Using THz Spectroscopy, Evolutionary Network Analysis Methods, and MD Simulation to Map the Evolution of Allosteric Communication Pathways in c-Type Lysozymes

Enhanced Immune Response Against Echinococcus Granulosus Through a CTLA-4/B7 Affinity-Based Vaccine

Background: Echinococcosis is a zoonotic infectious disease that poses a significant threat to the health of individuals living in rural regions. While vaccination represents a potential strategy for disease prevention, there is currently no effective vaccine available for humans to prevent cystic echinococcosis (CE). This study aimed to design a novel multi-epitope vaccine (MEV) against Echinococcus granulosus for human use, employing immunoinformatics methods. Methods: We identified core epitopes from two key antigens, EgA31 and EgG1Y162, and integrated them into the immunoglobulin variable region of CTLA-4 (CTLA-4lgV) to create the CVE31-162 vaccine construct. The secondary and tertiary structures of the CVE31-162 were established using bioinformatics methods. The interaction between the CVE31-162 and B7 molecules was assessed through molecular dynamics simulations. Finally, both in vitro and in vivo experiments were conducted to validate the effectiveness of the CVE31-162 against the immunological effects of Echinococcus granulosus. Results: Bioinformatics analysis indicated that CVE31-162 exhibits favorable antigenicity, stability, and non-allergenicity. Furthermore, CVE31-162 demonstrated a stable three-dimensional structural model. Molecular docking (MD) and molecular dynamics simulations (MDS) revealed a strong binding affinity between CVE31-162 and B7 molecules. Immune simulation results suggested that the vaccine elicits robust humoral and cell-mediated immune responses. Both in vitro and in vivo experiments demonstrated that immunized mice exhibited significantly elevated levels of antigen-specific antibodies and enhanced lymphocyte proliferation compared to the control group. Conclusions:CVE31-162, which is based on the interaction between CTLA-4 and B7, represents a promising multi-epitope vaccine for Echinococcus granulosus.

Chemo-mechanical forces modulate the topology dynamics of mesoscale DNA assemblies

The intrinsic complexity of many mesoscale (10-100 nm) cellular machineries makes it challenging to elucidate their topological arrangement and transition dynamics. Here, we exploit DNA origami nanospring as a model system to demonstrate that tens of piconewton linear force can modulate higher-order conformation dynamics of mesoscale molecular assemblies. By switching between two chemical structures (i.e., duplex and tetraplex DNA) in the junctions of adjacent origami modules, the corresponding stretching or compressing chemo-mechanical stress reversibly flips the backbone orientations of the DNA nanosprings. Both coarse-grained molecular dynamics simulations and atomic force microscopy measurements reveal that such a backbone conformational switch does not alter the right-handed chirality of the nanospring helix. This result suggests that mesoscale helical handedness may be governed by the torque, rather than the achiral orientation, of nanospring backbones. It offers a topology-based caging/uncaging concept to present chemicals in response to environmental cues in solution.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Effects of Distal Mutations on the Structure, Dynamics and Catalysis of Human Monoacylglycerol Lipase

An understanding of how conformational dynamics modulates function and catalysis of human monoacylglycerol lipase (hMGL), an important pharmaceutical target, can facilitate the development of novel ligands with potential therapeutic value. Here, we report the discovery and characterization of an allosteric, regulatory hMGL site comprised of residues Trp-289 and Leu-232 that reside over 18 Å away from the catalytic triad. These residues were identified as critical mediators of long-range communication and as important contributors to the integrity of the hMGL structure. Nonconservative replacements of Trp-289 or Leu-232 triggered concerted motions of structurally distinct regions with a significant conformational shift toward inactive states and dramatic loss in catalytic efficiency of the enzyme. Using a multimethod approach, we show that the dynamically relevant Trp-289 and Leu-232 residues serve as communication hubs within an allosteric protein network that controls signal propagation to the active site, and thus, regulates active-inactive interconversion of hMGL. Our findings provide new insights into the mechanism of allosteric regulation of lipase activity, in general, and may provide alternative drug design possibilities.

Patterns of coevolving amino acids unveil structural and dynamical domains

Patterns of interacting amino acids are so preserved within protein families that the sole analysis of evolutionary comutations can identify pairs of contacting residues. It is also known that evolution conserves functional dynamics, i.e., the concerted motion or displacement of large protein regions or domains. Is it, therefore, possible to use a pure sequence-based analysis to identify these dynamical domains? To address this question, we introduce here a general coevolutionary coupling analysis strategy and apply it to a curated sequence database of hundreds of protein families. For most families, the sequence-based method partitions amino acids into a few clusters. When viewed in the context of the native structure, these clusters have the signature characteristics of viable protein domains: They are spatially separated but individually compact. They have a direct functional bearing too, as shown for various reference cases. We conclude that even large-scale structural and functionally related properties can be recovered from inference methods applied to evolutionary-related sequences. The method introduced here is available as a software package and web server (spectrus.sissa.it/spectrus-evo_webserver).

Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals.