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Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings. Differential sensing aims to mimic senses such as taste and smell through the use of synthetic receptors. Here, the authors show that arrays of de novo designed peptide assemblies can be used as sensor components to distinguish various analytes and complex mixtures.

In coiled-coil (CC) protein structures α-helices wrap around one another to form rope-like assemblies. Most natural and designed CCs have two–four helices and cyclic (C n ) or dihedral (D n ) symmetry. Increasingly, CCs with five or more helices are being reported. A subset of these higher-order CCs is of interest as they have accessible central channels that can be functionalised; they are α-helical barrels. These extended cavities are surprising given the drive to maximise buried hydrophobic surfaces during protein folding and assembly in water. Here, we show that α-helical barrels can be maintained by the strategic placement of β-branched aliphatic residues lining the lumen. Otherwise, the structures collapse or adjust to give more-complex multi-helix assemblies without C n or D n symmetry. Nonetheless, the structural hallmark of CCs—namely, knobs-into-holes packing of side chains between helices—is maintained leading to classes of CCs hitherto unobserved in nature or accessed by design. Higher order coiled coils with five or more helices can form α-helical barrels. Here the authors show that placing β-branched aliphatic residues along the lumen yields stable and open α-helical barrels, which is of interest for the rational design of functional proteins; whereas, the absence of β-branched side chains leads to unusual low-symmetry α-helical bundles.

The LINC complex, consisting of interacting SUN and KASH proteins, mechanically couples nuclear contents to the cytoskeleton. In meiosis, the LINC complex transmits microtubule-generated forces to chromosome ends, driving the rapid chromosome movements that are necessary for synapsis and crossing over. In somatic cells, it defines nuclear shape and positioning, and has a number of specialised roles, including hearing. Here, we report the X-ray crystal structure of a coiled-coiled domain of SUN1’s luminal region, providing an architectural foundation for how SUN1 traverses the nuclear lumen, from the inner nuclear membrane to its interaction with KASH proteins at the outer nuclear membrane. In combination with light and X-ray scattering, molecular dynamics and structure-directed modelling, we present a model of SUN1’s entire luminal region. This model highlights inherent flexibility between structured domains, and raises the possibility that domain-swap interactions may establish a LINC complex network for the coordinated transmission of cytoskeletal forces.

The design of protein sequences that fold into prescribed de novo structures is challenging. General solutions to this problem require geometric descriptions of protein folds and methods to fit sequences to these. The α-helical coiled coils present a promising class of protein for this and offer considerable scope for exploring hitherto unseen structures. For α-helical barrels, which have more than four helices and accessible central channels, many of the possible structures remain unobserved. Here, we combine geometrical considerations, knowledge-based scoring, and atomistic modeling to facilitate the design of new channel-containing α-helical barrels. X-ray crystal structures of the resulting designs match predicted in silico models. Furthermore, the observed channels are chemically defined and have diameters related to oligomer state, which present routes to design protein function.

Periplasmic binding proteins (PBPs) are bacterial proteins commonly used as scaffolds for substrate-detecting biosensors. In these biosensors, effector proteins (for example fluorescent proteins) are inserted into a PBP such that the effector protein’s output changes upon PBP-substate binding. The insertion site is often determined by comparison of PBP apo / holo crystal structures, but random insertion libraries have shown that this can miss the best sites. Here, we present a PBP biosensor design method based on residue contact analysis from molecular dynamics. This computational method identifies the best previously known insertion sites in the maltose binding PBP, and suggests further previously unknown sites. We experimentally characterise fluorescent protein insertions at these new sites, finding they too give functional biosensors. Furthermore, our method is sufficiently flexible to both suggest insertion sites compatible with a variety of effector proteins, and be applied to binding proteins beyond PBPs.

Molecular dynamics (MD) simulations can be used by protein scientists to investigate a wide array of biologically relevant properties such as the effects of mutations on a protein's structure and activity, or probing intermolecular interactions with small molecule substrates or other macromolecules. Within the world of computational structural biology, several programs have become popular for running these simulations, but each of these programs requires a significant time investment from the researcher to run even simple simulations. Even after learning how to run and analyse simulations, many elements remain a \"black box\". This greatly limits the accessibility of molecular dynamics simulations for non-experts. Here we present drMD, an automated pipeline for running molecular dynamics simulations using the OpenMM molecular mechanics toolkit. We have created drMD with non-experts in computational biology in mind. The drMD codebase has several functions that automatically handle routine procedures associated with running molecular dynamics simulations. This greatly reduces the expertise required to run MD simulations. We have also introduced a series of quality-of-life features to make the process of running MD simulations both easier and more pleasant. Finally, drMD explains the steps it is taking interactively and, where useful, provides relevant references so the user can learn more. All these features make drMD an effective tool for learning molecular dynamics while running publication-quality simulations. drMD is open source and can be found on GitHub: https://github.com/wells-wood-research/drMD.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/wells-wood-research/drMD

