targets in the CAPRI competition. For each one of these targets, we also ran MD simulations for ten models that are distributed over the High, Medium and Acceptable accuracy categories. To assess the quality of these models from a dynamic perspective, we set up new criteria which take into account the stability of the reference experimental protein interface. We show that, when the protein interfaces are allowed to evolve along time, the original ranking based on the static CAPRI criteria no longer holds as over 50% of the docking models undergo a category change (which can be either toward a better or a lower accuracy group) when reassessing their quality using dynamic information.

Data-driven computational protein design

Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.

Crystal structure of the nonclassical cadherin-17 N-terminus and implications for its adhesive binding mechanism

The cadherin superfamily of calcium-dependent cell-adhesion proteins has over 100 members in the human genome. All members of the superfamily feature at least a pair of extracellular cadherin (EC) repeats with calcium-binding sites in the EC linker region. The EC repeats across family members form distinct complexes that mediate cellular adhesion. For instance, classical cadherins (five EC repeats) strand-swap their N-termini and exchange tryptophan residues in EC1, while the clustered protocadherins (six EC repeats) use an extended antiparallel `forearm handshake' involving repeats EC1-EC4. The 7D-cadherins, cadherin-16 (CDH16) and cadherin-17 (CDH17), are the most similar to classical cadherins and have seven EC repeats, two of which are likely to have arisen from gene duplication of EC1-2 from a classical ancestor. However, CDH16 and CDH17 lack the EC1 tryptophan residue used by classical cadherins to mediate adhesion. The structure of human CDH17 EC1-2 presented here reveals features that are not seen in classical cadherins and that are incompatible with the EC1 strand-swap mechanism for adhesion. Analyses of crystal contacts, predicted glycosylation and disease-related mutations are presented along with sequence alignments suggesting that the novel features in the CDH17 EC1-2 structure are well conserved. These results hint at distinct adhesive properties for 7D-cadherins.

Clustered Protocadherins Emerge as Novel Susceptibility Loci for Mental Disorders

The clustered protocadherins (cPcdhs) are a subfamily of type I single-pass transmembrane cell adhesion molecules predominantly expressed in the brain. Their stochastic and combinatorial expression patterns encode highly diverse neural identity codes which are central for neuronal self-avoidance and non-self discrimination in brain circuit formation. In this review, we first briefly outline mechanisms for generating a tremendous diversity of cPcdh cell-surface assemblies. We then summarize the biological functions of cPcdhs in a wide variety of neurodevelopmental processes, such as neuronal migration and survival, dendritic arborization and self-avoidance, axonal tiling and even spacing, and synaptogenesis. We focus on genetic, epigenetic, and 3D genomic dysregulations of cPcdhs that are associated with various neuropsychiatric and neurodevelopmental diseases. A deeper understanding of regulatory mechanisms and physiological functions of cPcdhs should provide significant insights into the pathogenesis of mental disorders and facilitate development of novel diagnostic and therapeutic strategies.

Chelicerata sDscam isoforms combine homophilic specificities to define unique cell recognition

Thousands of Down syndrome cell adhesion molecule (Dscam1) isoforms and ∼60 clustered protocadhrein (cPcdh) proteins are required for establishing neural circuits in insects and vertebrates, respectively. The strict homophilic specificity exhibited by these proteins has been extensively studied and is thought to be critical for their function in neuronal self-avoidance. In contrast, significantly less is known about the Dscam1-related family of ∼100 shortened Dscam (sDscam) proteins in Chelicerata. We report that Chelicerata sDscamα and some sDscamβ protein trans interactions are strictly homophilic, and that the trans interaction is meditated via the first Ig domain through an antiparallel interface. Additionally, different sDscam isoforms interact promiscuously in cis via membrane proximate fibronectin-type III domains. We report that cell-cell interactions depend on the combined identity of all sDscam isoforms expressed. A single mismatched sDscam isoform can interfere with the interactions of cells that otherwise express an identical set of isoforms. Thus, our data support a model by which sDscam association in cis and trans generates a vast repertoire of combinatorial homophilic recognition specificities. We propose that in Chelicerata, sDscam combinatorial specificity is sufficient to provide each neuron with a unique identity for self-nonself discrimination. Surprisingly, while sDscams are related to Drosophila Dscam1, our results mirror the findings reported for the structurally unrelated vertebrate cPcdh. Thus, our findings suggest a remarkable example of convergent evolution for the process of neuronal self-avoidance and provide insight into the basic principles and evolution of metazoan self-avoidance and self-nonself discrimination.

