Engineering an artificial catch bond using mechanical anisotropy.

Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin E (DocG:CohE) adhesion complex found on human gut bacteria. Using AFM single-molecule force spectroscopy in combination with bioorthogonal click chemistry, we mechanically dissociate the complex using five precisely controlled anchor geometries. When tension is applied between residue #13 on CohE and the N-terminus of DocG, the complex behaves as a two-state catch bond, while in all other tested pulling geometries, including the native configuration, it behaves as a slip bond. We use a kinetic Monte Carlo model with experimentally derived parameters to simulate rupture force and lifetime distributions, achieving strong agreement with experiments. Single-molecule FRET measurements further demonstrate that the complex does not exhibit dual binding mode behavior at equilibrium but unbinds along multiple pathways under force. Together, these results show how mechanical anisotropy and anchor point selection can be used to engineer artificial catch bonds.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions.

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

Understanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing.

Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we present the development of enzyme proximity sequencing, a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We use enzyme proximity sequencing to analyze how 6399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase from Rhodotorula gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. Enzyme proximity sequencing can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.

Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis.

Natural pest and weed regulation are essential for agricultural production, but the spatial distribution of natural enemies within crop fields and its drivers are mostly unknown. Using 28 datasets comprising 1204 study sites across eight Western and Central European countries, we performed a quantitative synthesis of carabid richness, activity densities and functional traits in relation to field edges (i.e. distance functions). We show that distance functions of carabids strongly depend on carabid functional traits, crop type and, to a lesser extent, adjacent non-crop habitats. Richness of both carnivores and granivores, and activity densities of small and granivorous species decreased towards field interiors, whereas the densities of large species increased. We found strong distance decays in maize and vegetables whereas richness and densities remained more stable in cereals, oilseed crops and legumes. We conclude that carabid assemblages in agricultural landscapes are driven by the complex interplay of crop types, adjacent non-crop habitats and further landscape parameters with great potential for targeted agroecological management. In particular, our synthesis indicates that a higher edge-interior ratio can counter the distance decay of carabid richness per field and thus likely benefits natural pest and weed regulation, hence contributing to agricultural sustainability.

Iterative Machine Learning for Classification and Discovery of Single-Molecule Unfolding Trajectories from Force Spectroscopy Data.

We report the application of machine learning techniques to expedite classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression, and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) learning where a user classifies a small number of repeatable unfolding patterns encoded as images, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multidomain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways that were undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis and generate new insights in protein biophysics.

Directed evolution of Rhodotorula gracilisd-amino acid oxidase using single-cell hydrogel encapsulation and ultrahigh-throughput screening.

Engineering catalytic and biophysical properties of enzymes is an essential step en route to advanced biomedical and industrial applications. Here, we developed a high-throughput screening and directed evolution strategy relying on single-cell hydrogel encapsulation to enhance the performance of d-Amino acid oxidase from Rhodotorula gracilis (RgDAAOx), a candidate enzyme for cancer therapy. We used a cascade reaction between RgDAAOx variants surface displayed on yeast and horseradish peroxidase (HRP) in the bulk media to trigger enzyme-mediated crosslinking of phenol-bearing fluorescent alginate macromonomers, resulting in hydrogel formation around single yeast cells. The fluorescent hydrogel capsules served as an artificial phenotype and basis for pooled library screening by fluorescence activated cell sorting (FACS). We screened a RgDAAOx variant library containing ∼106 clones while lowering the d-Ala substrate concentration over three sorting rounds in order to isolate variants with low Km. After three rounds of FACS sorting and regrowth, we isolated and fully characterized four variants displayed on the yeast surface. We identified variants with a more than 5-fold lower Km than the parent sequence, with an apparent increase in substrate binding affinity. The mutations we identified were scattered across the RgDAAOx structure, demonstrating the difficulty in rationally predicting allosteric sites and highlighting the advantages of scalable library screening technologies for evolving catalytic enzymes.

Direct Comparison of Lysine versus Site-Specific Protein Surface Immobilization in Single-Molecule Mechanical Assays.

