Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.04.19.537534

Impact of water models on structure and dynamics of ligand-transport tunnels in enzymes derived from molecular dynamics simulations

Agrawal, N.·Brezovsky, J.
View original source ↗Export metadata ↓

Abstract

Protein hydration plays a vital role in many biological functions and molecular simulations are frequently used to study the effect of protein hydration at the atomic level. However, the accuracy of these simulations has often been highly sensitive to the water model used, a phenomenon best known in the case of intrinsically disordered proteins. In the present study, we investigated the extent to which the choice of water model alters the behavior of complex networks of transport tunnels. Tunnels are essential because they allow substrates and products to access and exit the active sites of enzymes that are otherwise deeply embedded within the protein structure. The ability of these tunnels to regulate access directly affects enzyme efficiency and selectivity, making their study crucial for understanding enzyme function and inhibition at a mechanistic level. By performing all-atom molecular dynamics simulations of the wild-type haloalkane dehalogenase LinBWT and its two variants, LinB32 and LinB86, with synthetically engineered tunnel networks in TIP3P and OPC water models, we investigated the effects of these models on the overall tunnel topology. We also analyzed the properties of the main tunnels, such as their conformation, bottleneck dimensions, sampling efficiency, and duration of the tunnel opening. Our data demonstrate that all three proteins exhibited similar conformational behavior in both water models and differed in the geometrical characteristics of their auxiliary tunnels, in line with experimental observations. Interestingly, the results indicate that the stability of the open tunnels is sensitive to the water model and the system under question. Our findings suggest that the 3-point TIP3P model can provide comparable inference on the overall topology of the networks of primary tunnels and their geometry, and thus may be a desirable choice when computational resources are limited or when compatibility issues impede usage of OPC with certain protein force fields. However, when a more thorough investigation is performed, such as the calculation of ligand unbinding rates via such tunnel networks, where precision and intricate details are paramount, the more costly 4-point OPC model would be more suited. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/537534v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1d9a56eorg.highwire.dtl.DTLVardef@1fbe3f7org.highwire.dtl.DTLVardef@336f2corg.highwire.dtl.DTLVardef@c7b8fc_HPS_FORMAT_FIGEXP M_FIG C_FIG

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Agrawal, N., Brezovsky, J.. 2023-04-21. Impact of water models on structure and dynamics of ligand-transport tunnels in enzymes derived from molecular dynamics simulations. https://doi.org/10.1101/2023.04.19.537534

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↗