Biology subjects

Eschbach, E.

Publications and source records attributed to Eschbach, E..

4 recordsLinked to original sources

bioRxiv2026

Dual-graph deconvolution of enzyme fitness: an interpretable framework to identify and explain catalytic modulators

Models trained on protein sequences can predict mutational effects, but their outputs represent a convolution of different phenotypes. Isolating stability-independent activity effects is essential for rational enzyme design, allosteric drug design and functional annotation, yet existing pLM-based deconvolution methods lack interpretability. Here, we introduce a hybrid framework that pairs stability predictors with probabilistic graphical models of coevolution. Benchmarked on all enzymes with available multidimensional deep mutational scanning data, our framework isolates stability-independent activity effects with a fidelity that matches analogous pLM-based approaches. Crucially, we reveal biophysically plausible communication pathways that align with previous knowledge and that we validate with experimental data across a diverse set of targets. This hybrid approach merges statistical and geometric deep learning into a zero-shot, explainable tool for targeted engineering and mechanistic discovery.

bioinformatics↗

bioRxiv2026

evedesign: accessible biosequence design with a unified framework

Machine learning methods for protein engineering are rarely interoperable, require bespoke workflows, and remain inaccessible to non-experts. Yet the design problems that matter most - conditional design subject to real-world constraints, multi-objective optimization, and iterative lab-in-the-loop workflows where experimental data continuously refines successive design rounds - demand exactly the kind of flexible, composable infrastructure that no single tool provides. We present evedesign, a unified open-source framework that formalizes conditional biosequence design in a method-agnostic way, enabling complex multiobjective workflows combining supervised and unsupervised models from standardized specifications, and built from the outset to support iterative experimental integration. An interactive web interface facilitates end-to-end design for a broad scientific audience at https://evedesign.bio. We demonstrate evedesigns utility in antibody engineering, enzyme design, and natural enzyme discovery, and invite open-source community contributions.

bioinformatics↗

bioRxiv2025

Predicting peptide aggregation with protein language model embeddings

Amyloid fibrils, a form of peptide aggregate, are associated with multiple diseases and hinder the development of therapeutics. The experimental characterization of aggregating peptides is resource-intensive and data are scarce, limiting the development of accurate models. We present a deep-learning model, PALM (Predicting Aggregation with Language Model embeddings), which uses transfer learning to predict aggregation from embeddings extracted from a pretrained protein language model (pLM). PALM is trained on the WaltzDB-2.0 dataset to classify peptides and identify aggregation-prone regions within a sequence at single-residue resolution. Compared to existing models, it exhibits competitive performance on diverse held-out experimental datasets. We find that PALM fails to identify single mutations that increase the rate of aggregation of amyloid beta peptide; however, training the PALM architecture on a larger dataset, CANYA NNK1-3, substantially improves performance in this task. These results show that transfer learning with pLM embeddings improves performance when training on small datasets, but highlight that challenging tasks, such as predicting the effect of single mutations, require more experimental data.

bioinformatics↗

bioRxiv2019

Intraspecific population admixture of a top piscivore correlates with anthropogenic alteration of freshwater ecosystems

Conservation of local genetic diversity is an important policy objective, but intraspecific genetic diversity can be transformed by natural ecological processes associated with anthropogenic changes in ecosystems. Environmental changes and a strong interconnection of drainage systems impact freshwater biodiversity from gene to population level. Populations can either become extinct or expand their range and accompanying secondary contacts can lead to genetic admixture. We investigated how the genetic population structure and the patterns of genetic admixture of Esox lucius L. (the northern pike) vary with the type of ecosystem and the integrity of the ecosystem assessed by measures under the European Water Framework Directive. The pike inhabits river, lake and brackish water ecosystems, where it is confronted with different ecological disturbances. We analysed 1,384 pike samples from the North, Baltic and Black Sea drainages and differentiated between metapopulations from each hydrogeographic region using genotypes from 15 microsatellites and mitochondrial cyt b sequences. Individual populations showed signs of genetic admixture ranging from almost zero to complete replacement by foreign genotypes. Hierarchical general linear modeling revealed a highly significant positive association of the degree of genetic admixture with decreasing ecological status. This may mean that populations in disturbed environments are more prone to influences by foreign genotypes or, alternatively, increased genetic admixture may indicate adaptation to rapid environmental changes. Regardless of the underlying mechanisms, our results suggest that anthropogenic alterations of natural freshwater ecosystems can influence genetic structures, which may lead to a large-scale reduction of intraspecific genetic diversity.

evolutionary biology↗