Automated optimization of the solubility of a hyper-stable α-amylase.

Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm's capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.

Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning.

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of α-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.

Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids.

Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) such as residues containing post-translational modifications (PTMs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the intrinsic solubility of mAA-containing peptides in aqueous solution at room temperature. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different small-size mAAs for a total number of 37 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides and confirm that our method can accurately assess the solubility of peptides containing mAAs. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm .

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics.

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

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.

Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform.

Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 µg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

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.

Sequence-based prediction of pH-dependent protein solubility using CamSol.

Solubility is a property of central importance for the use of proteins in research in molecular and cell biology and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are material intensive and time consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of solubility for large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the development of one such method to include in the predictions the effect of the pH on solubility. We illustrate the resulting pH-dependent predictions on a variety of antibodies and other proteins to demonstrate that these predictions achieve an accuracy comparable with that of experimental methods. We make this method publicly available at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolph, as the version 3.0 of CamSol.

An antibody scanning method for the detection of α-synuclein oligomers in the serum of Parkinson's disease patients.

Misfolded α-synuclein oligomers are closely implicated in the pathology of Parkinson's disease and related synucleinopathies. The elusive nature of these aberrant assemblies makes it challenging to develop quantitative methods to detect them and modify their behavior. Existing detection methods use antibodies to bind α-synuclein aggregates in biofluids, although it remains challenging to raise antibodies against α-synuclein oligomers. To address this problem, we used an antibody scanning approach in which we designed a panel of 9 single-domain epitope-specific antibodies against α-synuclein. We screened these antibodies for their ability to inhibit the aggregation process of α-synuclein, finding that they affected the generation of α-synuclein oligomers to different extents. We then used these antibodies to investigate the size distribution and morphology of soluble α-synuclein aggregates in serum and cerebrospinal fluid samples from Parkinson's disease patients. Our results indicate that the approach that we present offers a promising route for the development of antibodies to characterize soluble α-synuclein aggregates in biofluids.

Fragment-based computational design of antibodies targeting structured epitopes.

De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.

Correlation between the binding affinity and the conformational entropy of nanobody SARS-CoV-2 spike protein complexes.

Camelid single-domain antibodies, also known as nanobodies, can be readily isolated from naïve libraries for specific targets but often bind too weakly to their targets to be immediately useful. Laboratory-based genetic engineering methods to enhance their affinity, termed maturation, can deliver useful reagents for different areas of biology and potentially medicine. Using the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and a naïve library, we generated closely related nanobodies with micromolar to nanomolar binding affinities. By analyzing the structure-activity relationship using X-ray crystallography, cryoelectron microscopy, and biophysical methods, we observed that higher conformational entropy losses in the formation of the spike protein-nanobody complex are associated with tighter binding. To investigate this, we generated structural ensembles of the different complexes from electron microscopy maps and correlated the conformational fluctuations with binding affinity. This insight guided the engineering of a nanobody with improved affinity for the spike protein.

π-Clamp-Mediated Homo- and Heterodimerization of Single-Domain Antibodies via Site-Specific Homobifunctional Conjugation.

Post-translational protein-protein conjugation produces bioconjugates that are unavailable via genetic fusion approaches. A method for preparing protein-protein conjugates using π-clamp-mediated cysteine arylation with pentafluorophenyl sulfonamide functional groups is described. Two computationally designed antibodies targeting the SARS-CoV-2 receptor binding domain were produced (KD = 146, 581 nM) with a π-clamp sequence near the C-terminus and dimerized using this method to provide a 10-60-fold increase in binding (KD = 8-15 nM). When two solvent-exposed cysteine residues were present on the second protein domain, the π-clamp cysteine residue was selectively modified over an Asp-Cys-Glu cysteine residue, allowing for subsequent small-molecule conjugation. With this strategy, we build molecule-protein-protein conjugates with complete chemical control over the sites of modification.

An aggregation inhibitor specific to oligomeric intermediates of Aß42 derived from phage display libraries of stable, small proteins.

The self-assembly of amyloid ß peptide (Aß) to fibrillar and oligomeric aggregates is linked to Alzheimer's disease. Aß binders may serve as inhibitors of aggregation to prevent the generation of neurotoxic species and for the detection of Aß species. A particular challenge involves finding binders to on-pathway oligomers given their transient nature. Here we construct two phage­display libraries built on the highly inert and stable protein scaffold S100G, one containing a six-residue variable surface patch and one harboring a seven-residue variable loop insertion. Monomers and fibrils of Aß40 and Aß42 were separately coupled to silica nanoparticles, using a coupling strategy leading to the presence of oligomers on the monomer beads, and they were used in three rounds of affinity selection. Next-generation sequencing revealed sequence clusters and candidate binding proteins (SXkmers). Two SXkmers were expressed as soluble proteins and tested in terms of aggregation inhibition via thioflavin T fluorescence. We identified an SXkmer with loop­insertion YLTIRLM as an inhibitor of the secondary nucleation of Aß42 and binding analyses using surface plasmon resonance technology, Förster resonance energy transfer, and microfluidics diffusional sizing imply an interaction with intermediate oligomeric species. A linear peptide with the YLTIRLM sequence was found inhibitory but at a lower potency than the more constrained SXkmer loop. We identified an SXkmer with side-patch VI-WI-DD as an inhibitor of Aß40 aggregation. Remarkably, our data imply that SXkmer-YLTIRLM blocks secondary nucleation through an interaction with oligomeric intermediates in solution or at the fibril surface, which is a unique inhibitory mechanism for a library-derived inhibitor.

