iFLinkC-X: A Scalable Framework to Assemble Bespoke Genetically Encoded Co-polymeric Linkers of Variable Lengths and Amino Acid Composition

Sequence determinants of protein phase separation and recognition by protein phase-separated condensates through molecular dynamics and active learning

Elucidating how protein sequence determines the properties of disordered proteins and their phase-separated condensates is a great challenge in computational chemistry, biology, and biophysics. Quantitative molecular dynamics simulations and derived free energy values can in principle capture how a sequence encodes the chemical and biological properties of a protein. These calculations are, however, computationally demanding, even after reducing the representation by coarse-graining; exploring the large spaces of potentially relevant sequences remains a formidable task. We employ an "active learning" scheme introduced by Yang et al. (bioRxiv, 2022, https://doi.org/10.1101/2022.08.05.502972) to reduce the number of labelled examples needed from simulations, where a neural network-based model suggests the most useful examples for the next training cycle. Applying this Bayesian optimisation framework, we determine properties of protein sequences with coarse-grained molecular dynamics, which enables the network to establish sequence-property relationships for disordered proteins and their self-interactions and their interactions in phase-separated condensates. We show how iterative training with second virial coefficients derived from the simulations of disordered protein sequences leads to a rapid improvement in predicting peptide self-interactions. We employ this Bayesian approach to efficiently search for new sequences that bind to condensates of the disordered C-terminal domain (CTD) of RNA Polymerase II, by simulating molecular recognition of peptides to phase-separated condensates in coarse-grained molecular dynamics. By searching for protein sequences which prefer to self-interact rather than interact with another protein sequence we are able to shape the morphology of protein condensates and design multiphasic protein condensates.

Direct and Ultrasensitive Bioluminescent Detection of Intact Respiratory Viruses

Respiratory viruses such as SARS-CoV-2, influenza, and respiratory syncytial virus (RSV) represent pressing health risks. Rapid diagnostic tests for these viruses detect single antigens or nucleic acids, which do not necessarily correlate with the amount of the intact virus. Instead, specific detection of intact respiratory virus particles may be more effective at assessing the contagiousness of a patient. Here, we report GLOVID, a modular biosensor platform to detect intact virions against a background of "free" viral proteins in solution. Our approach harnesses the multivalent display of distinct proteins on the surface of a viral particle to template the reconstitution of a split luciferase, allowing specific, single-step detection of intact influenza A and RSV virions corresponding to 0.1-0.3 fM of genomic units. The protein ligation system used to assemble GLOVID sensors is compatible with a broad range of binding domains, including nanobodies, scFv fragments, and cyclic peptides, which allows straightforward adjustment of the sensor platform to target different viruses.

Bioluminescent detection of viral surface proteins using branched multivalent protein switches

Fast and reliable virus diagnostics is key to prevent the spread of viruses in populations. A hallmark of viruses is the presence of multivalent surface proteins, a property that can be harnessed to control conformational switching in sensor proteins. Here, we introduce a new sensor platform (dark-LUX) for the detection of viral surface proteins consisting of a general bioluminescent framework that can be post-translationally functionalized with separately expressed binding domains. The platform relies on (1) plug-and-play bioconjugation of different binding proteins via SpyTag/SpyCatcher technology to create branched protein structures, (2) an optimized turn-on bioluminescent switch based on complementation of the split-luciferase NanoBiT upon target binding and (3) straightforward exploration of the protein linker space. The influenza A virus (IAV) surface proteins hemagglutinin (HA) and neuraminidase (NA) were used as relevant multivalent targets to establish proof of principle and optimize relevant parameters such as linker properties, choice of target binding domains and the optimal combination of the competing NanoBiT components SmBiT and DarkBiT. The sensor framework allows rapid conjugation and exchange of various binding domains including scFvs, nanobodies and de novo designed binders for a variety of targets, including the construction of a heterobivalent switch that targets the head and stem region of hemagglutinin. The modularity of the platform thus allows straightforward optimization of binding domains and scaffold properties for existing viral targets, and is well suited to quickly adapt bioluminescent sensor proteins to effectively detect newly evolving viral epitopes.

Dissecting the Permeability of the Escherichia coli Cell Envelope to a Small Molecule Using Tailored Intensiometric Fluorescent Protein Sensors

Membranes provide a highly selective barrier that defines the boundaries of any cell while providing an interface for communication and nutrient uptake. However, despite their central physiological role, our capacity to study or even engineer the permeation of distinct solutes across biological membranes remains rudimentary. This especially applies to Gram-negative bacteria, where the outer and inner membrane impose two permeation barriers. Addressing this analytical challenge, we exemplify how the permeability of the Escherichia coli cell envelope can be dissected using a small-molecule-responsive fluorescent protein sensor. The approach is exemplified for the biotechnologically relevant macrolide rapamycin, for which we first construct an intensiometric rapamycin detector (iRapTor) while comprehensively probing key design principles in the iRapTor scaffold. Specifically, this includes the scope of minimal copolymeric linkers as a function of topology and the concomitant need for gate post residues. In a subsequent step, we apply iRapTors to assess the permeability of the E. coli cell envelope to rapamycin. Despite its lipophilic character, rapamycin does not readily diffuse across the E. coli envelope but can be enhanced by recombinantly expressing a nanopore in the outer membrane. Our study thus provides a blueprint for studying and actuating the permeation of small molecules across the prokaryotic cell envelope.