Search bioRxiv⌕ Search

bioRxiv · 10.1101/2023.10.19.563051

FAKHRAVAC and BBIBP-CorV vaccine seeds' binding to angiotensin-converting enzyme 2: A comparative molecular dynamics study

Abstract

BackgroundSafety and efficacy of the SARS-CoV-2 inactivated vaccines have been question since the emergence of SARS-CoV-2 variants of concern (VOCs). Using residue fluctuations and statistically comparing RMSF values, have escalated the understanding of the binding dynamics of the viral proteins to their receptors and here in this study, we compared the interaction between inactivated spike proteins (representing FAKHRAVAC and BBIBP-CorV vaccines seed) and the human Angiotensin-Converting Enzyme 2 (hACE2) receptor. MethodologyThrough 100 set of accelerated 1 ns comparative molecular dynamics simulations, we analyze the binding dynamics and energy components of these interactions and compared residue backbone fluctuations using entropy and statistics including KL-Divergence and KS-test. Principal FindingsOur results reveal that FAKHRAVAC and Sinopharm exhibit similar binding dynamics and affinity to hACE2. Further examination of residue-wise fluctuations highlights the common behavior of binding key residues and mutation sites between the two vaccines. However, subtle differences in residue fluctuations, especially at critical sites like Q24, Y435, L455, S477, Y505, and F486, raise the possibility of distinct efficacy profiles. ConclusionThese variations may influence vaccine immunogenicity and safety in response to evolving SARS-CoV-2 variants. The study underscores the importance of considering residue-wise fluctuations for understanding vaccine-pathogen interactions and their implications for vaccine design. Author summaryIt is fundamentally important to ensure the safety and efficacy of the FAKHRAVAC, as an inactivated vaccine candidate for SARS-CoV-2. Considering the previously published pre-clinical and clinical findings about the similarity of the FAKHRAVACs safety and efficacy in comparison to the BBIBP-CorV vaccine seed (which is recalled as Sinopharm), it is necessary to gain more insights into structure and function of this vaccine at the molecular level, as well. Since the binding dynamics of the viral proteins to their receptor can imply the vaccines immunogenicity and mechanism-of-action, binding dynamics of a vaccine candidate must be studied comprehensively. Hereby, we have compared binding dynamics of the FAKHRAVAC and Sinopharm vaccine seeds to the SARS-CoV-2 spike proteins receptor, the ACE2. We took advantage of a comparative molecular dynamics simulation approach to effectively compare binding dynamics using atom fluctuations and at the residue level to ensure the resolution of this study. We have found similar binding dynamics and binding mechanics between these two vaccines, validating the pre-clinical and clinical findings computationally, as well as highlighting residues with different fluctuations and discussed their potential roles.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Setareh, S., Rad, I., Meghdadi, J., Khodayari, K., Karimi Rahjerdi, A.. 2023-10-20. FAKHRAVAC and BBIBP-CorV vaccine seeds' binding to angiotensin-converting enzyme 2: A comparative molecular dynamics study. https://doi.org/10.1101/2023.10.19.563051

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

KEEP EXPLORING

Related preprints

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↗

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↗

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↗