Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.08.21.608916

Insights from Molecular Docking and Dynamics Simulations of P2RX7-????Syn Complex

singh, K.·Joshi, J.·Sharma, S.·Irungbam, K. D.·Kumar, A.·Saini, M.·Kumar, M.
View original source ↗Export metadata ↓

Abstract

Alpha-synucleinopathies, characterized by extracellular alpha-synuclein (Syn) accumulation and aggregation, have been linked to neurological disorders including Parkinsons disease (PD) and multiple system atrophy (MSA). Syn interacts with membrane proteins, phospholipids, and cholesterol, leading to membrane damage and inflammation. This interaction heightens the likelihood of Syn engaging with P2RX7, a non-selective cationic transmembrane receptor that is primarily overexpressed in immune and neural cells. Activation of P2RX7 by Syn is implicated in neuronal degeneration, potentially causing pore dilation and increased inflammation. However, the precise molecular mechanisms and binding sites for this interaction, as well as the conformational dynamics of P2RX7 in response to Syn, remain unclear. We attempted to elucidate the molecular mechanisms and binding sites for P2RX7-Syn interaction, by integrating the data curation, molecular docking, and molecular dynamics (MD) simulations coupled with structural analyses. We elucidated interactions between P2RX7 and the N-terminal domain (NTD) of Syn using cryo-EM structures of P2RX7 in both ATP-bound and unbound states and assessed how Syn influences P2RX7structural and functional dynamics. Initially, the analyses revealed that Syn interactomes are mainly involved in regulating mitochondrial homeostasis, while P2RX7 interactors are linked to receptor internalization and calcium transport. Molecular docking with six tools identified that Syn-NTD fragments preferentially bind to the proximal region of P2RX7 transmembrane domain. Microsecond all atom MD simulations in a POPS lipid bilayer showed significant atomic fluctuations, particularly in the head region, lower body, and large loop of P2RX7 cytoplasmic domain. Secondary structure analysis indicated unfolding in regions related to pore dilation and receptor desensitization. Further by contact-based and solvent accessibility analyses, along with protein structure network (PSN) studies, we identified crucial residues involved in Syn-P2RX7 interactions. This insight deepens our understanding of how Syn and P2RX7 interact, offering a detailed atomic view of the structural and functional changes that occur during these interactions. This understanding could advance our grasp of neurodegenerative diseases and be vital for devising future preventive and therapeutic strategies. Author summaryNeural homeostasis is crucial for neuron survival and function, with protein-protein interactions, particularly between Syn and P2RX7, playing a vital role. Accumulation of Syn can worsen neuronal damage through its interaction with P2RX7. Traditional biological experiments struggle with time-scale limitations, but advances in computational methods--like molecular dynamics (MD) simulations and protein structure network (PSN) analysis--allow detailed atomic-level study of these interactions. This study used these computational tools to explore the dynamics between Syn and P2RX7, aiming to uncover structural and functional changes relevant to protein interactions and neural damage. The findings contribute to understanding the dynamics of P2RX7-Syn interactions.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

singh, K., Joshi, J., Sharma, S., Irungbam, K. D., Kumar, A., Saini, M., Kumar, M.. 2024-08-21. Insights from Molecular Docking and Dynamics Simulations of P2RX7-????Syn Complex. https://doi.org/10.1101/2024.08.21.608916

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