Periplasmic binding proteins (PBPs) are bacterial proteins commonly used as scaffolds for substrate-detecting biosensors. In such biosensors, effector proteins, for example circularly permuted green fluorescent protein (cpGFP), are inserted into a PBP such that the effector protein's output changes upon PBP-substate binding. The insertion site is often determined by comparison of PBP apo/holo crystal structures, but random insertion libraries have shown that this can miss the best sites. Here, we present a PBP biosensor design method based on residue contact analysis from molecular dynamics. This computational method identifies the best previously known insertion sites in the maltose binding PBP, and suggests further previously unknown sites. We experimentally characterise cpGFP insertions at these new sites, finding they too give functional biosensors. Our method is sufficiently flexible to both suggest insertion sites compatible with a variety of effector proteins and be applied to binding proteins beyond PBPs.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Updated abstract and corrected references.* https://github.com/wells-wood-research/oshea-j-wood-c-pbp-design-2023

Understanding how proteins fold into well-defined three-dimensional structures has been a longstanding challenge. Increased understanding has led to increased success at designing proteins that mimic existing protein folds. This raises the possibility of custom design of proteins with structures not seen in nature. Thomson et al. describe the design of channelcontaining α-helical barrels, and Huang et al. designed hyperstable helical bundles. Both groups used rational and computational design to make new protein structures based on α-helical coiled coils but took different routes to reach different target structures. Science, this issue p. 485, p. 481 The design of protein sequences that fold into prescribed de novo structures is challenging. General solutions to this problem require geometric descriptions of protein folds and methods to fit sequences to these. The α-helical coiled coils present a promising class of protein for this and offer considerable scope for exploring hitherto unseen structures. For α-helical barrels, which have more than four helices and accessible central channels, many of the possible structures remain unobserved. Here, we combine geometrical considerations, knowledge-based scoring, and atomistic modeling to facilitate the design of new channel-containing α-helical barrels. X-ray crystal structures of the resulting designs match predicted in silico models. Furthermore, the observed channels are chemically defined and have diameters related to oligomer state, which present routes to design protein function.

The association of amphipathic α helices in water leads to α-helical-bundle protein structures. However, the driving force for this--the hydrophobic effect--is not specific and does not define the number or the orientation of helices in the associated state. Rather, this is achieved through deeper sequence-to-structure relationships, which are increasingly being discerned. For example, for one structurally extreme but nevertheless ubiquitous class of bundle--the α-helical coiled coils--relationships have been established that discriminate between all-parallel dimers, trimers and tetramers. Association states above this are known, as are antiparallel and mixed arrangements of the helices. However, these alternative states are less-well understood. Here, we describe a synthetic-peptide system that switches between parallel hexamers and various up-down-up-down tetramers in response to single-amino-acid changes and solution conditions. The main accessible states of each peptide variant are characterized fully in solution and, in most cases, to high-resolution X-ray crystal structures. Analysis and inspection of these structures helps rationalize the different states formed. This navigation of the structural landscape of α-helical coiled coils above the dimers and trimers that dominate in nature has allowed us to design rationally a well-defined and hyperstable antiparallel coiled-coil tetramer (apCC-Tet). This robust de novo protein provides another scaffold for further structural and functional designs in protein engineering and synthetic biology.

Deep learning has made remarkable progress in protein design, yet current protein representations remain largely black-box and scale poorly with protein length, leading to high computational costs. We propose a fragment-based protein representation that balances interpretability and efficiency. Using a curated set of 40 evolutionarily conserved fragments, we represent proteins as fragment sets or fragment graphs, significantly reducing dimensionality while preserving functional information. Here, we show that fragment-based representations capture significantly more information at much lower dimensions compared to traditional methods. On a dataset of 215 functionally diverse proteins, our approach outperforms traditional sequence- and structure-based methods in clustering by protein function at <=30% sequence identity. Additionally, fragment-based search achieves comparable accuracy while using 90% fewer tokens. It also runs ~68.7x faster than RMSD-based methods and ~1.64x faster than sequence-based methods, even when including fragment pre-processing overhead. Finally, we show that fragments can guide RFDiffusion backbone generation, with recovery rates higher than 40%. We propose fragment-based representations as a scalable and interpretable alternative for the next generation of protein design tools, spanning backbone and sequence design to functional searches in protein structure databases.