Biology subjects

Frohn, B. P.

Publications and source records attributed to Frohn, B. P..

4 recordsLinked to original sources

bioRxiv2026

Reframing enzyme function prediction as conditional generation

Enzymes frequently exhibit promiscuous activity beyond their native roles, providing starting-points for new functions. Finding these promiscuous enzymes, especially for non-native chemical transformations, is challenging but highly valuable, as they promise novel, sustainable solutions for chemistry and biotechnology. However, current machine learning methods are poorly suited to discovering unseen chemistry as they often frame function prediction as closed set classification or a retrieval task. Here, we present Fluxion, a generative deep learning framework that learns enzymatic catalysis by modeling dynamic electron flow trajectories across the enzyme's catalytic residues. By combining both synthetic chemistry and biochemical datasets with protein language model representations, Fluxion generates multi-step electron-flow trajectories analogous to the arrow-pushing representations used to describe enzyme reaction mechanisms. Generation is conditioned on enzyme context, including the enzyme sequence, catalytic residues, substrates, and cofactors. We show that this conditioning allows Fluxion to learn enzyme-dependent regioselectivity across cytochrome P450 enzymes with different sequences shifting the predicted reaction sites for the same substrate. We then demonstrate that Fluxion's embeddings are useful for downstream tasks, such as specificity prediction on two experimental datasets, with and without finetuning. Finally, we show that Fluxion has the potential to transfer synthetic chemical logic to biology; it can generate the observed non-native product from real-world non-native directed evolution screens. Our results establish a proof of concept that generative modeling through mechanistic representations of enzymes can shift enzyme function prediction beyond static database retrieval and closed set classification to function generation. This conceptual framework provides a stepping stone towards an in silico generative method to discover non-native biocatalysts.

biochemistry↗

bioRxiv2026

Automated Synthetic Cell-based Screening for Designed Proteins with Emergent Functions

Designing minimal biological systems with emergent functions such as spatiotemporal self-organization is a central goal of bottom-up synthetic biology. While computational optimization and design show promises to accelerate functional protein engineering through Design-Build-Test-Learn cycles, screening libraries for complex functions remains a major challenge. Conventional screens typically lack the spatiotemporal resolution and cell-like confinement required in bottom-up synthetic biology. Here, we present PUREdrop, an automated microfluidic platform that encapsulates and expresses protein libraries in thousands of picolitre-sized synthetic cells per construct. These droplets are sorted into a 96-well plate and analyzed by time-lapse imaging, allowing parallel quantification of expression kinetics and emergent functions. To demonstrate the platforms potential, we first screened computationally re-designed variants of the bacterial cell division protein FtsZ, identifying variants with improved bundling phenotypes and faster kinetics. We then extended our screening procedure towards general protein modulators of FtsZ and identified a combination that anchors filaments to the interface, producing a ring-like phenotype. PUREdrop bridges computational protein engineering and synthetic cell research, elevating the rational engineering of complex biological function to the next level.

synthetic biology↗

bioRxiv2023

Designing a protein with emergent function by combined in silico, in vitro and in vivo screening

Recently, utilization of machine learning (ML) based methods has led to astonishing progress in protein design and, thus, the design of new biological functionality. However, emergent functions that require higher-order molecular interactions, such as the ability to self-organize, are still extremely challenging to implement. Here, we describe a comprehensive in silico, in vitro, and in vitro screening pipeline (i3-screening) to develop and validate ML-designed artificial homologs of a bacterial protein that confers its role in cell division through the emergent function of spatiotemporal pattern formation. Moreover, we present complete substitution of a wildtype gene by an ML-designed artificial homolog in Escherichia coli. These results raise great hopes for the next level of synthetic biology, where ML-designed synthetic proteins will be used to engineer cellular functions.

synthetic biology↗

bioRxiv2021

Protein domain patterns reveal a functional diversity of the archaeal Cdv system and give insights into the origin of the eukaryotic ESCRT system

The Endosomal Sorting Complex Required for Transport (ESCRT) system is a multi-protein machinery that is involved in cell division of both Eukaryotes and Archaea. This spread across super-phyla suggests that a precursor ESCRT machinery existed already at an evolutionary early stage of life, making it a promising candidate for the (re)construction of a minimal cell division machinery. There are, however, only few experimental data about ESCRT machineries in Archaea, due to high technical challenges in cultivation and microscopy. Here, we analyse the proteins of archaeal ESCRT machineries bioinformatically on a domain level, to enable mechanistical comparison without such challenging experiments. First, we infer that there are at least three different cell division mechanisms utilizing ESCRT proteins in archaea, probably similar in their constriction mechanisms but different in membrane tethering. Second, we show that ESCRT proteins in Asgard archaea are highly similar to eukaryotic ESCRT proteins, strengthening the recently developed idea that all Eukaryotes descended from archaea. Third, we reconstruct a plausible evolutionary development of ESCRT machineries and suggest that a simple ESCRT-based constriction machinery existed in the last archaeal common ancestor. These findings not only give very interesting insights into the evolution of cell division in Archaea and Eukaryotes, but also reveal new research avenues by suggesting hypothesis-driven experiments for both, cell biology and bottom-up synthetic biology.

molecular biology↗