Animal toxins present a major threat to human health worldwide, predominantly through snakebite envenomings, which are responsible for over 100,000 deaths each year. To date, the only available treatment against snakebite envenoming is plasma-derived antivenom. However, despite being key to limiting morbidity and mortality among snakebite victims, current antivenoms suffer from several drawbacks, such as immunogenicity and high cost of production. Consequently, avenues for improving envenoming therapy, such as the discovery of toxin-sequestering monoclonal antibodies against medically important target toxins through phage display selection, are being explored. However, alternative binding protein scaffolds that exhibit certain advantages compared to the well-known immunoglobulin G scaffold, including high stability under harsh conditions and low cost of production, may pose as possible low-cost alternatives to antibody-based therapeutics. There is now a plethora of alternative binding protein scaffolds, ranging from antibody derivatives (e.g., nanobodies), through rationally designed derivatives of other human proteins (e.g., DARPins), to derivatives of non-human proteins (e.g., affibodies), all exhibiting different biochemical and pharmacokinetic profiles. Undeniably, the high level of engineerability and potentially low cost of production, associated with many alternative protein scaffolds, present an exciting possibility for the future of snakebite therapeutics and merit thorough investigation. In this review, a comprehensive overview of the different types of binding protein scaffolds is provided together with a discussion on their relevance as potential modalities for use as next-generation antivenoms.

Venom-induced haemorrhage constitutes a severe pathology in snakebite envenomings, especially those inflicted by viperid species. To both explore venom activity accurately and evaluate the efficacy of viperid antivenoms for the neutralisation of haemorrhagic activity it is essential to have available a precise, quantitative tool for empirically determining venom-induced haemorrhage. Thus, we have built on our prior approach and developed a new AI-guided tool (AHA) for the quantification of venom-induced haemorrhage in mice. Using a smartphone, it takes less than a minute to take a photo, upload the image, and receive accurate information on the magnitude of a venom-induced haemorrhagic lesion in mice. This substantially decreases analysis time, reduces human error, and does not require expert haemorrhage analysis skills. Furthermore, its open access web-based graphical user interface makes it easy to use and implement in laboratories across the globe. Together, this will reduce the resources required to preclinically assess and control the quality of antivenoms, whilst also expediting the profiling of haemorrhagic activity in venoms for the wider toxinology community.

The assembly of robust, modular biological components into complex functional systems is central to synthetic biology. Here, we apply modular “plug and play” design principles to a solid-phase protein display system that facilitates protein purification and functional assays. Specifically, we capture proteins on polyacrylamide hydrogel display beads (PHD beads) made in microfluidic droplet generators. These monodisperse PHD beads are decorated with predefined amounts of anchors, methacrylate-PEG-benzylguanine (BG) and methacrylate-PEG-chloroalkane (CA), that react covalently with SNAP-/Halo-tag fusion proteins, respectively, in a specific, orthogonal, and stable fashion. Anchors, and thus proteins, are distributed throughout the entire bead volume, allowing attachment of ∼109 protein molecules per bead (⌀ 20 μm) a higher density than achievable with commercial surface-modified beads. We showcase a diverse array of protein modules that enable the secondary capture of proteins, either noncovalently (IgG and SUMO-tag) or covalently (SpyCatcher, SpyTag, SnpCatcher, and SnpTag), in mono- and multivalent display formats. Solid-phase protein binding and enzymatic assays are carried out, and incorporating the photocleavable protein PhoCl enables the controlled release of modules via visible-light irradiation for functional assays in solution. We utilize photocleavage for valency engineering of an anti-TRAIL-R1 scFv, enhancing its apoptosis-inducing potency ∼50-fold through pentamerization.

Snakebite envenoming is a global public health issue that causes significant morbidity and mortality, particularly in low-income regions of the world. The clinical manifestations of envenomings vary depending on the snake's venom, with paralysis, haemorrhage, and necrosis being the most common and medically relevant effects. To assess the efficacy of antivenoms against dermonecrosis, a preclinical testing approach involves in vivo mouse models that mimic local tissue effects of cytotoxic snakebites in humans. However, current methods for assessing necrosis severity are time-consuming and susceptible to human error. To address this, we present the Venom Induced Dermonecrosis Analysis tool (VIDAL), a machine-learning-guided image-based solution that can automatically identify dermonecrotic lesions in mice, adjust for lighting biases, scale the image, extract lesion area and discolouration, and calculate the severity of dermonecrosis. We also introduce a new unit, the dermonecrotic unit (DnU), to better capture the complexity of dermonecrosis severity. Our tool is comparable to the performance of state-of-the-art histopathological analysis, making it an accessible, accurate, and reproducible method for assessing dermonecrosis. Given the urgent need to address the neglected tropical disease that is snakebite, high-throughput technologies such as VIDAL are crucial in developing and validating new and existing therapeutics for this debilitating disease.