Conformational Entropy as a Potential Liability of Computationally Designed Antibodies.

In silico antibody discovery is emerging as a viable alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. A major question concerns the structural features of computer-designed complementarity determining regions (CDRs), including the role of conformational entropy in determining the stability and binding affinity of the designed antibodies. To address this problem, we used enhanced-sampling molecular dynamics simulations to compare the free energy landscapes of single-domain antibodies (sdAbs) designed using structure-based (DesAb-HSA-D3) and sequence-based approaches (DesAbO), with that of a nanobody derived from llama immunization (Nb10). Our results indicate that the CDR3 of DesAbO is more conformationally heterogeneous than those of both DesAb-HSA-D3 and Nb10, and the CDR3 of DesAb-HSA-D3 is slightly more dynamic than that of Nb10, which is the original scaffold used for the design of DesAb-HSA-D3. These differences underline the challenges in the rational design of antibodies by revealing the presence of conformational substates likely to have different binding properties and to generate a high entropic cost upon binding.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods.

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Neuroserpin and transthyretin are extracellular chaperones that preferentially inhibit amyloid formation.

Neuroserpin is a secreted protease inhibitor known to inhibit amyloid formation by the Alzheimer's beta peptide (Aß). To test whether this effect was constrained to Aß, we used a range of in vitro assays to demonstrate that neuroserpin inhibits amyloid formation by several different proteins and protects against the associated cytotoxicity but, unlike other known chaperones, has a poor ability to inhibit amorphous protein aggregation. Collectively, these results suggest that neuroserpin has an unusual chaperone selectivity for intermediates on the amyloid-forming pathway. Bioinformatics analyses identified a highly conserved 14-residue region containing an α helix shared between neuroserpin and the thyroxine-transport protein transthyretin, and we subsequently demonstrated that transthyretin also preferentially inhibits amyloid formation. Last, we used rationally designed neuroserpin mutants to demonstrate a direct involvement of the conserved 14-mer region in its chaperone activity. Identification of this conserved region may prove useful in the future design of anti-amyloid reagents.

An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement.

The solubility of proteins correlates with a variety of their properties, including function, production yield, pharmacokinetics, and formulation at high concentrations. High solubility is therefore a key requirement for the development of protein-based reagents for applications in life sciences, biotechnology, diagnostics, and therapeutics. Accurate solubility measurements, however, remain challenging and resource intensive, which limits their throughput and hence their applicability at the early stages of development pipelines, when long-lists of candidates are typically available in minute amounts. Here, we present an automated method based on the titration of a crowding agent (polyethylene glycol, PEG) to quantitatively assess relative solubility of proteins using about 200 µg of purified material. Our results demonstrate that this method is accurate and economical in material requirement and costs of reagents, which makes it suitable for high-throughput screening. This approach is freely-shared and based on a low cost, open-source liquid-handling robot. We anticipate that this method will facilitate the assessment of the developability of proteins and make it substantially more accessible.

Comparative Studies in the A30P and A53T α-Synuclein C. elegans Strains to Investigate the Molecular Origins of Parkinson's Disease.

The aggregation of α-synuclein is a hallmark of Parkinson's disease (PD) and a variety of related neurological disorders. A number of mutations in this protein, including A30P and A53T, are associated with familial forms of the disease. Patients carrying the A30P mutation typically exhibit a similar age of onset and symptoms as sporadic PD, while those carrying the A53T mutation generally have an earlier age of onset and an accelerated progression. We report two C. elegans models of PD (PDA30P and PDA53T), which express these mutational variants in the muscle cells, and probed their behavior relative to animals expressing the wild-type protein (PDWT). PDA30P worms showed a reduced speed of movement and an increased paralysis rate, control worms, but no change in the frequency of body bends. By contrast, in PDA53T worms both speed and frequency of body bends were significantly decreased, and paralysis rate was increased. α-Synuclein was also observed to be less well localized into aggregates in PDA30P worms compared to PDA53T and PDWT worms, and amyloid-like features were evident later in the life of the animals, despite comparable levels of expression of α-synuclein. Furthermore, squalamine, a natural product currently in clinical trials for treating symptomatic aspects of PD, was found to reduce significantly the aggregation of α-synuclein and its associated toxicity in PDA53T and PDWT worms, but had less marked effects in PDA30P. In addition, using an antibody that targets the N-terminal region of α-synuclein, we observed a suppression of toxicity in PDA30P, PDA53T and PDWT worms. These results illustrate the use of these two C. elegans models in fundamental and applied PD research.