Wiring the Brain by Clustered Protocadherin Neural Codes

There are more than a thousand trillion specific synaptic connections in the human brain and over a million new specific connections are formed every second during the early years of life. The assembly of these staggeringly complex neuronal circuits requires specific cell-surface molecular tags to endow each neuron with a unique identity code to discriminate self from non-self. The clustered protocadherin (Pcdh) genes, which encode a tremendous diversity of cell-surface assemblies, are candidates for neuronal identity tags. We describe the adaptive evolution, genomic structure, and regulation of expression of the clustered Pcdhs. We specifically focus on the emerging 3-D architectural and biophysical mechanisms that generate an enormous number of diverse cell-surface Pcdhs as neural codes in the brain.

Many facades of CTCF unified by its coding for three-dimensional genome architecture

CCCTC-binding factor (CTCF) is a multifunctional zinc finger protein that is conserved in metazoan species. CTCF is consistently found to play an important role in many diverse biological processes. CTCF/cohesin-mediated active chromatin 'loop extrusion' architects three-dimensional (3D) genome folding. The 3D architectural role of CTCF underlies its multifarious functions, including developmental regulation of gene expression, protocadherin (Pcdh) promoter choice in the nervous system, immunoglobulin (Ig) and T-cell receptor (Tcr) V(D)J recombination in the immune system, homeobox (Hox) gene control during limb development, as well as many other aspects of biology. Here, we review the pleiotropic functions of CTCF from the perspective of its essential role in 3D genome architecture and topological promoter/enhancer selection. We envision the 3D genome as an enormous complex architecture, with tens of thousands of CTCF sites as connecting nodes and CTCF proteins as mysterious bonds that glue together genomic building parts with distinct articulation joints. In particular, we focus on the internal mechanisms by which CTCF controls higher order chromatin structures that manifest its many façades of physiological and pathological functions. We also discuss the dichotomic role of CTCF sites as intriguing 3D genome nodes for seemingly contradictory 'looping bridges' and 'topological insulators' to frame a beautiful magnificent house for a cell's nuclear home.

Protocadherins at the Crossroad of Signaling Pathways

Protocadherins (Pcdhs) are cell adhesion molecules that belong to the cadherin superfamily, and are subdivided into clustered (cPcdhs) and non-clustered Pcdhs (ncPcdhs) in vertebrates. In this review, we summarize their discovery, expression mechanisms, and roles in neuronal development and cancer, thereby highlighting the context-dependent nature of their actions. We furthermore provide an extensive overview of current structural knowledge, and its implications concerning extracellular interactions between cPcdhs, ncPcdhs, and classical cadherins. Next, we survey the known molecular action mechanisms of Pcdhs, emphasizing the regulatory functions of proteolytic processing and domain shedding. In addition, we outline the importance of Pcdh intracellular domains in the regulation of downstream signaling cascades, and we describe putative Pcdh interactions with intracellular molecules including components of the WAVE complex, the Wnt pathway, and apoptotic cascades. Our overview combines molecular interaction data from different contexts, such as neural development and cancer. This comprehensive approach reveals potential common Pcdh signaling hubs, and points out future directions for research. Functional studies of such key factors within the context of neural development might yield innovative insights into the molecular etiology of Pcdh-related neurodevelopmental disorders.