The robust modularity of biological components that are assembled into complex functional systems is central to synthetic biology. Here we apply modular “plug and play” design principles to a microscale solid phase protein display system that enables protein purification and functional assays for biotherapeutics. Specifically, we capture protein molecules from cell lysates on polyacrylamide hydrogel display beads (‘PHD beads’), made in microfluidic droplet generators. These monodisperse PHD beads are decorated with predefined amounts of anchors, methacrylate-PEG-benzylguanine (BG) and methacrylate-PEG-chloroalkane (CA). Anchors form covalent bonds with fusion proteins bearing cognate tag recognition (SNAP and Halo-tags) in specific, orthogonal and stable fashion. Given that these anchors are copolymerised throughout the 3D structure of the beads, proteins are also distributed across the entire bead sphere, allowing attachment of ∼109 protein molecules per bead (Ø 20 μm). This mode of attachment reaches a higher density than possible on widely used surface-modified beads, and additionally mitigates surface effects that often complicate studies with proteins on beads. We showcase a diverse array of protein modules that enable the secondary capture of proteins, either non-covalently (IgG and SUMO-tag) or covalently (SpyCatcher, SpyTag, SnpCatcher and SnpTag). Proteins can be displayed in their monomeric forms, but also reformatted as a multivalent display (using secondary capture modules that create branches) to test the contributions of avidity and multivalency towards protein function. Finally, controlled release of modules by irradiation of light is achieved by incorporating the photocleavable protein PhoCl: irradiation severs the displayed protein from the solid support, so that functional assays can be carried out in solution. As a demonstration of the utility of valency engineering, an antibody drug screen is performed, in which an anti-TRAIL-R1 scFv protein is released into solution as monomers-hexamers, showing a ∼50-fold enhanced potency in the pentavalent format. The ease of protein purification on solid support, quantitative control over presentation and release of proteins and choice of valency make this experimental format a versatile, modular platform for large scale functional analysis of proteins, in bioassays of protein-protein interactions, enzymatic catalysis and bacteriolysis.Table of Contents Graphics

Venom-induced haemorrhage constitutes a severe pathology in snakebite envenomings, especially those inflicted by viperid species. In order to both explore venom compositions accurately, and evaluate the efficacy of viperid antivenoms for the neutralisation of haemorrhagic activity it is essential to have available a precise, quantitative tool for empirically determining venom-induced haemorrhage. Thus, we have built on our prior approach and developed a new AI-guided tool (ALOHA) for the quantification of venom-induced haemorrhage in mice. Using a smartphone, it takes less than a minute to take a photo, upload the image, and receive accurate information on the magnitude of a venom-induced haemorrhagic lesion in mice. This substantially decreases analysis time, reduces human error, and does not require expert haemorrhage analysis skills. Furthermore, its open access web-based graphical user interface makes it easy to use and implement in laboratories across the globe. Together, this will reduce the resources required to preclinically assess and control the quality of antivenoms, whilst also expediting the profiling of hemorrhagic activity in venoms for the wider toxinology community.

Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold. However, their performance has primarily been evaluated on well-characterised proteins, and comparisons using proteins with poor reference templates are lacking. In this study, we evaluated three modelling tools on their prediction of over 1000 snake venom toxin structures with no reference templates. Our findings show that AlphaFold2 (AF2) performed the best across all assessed parameters. We also observed that ColabFold (CF) only scored slightly worse than AF2, while being computationally less intensive. All tools struggled with regions of intrinsic disorder, such as loops and propeptide regions, and performed well in predicting the structure of functional domains. Overall, our study highlights the importance of exercising caution when working with proteins that have poor reference templates, are large, and contain flexible regions. Nonetheless, leveraging computational structure prediction tools can provide valuable insights into the modelling of protein interactions with different targets and reveal potential binding sites, active sites, and conformational changes, as well as into the design of potential molecular binders for reagent, diagnostic, or therapeutic purposes.