An in vivo platform to select and evolve aggregation-resistant proteins

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Eliminating OFF-frame clones in randomized gene libraries: An improved split β-lactamase enrichment system

Large, randomized libraries are a key technology for many biotechnological applications. While genetic diversity is the main parameter most libraries direct their resources on, less focus is devoted to ensuring functional IN-frame expression. This study describes a faster and more efficient system based on a split β-lactamase complementation for removal of OFF-frame clones and increase of functional diversity, suitable for construction of randomized libraries. The gene of interest is inserted between two fragments of the β-lactamase gene, conferring resistance to β-lactam drugs only upon expression of an inserted IN-frame gene without stop codons or frameshifts. The preinduction-free system was capable of eliminating OFF-frame clones in starting mixtures of as little as 1% IN-frame clones and enriching to about 70% IN-frame clones, even when their starting rate was as low as 0.001%. The curation system was verified by constructing a single-domain antibody phage display library using trinucleotide phosphoramidites for randomizing a complementary determining region, while eliminating OFF-frame clones and maximizing functional diversity.

Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

Increasing protein stability by inferring substitution effects from high-throughput experiments

We apply a computational model, global multi-mutant analysis (GMMA), to inform on effects of most amino acid substitutions from a randomly mutated gene library. Using a high mutation frequency, the method can determine mutations that increase the stability of even very stable proteins for which conventional selection systems have reached their limit. As a demonstration of this, we screened a mutant library of a highly stable and computationally redesigned model protein using an in vivo genetic sensor for folding and assigned a stability effect to 374 of 912 possible single amino acid substitutions. Combining the top 9 substitutions increased the unfolding energy 47 to 69 kJ/mol in a single engineering step. Crystal structures of stabilized variants showed small perturbations in helices 1 and 2, which rendered them closer in structure to the redesign template. This case study illustrates the capability of the method, which is applicable to any screen for protein function.

Supramolecular Loading of DNA Hydrogels with Dye-Drug Conjugates for Real-Time Photoacoustic Monitoring of Chemotherapy

A longstanding problem with conventional cancer therapy is the nonspecific distribution of chemotherapeutics. Monitoring drug release in vivo via noninvasive bioimaging can thus have value, but it is difficult to distinguish loaded from released drug in live tissue. Here, this work describes an injectable supramolecular hydrogel that allows slow and trackable release of doxorubicin (Dox) via photoacoustic (PA) tomography. Dox is covalently linked with photoacoustic methylene blue (MB) to monitor Dox before, during, and after release from the hydrogel carrier. The conjugate (MB-Dox) possesses an IC50 of 161.4 × 10-9  m against human ovarian carcinoma (SKOV3) cells and loads into a DNA-clad hydrogel with 91.3% loading efficiency due to MB-Dox's inherent intramolecular affinity to DNA. The hydrogel is biodegradable by nuclease digestion, which causes gradual release of MB-Dox. This release rate is tunable based on the wt% of the hydrogel. This hydrogel maintains distinct PA contrast on the order of days when injected in vivo and demonstrates activatable PA spectral shifts   during hydrogel degradation. The released and loaded payload can be imaged relative to live tissue via PA and ultrasound signal being overlaid in real-time. The hydrogel slowed the rate of the murine intraperitoneal tumor growth 72.2% more than free Dox.

Single residue modulators of amyloid formation in the N-terminal P1-region of α-synuclein

Alpha-synuclein (αSyn) is a protein involved in neurodegenerative disorders including Parkinson’s disease. Amyloid formation of αSyn can be modulated by the ‘P1 region’ (residues 36-42). Here, mutational studies of P1 reveal that Y39A and S42A extend the lag-phase of αSyn amyloid formation in vitro and rescue amyloid-associated cytotoxicity in C. elegans. Additionally, L38I αSyn forms amyloid fibrils more rapidly than WT, L38A has no effect, but L38M does not form amyloid fibrils in vitro and protects from proteotoxicity. Swapping the sequence of the two residues that differ in the P1 region of the paralogue γSyn to those of αSyn did not enhance fibril formation for γSyn. Peptide binding experiments using NMR showed that P1 synergises with residues in the NAC and C-terminal regions to initiate aggregation. The remarkable specificity of the interactions that control αSyn amyloid formation, identifies this region as a potential target for therapeutics, despite their weak and transient nature.

The authors of this work characterize the effect of amino acid substitution on α-synuclein (α-Syn) aggregation. Residues 38 and 42 (in addition to 39) within the P1 region of α-Syn affect amyloid formation. The effect of substitution at position 38 is dependent on the amino-acid introduced, suggesting that specific interactions control α -Syn aggregation.

