Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.03.09.710665

DEX: a consensus-based amino acid exchangeability measure for improved codon substitution modelling

Abstract

Physicochemically similar amino acids undergo more frequent substitutions compared to dissimilar amino acid pairs. Despite their clear potential, amino acid similarity matrices remain underused for certain molecular evolution applications. One key potential application that is understudied is in quantifying the strength of natural selection based on amino acid substitution patterns. This is partially due to the high number of proposed amino acid distance measures and the lack of agreement on which are most accurate. In this study, we assessed the performance of 30 amino acid distance measures, including a new amino acid distance measure we developed based on recent deep mutational scanning data. We compared these measures across codon substitution models fit to alignments spanning Streptococcus, Drosophila, and mammalian lineages, as well as segregating variants across Escherichia coli strains and human genotypes. We further constructed consensus matrices from combinations of top-performing measures in this analysis using the DISTATIS approach and retested these matrices. Our results show that experimentally-derived measures, particularly our new measure, DMS-EX and the existing experimental exchangeability measure, best fit codon substitution patterns across diverse lineages. We found that a consensus measure based on these two approaches, which we named DEX, performed best overall. We also explored the value of asymmetric exchangeabilities in DMS-EX for predicting allele frequencies of replacement polymorphisms across diverse lineages, including when conditioned on buried vs. exposed sites. Overall, we provide a systematic comparison of the performance of existing measures. The amino acid distance measures we introduce constitute a substantial improvement for exploring novel methods for quantifying the strength of natural selection and for providing improved baselines for future benchmarking approaches. SignificanceProtein-coding genes have long been a focus for researchers studying the strength and direction of selection. By studying non-synonymous substitutions, those that change amino acids, it is possible to estimate the relative strength of selection. Despite widespread interest in such approaches, information on which amino acids are exchanged is underused in most molecular evolution applications. This is partly because many different measures exist for quantifying amino acid distances, particularly those based on physicochemical properties. A newer class of amino acid distance measures is derived from deep mutational scanning datasets, where virtually every possible substitution is tested for its impact on protein function. We characterised and compared 30 amino acid distance measures, including a novel measure based on deep mutational scanning data. We highlight differences in how well these measures fit real substitution data. Overall, we find that DEX, which is a consensus of our new measure and an existing experimental exchangeability measure, performed best in these models. This work will serve as the basis for future improved methods for inferring selection efficacy from protein-coding alignments.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Douglas, G. M., Bobay, L.-M.. 2026-03-12. DEX: a consensus-based amino acid exchangeability measure for improved codon substitution modelling. https://doi.org/10.64898/2026.03.09.710665

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↗