Single-molecule force spectroscopy (SMFS) is powerful for studying folding states and mechanical properties of proteins, however, it requires protein immobilization onto force-transducing probes such as cantilevers or microbeads. A common immobilization method relies on coupling lysine residues to carboxylated surfaces using 1-ethyl-3-(3-dimethyl-aminopropyl) carbodiimide and N-hydroxysuccinimide (EDC/NHS). Because proteins typically contain many lysine groups, this strategy results in a heterogeneous distribution of tether positions. Genetically encoded peptide tags (e.g., ybbR) provide alternative chemistries for achieving site-specific immobilization, but thus far a direct comparison of site-specific vs. lysine-based immobilization strategies to assess effects on the observed mechanical properties was lacking. Here, we compared lysine- vs. ybbR-based protein immobilization in SMFS assays using several model polyprotein systems. Our results show that lysine-based immobilization results in significant signal deterioration for monomeric streptavidin-biotin interactions, and loss of the ability to correctly classify unfolding pathways in a multipathway Cohesin-Dockerin system. We developed a mixed immobilization approach where a site-specifically tethered ligand was used to probe surface-bound proteins immobilized through lysine groups, and found partial recovery of specific signals. The mixed immobilization approach represents a viable alternative for mechanical assays on in vivo-derived samples or other proteins of interest where genetically encoded tags are not feasible.

Multiplexed on-yeast serological assay for immune escape screening of SARS-CoV-2 variants.

The emergence of the SARS-CoV-2 Omicron variant altered patient risk profiles and shifted the trajectory of the COVID-19 pandemic. Therefore, sensitive serological tests capable of analyzing patient IgG responses to multiple variants in parallel are highly desirable. Here, we present an adaptable serological test based on yeast surface display and serum biopanning that characterizes immune profiles against SARS-CoV-2 Wuhan (B lineage), Delta (B.1.617.2 lineage), and Omicron (B.1.1.529 lineage) receptor-binding domain (RBD) variants. We examined IgG titers from 30 serum samples from COVID-19-convalescent and vaccinated cohorts in Switzerland, and assessed the relative affinity of polyclonal serum IgG for RBD domains. We demonstrate that serum IgGs from patients recovered from severe COVID-19 between March-June 2021 bound tightly to both original Wuhan and Delta RBD variants, but failed to recognize Omicron RBDs, representing an affinity loss of >10- to 20-fold. Our yeast immunoassay is easily tailored, expandable and parallelized with newly emerging RBD variants.

Enzyme Cascade with Horseradish Peroxidase Readout for High-Throughput Screening and Engineering of Human Arginase-1.

We report an enzyme cascade with horseradish peroxidase-based readout for screening human arginase-1 (hArg1) activity. We combined the four enzymes hArg1, ornithine decarboxylase, putrescine oxidase, and horseradish peroxidase in a reaction cascade that generated colorimetric or fluorescent signals in response to hArg1 activity and used this cascade to assay wild-type and variant hArg1 sequences as soluble enzymes and displayed on the surface of Escherichia coli. We screened a curated 13-member hArg1 library covering mutations that modified the electrostatic environment surrounding catalytic residues D128 and H141, and identified the R21E variant with a 13% enhanced catalytic turnover rate compared to wild type. Our scalable one-pot single-step arginase assay with continuous kinetic readout is amenable to high-throughput screening and directed evolution of arginase libraries and testing drug candidates for arginase inhibition.

Titrating Avidity of Yeast-Displayed Proteins Using a Transcriptional Regulator.

Yeast surface display is a valuable tool for protein engineering and directed evolution; however, significant variability in the copy number (i.e., avidity) of displayed variants on the yeast cell wall complicates screening and selection campaigns. Here, we report an engineered titratable display platform that modulates the avidity of Aga2-fusion proteins on the yeast cell wall dependent on the concentration of the anhydrotetracycline (aTc) inducer. Our design is based on a genomic Aga1 gene copy and an episomal Aga2-fusion construct both under the control of an aTc-dependent transcriptional regulator that enables stoichiometric and titratable expression, secretion, and display of Aga2-fusion proteins. We demonstrate tunable display levels over 2-3 orders of magnitude for various model proteins, including glucose oxidase enzyme variants, mechanostable dockerin-binding domains, and anti-PDL1 affibody domains. By regulating the copy number of displayed proteins, we demonstrate the effects of titratable avidity levels on several specific phenotypic activities, including enzyme activity and cell adhesion to surfaces under shear flow. Finally, we show that titrating down the display level allows yeast-based binding affinity measurements to be performed in a regime that avoids ligand depletion effects while maintaining small sample volumes, avoiding a well-known artifact in yeast-based binding assays. The ability to titrate the multivalency of proteins on the yeast cell wall through simple inducer control will benefit protein engineering and directed evolution methodology relying on yeast display for broad classes of therapeutic and diagnostic proteins of interest.

