Biology subjects

von Bachmann, A.

Publications and source records attributed to von Bachmann, A..

3 recordsLinked to original sources

bioRxiv2025

Modular Engineering of Thermo-Responsive Allosteric Proteins

Thermogenetics enables non-invasive spatiotemporal control over protein activity in living cells and tissues, yet its applications have largely been restricted to transcriptional regulation and membrane recruitment. Here, we present a generalizable strategy for engineering thermosensitive allosteric proteins through the insertion of optimized Avena sativa LOV2 domain variants. Applying this approach to a diverse set of structurally and functionally unrelated proteins in Escherichia coli, we generated potent, thermo-switchable chimeric variants that can be tightly controlled within narrow temperature ranges (37-41{degrees}C). Extending this strategy to mammalian systems, we engineered the first CRISPR-Cas genome editors directly modulated by subtle temperature changes within the physiological range. Finally, we showcase the incorporation of a chemoreceptor domain as an alternative thermosensing module, suggesting thermo-sensitivity to be a widespread feature in receptor domains. This work expands the toolkit of thermogenetics, providing a blueprint for temperature-dependent control of virtually any protein of interest.

synthetic biology↗

bioRxiv2024

Rational engineering of allosteric protein switches by in silico prediction of domain insertion sites

Domain insertion engineering is a powerful approach to juxtapose otherwise separate biological functions, resulting in proteins with new-to-nature activities. A prominent example are switchable protein variants, created by receptor domain insertion into effector proteins. Identifying suitable, allosteric sites for domain insertion, however, typically requires extensive screening and optimization. We present ProDomino, a novel machine learning pipeline to rationalize domain recombination, trained on a semi-synthetic protein sequence dataset derived from naturally occurring intradomain insertion events. ProDomino robustly identifies domain insertion sites in proteins of biotechnological relevance, which we experimentally validated in E. coli and human cells. Finally, we employed light- and chemically regulated receptor domains as inserts and demonstrate the rapid, model-guided creation of potent, single-component opto- and chemogenetic protein switches. These include novel CRISPR-Cas9 and -Cas12a variants for inducible genome engineering in human cells. Our work enables one-shot domain insertion engineering and substantially accelerates the design of customized allosteric proteins.

synthetic biology↗

bioRxiv2024

Prediction of context-specific regulatory programs and pathways using interpretable deep learning

Variational autoencoders (VAEs) are being widely adopted for the analysis of single-cell RNA sequencing (scRNA-seq) data. As with any non-linear models, however, they lack interpretability, which is a crucial aspect in the biomedical field where researchers want to be able to trust their model predictions. Our previously developed OntoVAE model addressed this issue by integrating biological ontologies in the decoder, which made the neuronal activations correspond to pathway activities. However, when multiple covariates are present, disentangling their relative contributions is challenging. To address this limitation, we developed COBRA, a VAE tool that combines the interpretable decoder part of OntoVAE with an adversarial approach that separates covariate effects in the latent space. In this work, we demonstrate the use of COBRA on two different scRNA-seq datasets in different contexts. We applied the tool to an interferon stimulated mouse dataset to separate the effects of celltype and treatment on transcription factors and biological pathways. We furthermore showed how COBRA can be used to predict the state of unseen celltypes.

bioinformatics↗