Search bioRxivSearch

bioRxiv · 10.1101/311191

Structural insights into the binding mechanism of Plasmodium falciparum exported Hsp40-Hsp70 chaperone pair

Abstract

Expression of heat shock proteins in Plasmodium falciparum (Pf) increases during febrile episodes to play key roles in several necessary cellular processes. PFA0660w-PfHsp70-x, an exported chaperone pair is known to co-localize to specialized intracellular structures termed J-dots, and has been implicated in trafficking of the major virulence factor, PfEMP1 (Plasmodium falciparum erythrocyte membrane protein 1) across the host cell. This article highlights for the first time detailed structural analysis of PFA0660w-PfHsp70-x chaperone pair to better understand their binding mechanism. Here, we have modeled reliable molecular structures for the complete conserved region of PFA0660w and PfHsp70-x. These structures were evaluated by different structure verification tools followed by molecular dynamics (MD) simulations. The model of PFA0660w was subjected to docking with PfHsp70-x using Haddock to reveal a number of residues crucial for their bipartite interaction, and also performed MD simulations on the complex. The peptide binding clefts of PFA0660w and its other Plasmodium species homologs were found to be bigger than their counterparts in higher eukaryotes like yeast, humans and C. parvum. Based on our results, we propose a model for PFA0660w-PfHsp70-x interaction and a mechanism of substrate binding, and compare it with its dimeric human counterparts. Owing to these striking structural differences between the host and parasite chaperones, such information on the essential Hsp40 and its partner Hsp70 may form the basis for rational drug design against fatal malaria.

Source connections

Explore related subjects

Keep this discovery

BibTeXRIS

Behl, A., Mishra, P. C.. 2018-04-30. Structural insights into the binding mechanism of Plasmodium falciparum exported Hsp40-Hsp70 chaperone pair. https://doi.org/10.1101/311191

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