Adhesive Virulence Factors of Staphylococcus aureus Resist Digestion by Coagulation Proteases Thrombin and Plasmin.

Staphylococcus aureus (S. aureus) is an invasive and life-threatening pathogen that has undergone extensive coevolution with its mammalian hosts. Its molecular adaptations include elaborate mechanisms for immune escape and hijacking of the coagulation and fibrinolytic pathways. These capabilities are enacted by virulence factors including microbial surface components recognizing adhesive matrix molecules (MSCRAMMs) and the plasminogen-activating enzyme staphylokinase (SAK). Despite the ability of S. aureus to modulate coagulation, until now the sensitivity of S. aureus virulence factors to digestion by proteases of the coagulation system was unknown. Here, we used protein engineering, biophysical assays, and mass spectrometry to study the susceptibility of S. aureus MSCRAMMs to proteolytic digestion by human thrombin, plasmin, and plasmin/SAK complexes. We found that MSCRAMMs were highly resistant to proteolysis, and that SAK binding to plasmin enhanced this resistance. We mapped thrombin, plasmin, and plasmin/SAK cleavage sites of nine MSCRAMMs and performed biophysical, bioinformatic, and stability analysis to understand structural and sequence features common to protease-susceptible sites. Overall, our study offers comprehensive digestion patterns of S. aureus MSCRAMMs by thrombin, plasmin, and plasmin/SAK complexes and paves the way for new studies into this resistance and virulence mechanism.

Optimal Sacrificial Domains in Mechanical Polyproteins: S. epidermidis Adhesins Are Tuned for Work Dissipation.

The opportunistic pathogen Staphylococcus epidermidis utilizes a multidomain surface adhesin protein to bind host components and adhere to tissues. While it is known that the interaction between the SdrG receptor and its fibrinopeptide target (FgB) is exceptionally mechanostable (∼2 nN), the influence of downstream B domains (B1 and B2) is unclear. Here, we studied the mechanical relationships between folded B domains and the SdrG receptor bound to FgB. We used protein engineering, single-molecule force spectroscopy (SMFS) with an atomic force microscope (AFM), and Monte Carlo simulations to understand how the mechanical properties of folded sacrificial domains, in general, can be optimally tuned to match the stability of a receptor-ligand complex. Analogous to macroscopic suspension systems, sacrificial shock absorber domains should neither be too weak nor too strong to optimally dissipate mechanical energy. We built artificial molecular shock absorber systems based on the nanobody (VHH) scaffold and studied the competition between domain unfolding and receptor unbinding. We quantitatively determined the optimal stability of shock absorbers that maximizes work dissipation on average for a given receptor and found that natural sacrificial domains from pathogenic S. epidermidis and Clostridium perfringens adhesins exhibit stabilities at or near this optimum within a specific range of loading rates. These findings demonstrate how tuning the stability of sacrificial domains in adhesive polyproteins can be used to maximize mechanical work dissipation and serve as an adhesion strategy by bacteria.

Engineered Molecular Therapeutics Targeting Fibrin and the Coagulation System: a Biophysical Perspective.

