Biology subjects

Scholz, S. A.

Publications and source records attributed to Scholz, S. A..

3 recordsLinked to original sources

bioRxiv2026

Chemically tunable permeability of engineered alpha-Hemolysin in synthetic cells

Controlling molecular transport across membranes is a defining feature of living cells, yet replicating this functionality in synthetic systems remains a major challenge. Self-inserting protein nanopores, such as -hemolysin (HL), offer a promising route towards programmable membrane permeability due to their robust assembly, compatibility with diverse membrane systems, and intrinsic permeability for diverse biomolecules. Here, we explored the use of chemically functionalized HL nanopores as tunable transport modules. To quantify translocation of peptide substrates across HL-containing membranes, we developed a high-throughput luminescence-based breakage-controlled assay using large unilamellar vesicles. With this assay we introduce a one-pot nanopore modification and strategy, compatible with scalable workflows. Electrophysiology and molecular simulations demonstrate that the introduction of cysteine residues at defined pore locations, combined with targeted chemical modification, enables controlled tuning of HL-nanopore selectivity based on peptide structure and charge. Together, these findings position engineered protein nanopores as versatile and responsive components for controlling membrane transport in synthetic biology.

synthetic biology↗

bioRxiv2025

Functional Mapping and Engineering of the Sec Translocon Unlocked by a Cell-Free System

Almost all proteins are inserted or translocated across membranes by the universally conserved Sec translocon. Despite its central role, experimental access to Sec function has remained limited. Here, we present a cell-free protein synthesis platform that inserts SecYEG into synthetic vesicles, enabling direct testing of Sec in real-time and high-throughput, circumventing longstanding viability constraints. Screening 300 Sec variants in a single experiment, we consolidate three decades of Sec research, while vastly expanding mutant diversity for structure-function insights. Mapping over 30 functionally critical regions that modulate Sec activity across three orders of magnitude, we uncover dozens of super-active variants. We further leverage our system to increase membrane protein quality and nanobody export, highlighting the potential of our system for advancing applications in synthetic biology and biotechnology.

molecular biology↗

bioRxiv2021

A versatile active learning workflow for optimization of genetic and metabolic networks

The study, engineering and application of biological networks require practical and efficient approaches. Current optimization efforts of these systems are often limited by wet lab labor and cost, as well as the lack of convenient, easily adoptable computational tools. Aimed at democratization and standardization, we describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets. We demonstrate our workflow for various applications, from simple to complex gene circuits and metabolic networks, including several cell-free transcription and translation systems, a LacI-based multi-level controller and a 27-variable synthetic CO2-fixation cycle (CETCH cycle). Using METIS, we could improve above systems between one and two orders of magnitude compared to their original setup with minimal experimental efforts. For the CETCH cycle, we explored the combinatorial space of [~]1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system. This allows to identify so far unknown interactions and bottlenecks in complex systems, which paves the way for their hypothesis-driven improvement, which we demonstrate for the LacI multi-level controller that we were able to improve by 34-fold after having identified resource competition as limiting factor. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

synthetic biology↗