Understanding and evolving prions by yeast multiplexed assays

Yeast genetics made it possible to derive the first fundamental insights into prion composition, conformation, and propagation. Fast-forward 30 years and the same model organism is now proving an extremely powerful tool to comprehensively explore the impact of mutations in prion sequences on their function, toxicity, and physical properties. Here, we provide an overview of novel multiplexed strategies where deep mutagenesis is combined to a range of tailored selection assays in yeast, which are particularly amenable for investigating prions and prion-like sequences. By mimicking evolution in a flask, these multiplexed approaches are revealing mechanistic insights on the consequences of prion self-assembly, while also reporting on the structure prion sequences adopt in vivo.

The effect of mutation on an aggregation-prone protein: An in vivo, in vitro, and in silico analysis

Aggregation of initially stably structured proteins is involved in more than 20 human amyloid diseases. Despite intense research, however, how this class of proteins assembles into amyloid fibrils remains poorly understood, principally because of the complex effects of amino acid substitutions on protein stability, solubility, and aggregation propensity. We address this question using β2-microglobulin (β2m) as a model system, focusing on D76N-β2m that is involved in hereditary amyloidosis. This amino acid substitution causes the aggregation-resilient wild-type protein to become highly aggregation prone in vitro, although the mechanism by which this occurs remained elusive. Here, we identify the residues key to protecting β2m from aggregation by coupling aggregation with antibiotic resistance in E. coli using a tripartite β-lactamase assay (TPBLA). By performing saturation mutagenesis at three different sites (D53X-, D76X-, and D98X-β2m) we show that residue 76 has a unique ability to drive β2m aggregation in vivo and in vitro. Using a randomly mutated D76N-β2m variant library, we show that all of the mutations found to improve protein behavior involve residues in a single aggregation-prone region (APR) (residues 60 to 66). Surprisingly, no correlation was found between protein stability and protein aggregation rate or yield, with several mutations in the APR decreasing aggregation without affecting stability. Together, the results demonstrate the power of the TPBLA to develop proteins that are resilient to aggregation and suggest a model for D76N-β2m aggregation involving the formation of long-range couplings between the APR and Asn76 in a nonnative state.

Aggregation Time Machine: A Platform for the Prediction and Optimization of Long-Term Antibody Stability Using Short-Term Kinetic Analysis

Monoclonal antibodies are the fastest growing class of therapeutics. However, aggregation limits their shelf life and can lead to adverse immune responses. Assessment and optimization of the long-term antibody stability are therefore key challenges in the biologic drug development. Here, we present a platform based on the analysis of temperature-dependent aggregation data that can dramatically shorten the assessment of the long-term aggregation stability and thus accelerate the optimization of antibody formulations. For a set of antibodies used in the therapeutic areas from oncology to rheumatology and osteoporosis, we obtain an accurate prediction of aggregate fractions for up to three years using the data obtained on a much shorter time scale. Significantly, the strategy combining kinetic and thermodynamic analysis not only contributes to a better understanding of the molecular mechanisms of antibody aggregation but has already proven to be very effective in the development and production of biological therapeutics.

Evaluating parameters affecting drug fate at the intramuscular injection site

Intramuscular (IM) injections are a well-established method of delivering a variety of therapeutics formulated for parenteral administration. While the wide range of commercial IM pharmaceuticals provide a wealth of pharmacokinetic (PK) information following injection, there remains an inadequate understanding of drug fate at the IM injection site that could dictate these PK outcomes. An improved understanding of injection site events could improve approaches taken by formulation scientists to identify therapeutically effective and consistent drug PK outcomes. Interplay between the typically non-physiological aspects of drug formulations and the homeostatic IM environment may provide insights into the fate of drugs at the IM injection site, leading to predictions of how a drug will behave post-injection in vivo. Immune responses occur by design after e.g. vaccine administration, however immune responses post-injection are not in the scope of this article. Taking cues from existing in vitro modelling technologies, the purpose of this article is to propose "critical parameters" of the IM environment that could be examined in hypothesis-driven studies. Outcomes of such studies might ultimately be useful in predicting and improving in vivo PK performance of IM injected drugs.