The coagulation cascade represents a sophisticated and highly choreographed series of molecular events taking place in the blood with important clinical implications. One key player in coagulation is fibrinogen, a highly abundant soluble blood protein that is processed by thrombin proteases at wound sites, triggering self-assembly of an insoluble protein hydrogel known as a fibrin clot. By forming the key protein component of blood clots, fibrin acts as a structural biomaterial with biophysical properties well suited to its role inhibiting fluid flow and maintaining hemostasis. Based on its clinical importance, fibrin is being investigated as a potentially valuable molecular target in the development of coagulation therapies. In this topical review, we summarize our current understanding of the coagulation cascade from a molecular, structural and biophysical perspective. We highlight single-molecule studies on proteins involved in blood coagulation and report on the current state of the art in directed evolution and molecular engineering of fibrin-targeted proteins and polymers for modulating coagulation. This biophysical overview will help acclimatize newcomers to the field and catalyze interdisciplinary work in biomolecular engineering toward the development of new therapies targeting fibrin and the coagulation system.

Correlating single-molecule rupture mechanics with cell population adhesion by yeast display.

Here, we present a method based on yeast surface display that allows for direct comparison between population-level cell adhesion strength and single-molecule receptor-ligand rupture mechanics. We developed a high-throughput yeast adhesion assay in which yeasts displaying monomeric streptavidin (mSA) or enhanced mutant mSA were adhered to a biotinylated coverglass submerged in fluid. After exposure to shear stress (20-1000 dyn/cm2) by rapid spinning of the coverglass, cells were imaged to quantify the midpoint detachment shear stress for the cell population. We then performed atomic force microscope single-molecule force spectroscopy (SMFS) on purified mSA variants and identified correlations between single-molecule rupture force distributions and cell population adhesion strength. Several features of yeast display were important for successful correlations of adhesion strength to be drawn, including covalent attachment of the receptor to the cell wall, a precisely defined molecular pulling geometry, repression of nonspecific adhesion, and control for multivalency. With these factors properly taken into account, we show that spinning disk cell adhesion assays can be correlated with SMFS and are capable of screening the mechanical strength of receptor-ligand complexes. These workflow enhancements will accelerate research on mechanostable receptor-ligand complexes and receptor-mediated cell adhesion.

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and in vitro evolution campaigns include improved folding stability, catalytic activity, and/or substrate specificity. Despite significant progress in recent years in the areas of high-throughput screening and DNA sequencing, our ability to explore the vast space of functional enzyme sequences remains severely limited. Here, we review the currently available suite of modern methods for enzyme engineering, with a focus on novel readout systems based on enzyme cascades, and new approaches to reaction compartmentalization including single-cell hydrogel encapsulation techniques to achieve a genotype-phenotype link. We further summarize systematic scanning mutagenesis approaches and their merger with deep mutational scanning and massively parallel next-generation DNA sequencing technologies to generate mutability landscapes. Finally, we discuss the implementation of machine learning models for computational prediction of enzyme phenotypic fitness from sequence. This broad overview of current state-of-the-art approaches for enzyme engineering and evolution will aid newcomers and experienced researchers alike in identifying the important challenges that should be addressed to move the field forward.

Mapping Mechanostable Pulling Geometries of a Therapeutic Anticalin/CTLA-4 Protein Complex.

We used single-molecule AFM force spectroscopy (AFM-SMFS) in combination with click chemistry to mechanically dissociate anticalin, a non-antibody protein binding scaffold, from its target (CTLA-4), by pulling from eight different anchor residues. We found that pulling on the anticalin from residue 60 or 87 resulted in significantly higher rupture forces and a decrease in koff by 2-3 orders of magnitude over a force range of 50-200 pN. Five of the six internal anchor points gave rise to complexes significantly more stable than N- or C-terminal anchor points, rupturing at up to 250 pN at loading rates of 0.1-10 nN s-1. Anisotropic network modeling and molecular dynamics simulations helped to explain the geometric dependency of mechanostability. These results demonstrate that optimization of attachment residue position on therapeutic binding scaffolds can provide large improvements in binding strength, allowing for mechanical affinity maturation under shear stress without mutation of binding interface residues.

Metastatic Esophageal Carcinoma Cells Exhibit Reduced Adhesion Strength and Enhanced Thermogenesis.

Despite continuous improvements in multimodal therapeutic strategies, esophageal carcinoma maintains a high mortality rate. Metastases are a major life-limiting component; however, very little is known about why some tumors have high metastatic potential and others not. In this study, we investigated thermogenic activity and adhesion strength of primary tumor cells and corresponding metastatic cell lines derived from two patients with metastatic adenocarcinoma of the esophagus. We hypothesized that the increased metastatic potential of the metastatic cell lines correlates with higher thermogenic activity and decreased adhesion strength. Our data show that patient-derived metastatic esophageal tumor cells have a higher thermogenic profile as well as a decreased adhesion strength compared to their corresponding primary tumor cells. Using two paired esophageal carcinoma cell lines of primary tumor and lymph nodes makes the data unique. Both higher specific thermogenesis profile and decreased adhesion strength are associated with a higher metastatic potential. They are in congruence with the clinical patient presentation. Understanding these functional, biophysical properties of patient derived esophageal carcinoma cell lines will enable us to gain further insight into the mechanisms of metastatic potential of primary tumors and metastases. Microcalorimetric evaluation will furthermore allow for rapid assessment of new treatment options for primary tumor and metastases aimed at decreasing the metastatic potential.

Scalable Online Learning in Physical Chemistry.

The SARS-CoV-2/COVID-19 pandemic has disrupted higher education across the globe. As of early November 2020, Europe now finds itself in the middle of a second wave that is even more destructive than the first. The Swiss Federal Council declared on 28 October, 2020 that face-to-face teaching at Swiss Universities was to cease within days. With large introductory lectures in natural science faculties forced entirely online, educators in Switzerland are facing new challenges and dealing with the limitations of remote instruction. Through a series of anecdotes and observations, this article identifies challenges associated with scalable online learning, and explores methods to mitigate them. Additionally, several advantages to scalable online instruction are identified. By focusing on areas where online instruction has significant advantages, I argue that we can deliver high quality instruction in the chemical sciences remotely.

Lifecycle of the invasive omnivore, Forficula auricularia, in Australian grain growing environments.

BACKGROUND: The European earwig, Forficula auricularia (L.) (Dermaptera: Forficulidae), is regarded as an important beneficial in many orchard environments but has the potential to be a plant pest in other systems, including to grain crops. Due to its agricultural importance, the lifecycle of F. auricularia has been widely studied in North America and Europe. However, much less is known in the southern hemisphere, including Australia where F. auricularia has been present for over 170 years. RESULTS: To elucidate the lifecycle of F. auricularia, we monitored five sites in grain-growing areas of southern Australia using two different trap types. Adults were found year-round, but most prevalent from late-spring to mid-winter. First instars were typically found from mid to late winter. Second, third and fourth instars occurred from winter through to late spring. The seasonal development of F. auricularia in Australia may be much earlier than observed in comparable North American studies. Degree day modelling highlights variation in development times across the active season for F. auricularia across our sites. CONCLUSION: Forficula auricularia is well adapted to the Australian grain growing environments. The timing of egg hatching aligns closely with crop emergence, juveniles then develop alongside the crop, and adult development occurs by harvest time in late spring. These findings are important given that many of these crops (canola, lucerne, pulses) are vulnerable to attack by F. auricularia during emergence and development. They also suggest a phenotypic capacity of this species to adapt different phenology after introduction into a novel environment. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Influence of Fluorination on Single-Molecule Unfolding and Rupture Pathways of a Mechanostable Protein Adhesion Complex.

We investigated the influence of fluorination on unfolding and unbinding reaction pathways of a mechanostable protein complex comprising the tandem dyad XModule-Dockerin bound to Cohesin. Using single-molecule atomic force spectroscopy, we mapped the energy landscapes governing the unfolding and unbinding reactions. We then used sense codon suppression to substitute trifluoroleucine in place of canonical leucine globally in XMod-Doc. Although TFL substitution thermally destabilized XMod-Doc, it had little effect on XMod-Doc:Coh binding affinity at equilibrium. When we mechanically dissociated global TFL-substituted XMod-Doc from Coh, we observed the emergence of a new unbinding pathway with a lower energy barrier. Counterintuitively, when fluorination was restricted to Doc, we observed mechano-stabilization of the non-fluorinated neighboring XMod domain. This suggests that intramolecular deformation is modulated by fluorination and highlights the differences between equilibrium thermostability and non-equilibrium mechanostability. Future work is poised to investigate fluorination as a means to modulate mechanical properties of synthetic proteins and hydrogels.