High-throughput developability assays enable library-scale identification of producible protein scaffold variants

Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We identified the most informative assay combination via cross-validation accuracy and correlation feature selection and demonstrated the ability of machine learning models to exploit nonlinear mutual information to increase the assays' predictive utility. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a site-wise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

Challenges for design of aggregation-resistant variants of granulocyte colony-stimulating factor

Non-native protein aggregation is a long-standing issue in pharmaceutical biotechnology. A rational design approach was used in order to identify variants of recombinant human granulocyte colony-stimulating factor (rhG-CSF) with lower aggregation propensity at solution conditions that are typical of commercial formulation. The approach used aggregation-prone-region (APR) predictors to select single amino acid substitutions that were predicted to decrease intrinsic aggregation propensity (IAP). The results of static light scattering temperature-ramps and chemical unfolding experiments demonstrated that none of the selected variants exhibited improved aggregation resistance, and the apparent conformational stability of each variant was lower than that of WT. Aggregation studies under partly denaturing conditions suggested that the IAP of at least one variant remained unaltered. Overall, this study highlights a general challenge in designing aggregation resistance for proteins, due to the need to accurately predict both APRs and conformational stability.

Population Modelling in Affective Disorders

Purpose of Review

The prevalence of affective disorders is on the rise. This upward trajectory leads to a substantial personal and societal cost. There is growing body of literature demonstrating decision-making impairments associated with affective disorders, and more studies are using computational modelling methods to infer underlying mechanisms of these impairments from participant choice behaviour. However, lack of population modelling suggests that data resources may still be underutilised.

Recent Findings

A number of recent studies associated major depression with abnormal risky decision-making as well as impairments in temporal discounting and social decision-making. These domains capture relevant aspects of real-life decision-making. Consequently, data from these studies can be used to define behavioural phenotypes for major depression.

Summary

The manuscript describes a detailed proposal for population modelling to capture changes in the prevalence rate of major depression. The population modelling approach can also identify which decision-making domains can account for a larger part of impairments in psychosocial functioning and how behavioural interventions built on computational principles can target these to improve real-life psychosocial functioning in patient groups.

An enzyme-based biosensor for monitoring and engineering protein stability in vivo

Protein stability affects the physiological functions of proteins and is also a desirable trait in many protein engineering tasks, yet improving protein stability is challenging because of limitations in methods for directly monitoring protein stability in cells. Here, we report an in vivo stability biosensor wherein a protein of interest (POI) is inserted into a microbial enzyme (CysGA) that catalyzes the formation of endogenous fluorescent compounds, thereby coupling POI stability to simple fluorescence readouts. We demonstrate the utility of the biosensor in directed evolution to obtain stabilized, less aggregation-prone variants of two POIs (including nonamyloidogenic variants of human islet amyloid polypeptide). Beyond engineering applications, we exploited our biosensor in deep mutational scanning for experimental delineation of the stability-related contributions of all residues throughout the catalytic domain of a histone H3K4 methyltransferase, thereby revealing its scientifically informative stability landscape. Thus, our highly accessible method for in vivo monitoring of the stability of diverse proteins will facilitate both basic research and applied protein engineering efforts.

Natural product-informed exploration of chemical space to enable bioactive molecular discovery

The search for new bioactive molecules remains an open challenge limiting our ability to discover new drugs to treat disease and chemical probes to comprehensively study biological processes. The vastness of chemical space renders its exploration unfeasible by synthesis alone. Historically, chemists have tended to explore chemical space unevenly without committing to systematic frameworks for navigation. This minireview covers a range of approaches that take inspiration from the structure or origin of natural products, and help focus molecular discovery on biologically-relevant regions of chemical space. All these approaches have enabled the discovery of distinctive and novel bioactive small molecules such as useful chemical probes of biological mechanisms. This minireview comments on how such approaches may be developed into more general frameworks for the systematic identification of currently unexplored regions of biologically-relevant chemical space, a challenge that is central to both chemical biology and medicinal chemistry.

Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

Aptamer Functionalized Lipid Multilayer Gratings for Label-Free Analyte Detection

Lipid multilayer gratings are promising optical biosensor elements that are capable of transducing analyte binding events into changes in an optical signal. Unlike solid state transducers, reagents related to molecular recognition and signal amplification can be incorporated into the lipid grating ink volume prior to fabrication. Here we describe a strategy for functionalizing lipid multilayer gratings with a DNA aptamer for the protein thrombin that allows label-free analyte detection. A double cholesterol-tagged, double-stranded DNA linker was used to attach the aptamer to the lipid gratings. This approach was found to be sufficient for binding fluorescently labeled thrombin to lipid multilayers with micrometer-scale thickness. In order to achieve label-free detection with the sub-100 nm-thick lipid multilayer grating lines, the binding affinity was improved by varying the lipid composition. A colorimetric image analysis of the light diffracted from the gratings using a color camera was then used to identify the grating nanostructures that lead to an optimal signal. Lipid composition and multilayer thickness were found to be critical parameters for the signal transduction from the aptamer functionalized lipid multilayer gratings.