Interactions between misfolded protein oligomers and membranes: A central topic in neurodegenerative diseases?

AbstractThe deposition of amyloid material has been associated with many different diseases. Although these diseases are very diverse the amyloid material share many common features such as cross-β-sheet structure of the backbone of the proteins deposited. Another common feature of the aggregation process for a wide variety of proteins is the presence of prefibrillar oligomers. These oligomers are linked to the cytotoxicity occurring during the aggregation of proteins. These prefibrillar oligomers interact extensively with lipid membranes and in some cases leads to destabilization of lipid membranes. This interaction is however highly dependent on the nature of both the oligomer and the lipids. Anionic lipids are often required for interaction with the lipid membrane while increased exposure of hydrophobic patches from highly dynamic protein oligomers are structural determinants of cytotoxicity of the oligomers. To explore the oligomer lipid interaction in detail the interaction between oligomers of α-synuclein and the 4th fasciclin-1 domain of TGFBIp with lipid membranes will be examined here. For both proteins the dynamic species are the ones causing membrane destabilization and the membrane interaction is primarily seen when the lipid membranes contain anionic lipids. Hence the dynamic nature of oligomers with exposed hydrophobic patches alongside the presence of anionic lipids could be essential for the cytotoxicity observed for prefibrillar oligomers in general. This article is part of a Special Issue entitled: Lipid–protein interactions

Microfluidic and nanotechnology based assays for the development of safe biopharmaceuticals

Protein stability towards aggregation represents a potential challenge for the production and administration of pharmaceuticals. In particular, aggregation can compromise the developability and shelf-life of the products, with consequences for yield and safety, respectively. In this work, we discuss two novel approaches for the analysis of the stability of protein formulations: (1) A microfluidic diffusion-sizing platform to analyze protein sizes and interactions at high protein concentration directly in the solution state with minimal perturbation of the sample. The limited dilution of the sample during the analysis and the possibility to characterize properties directly in the solution state make the technique suitable for the analysis of heterogeneous solutions of proteins under dynamic equilibrium. We show how the platform represents an attractive tool for the analysis of sizes and interactions of proteins in both diluted and high-concentration solutions during development, manufacturing, and formulation. (2) A highly controlled assay of surface-induced protein aggregation based on nanoparticles. Protein aggregation is often due to heterogeneous nucleation events occurring at interfaces, including air/water interface, impurities and leachable particles. However, the development of screening tools against surface aggregation has been hindered by the difficulty in generating a controlled amount of surface stress in the formulation as well as in decoupling the surface effect from the contribution of hydrodynamic flows. In our assay, we leverage the flexibility of polymer chemistry to finely tune the properties and amount of surfaces provided by the nanoparticles, inducing aggregation of soluble peptides and proteins, including antibodies, in a time scale of a few hours. This platform represents i) an attractive tool for fundamental studies of heterogeneous nucleation events under stagnant and flow conditions, and ii) a high-throughput screening assay of the effect of intrinsic and extrinsic variables on protein stability towards interface-induced aggregation. Please click Additional Files below to see the full abstract

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

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

Automated optimisation of solubility and conformational stability of antibodies and proteins

Funder: Isaac Newton Trust / Wellcome Trust ISSF / University of Cambridge Joint Research GrantBiologics, 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.ukPietro Sormanni is a Royal Society University Research Fellow (URF\R1\201461). This work was partly funded by a Research Grant (RGS\R1\211126) from the Royal Society, by an Isaac Newton Trust / Wellcome Trust ISSF / University of Cambridge Joint Research Grant (MBAG/624 RG89305), and by UKRI EPSRC (EP/X024733/1). Biomolecular production and some of the characterisations were funded by Novo Nordisk. M.O. is a PhD student funded by AstraZeneca. A.B. and M.M.M are PhD students within the Novo Nordisk R&D STAR Fellowship programme and are partially funded by Innovation Fund Denmark

Automated optimisation of solubility and conformational stability of antibodies and proteins

Antibodies find key applications in research, diagnostics, and therapeutics, but their development can be impeded by poor stability or solubility. Here the authors developed a computational strategy that enables antibody optimisation, without affecting functionality

Automated optimisation of solubility and conformational stability of antibodies and proteins

Acknowledgements: P.S. is a Royal Society University Research Fellow (URF\R1\201461). This work was partly funded by a Research Grant (RGS\R1\211126) from the Royal Society, by an Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge Joint Research Grant (MBAG/624 RG89305), and by UKRI EPSRC (EP/X024733/1). Biomolecular production and some of the characterisations were funded by Novo Nordisk. M.O. is a Ph.D. student funded by AstraZeneca. A.B. and M.M.M are Ph.D. students within the Novo Nordisk R&D STAR Fellowship programme and are partially funded by Innovation Fund Denmark.Funder: Isaac Newton Trust / Wellcome Trust ISSF / University of Cambridge Joint Research Grant (MBAG/624 RG89305)AbstractBiologics, 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.</jats:p

An accelerated surface-mediated stress assay of antibody instability for developability studies

© 2020 The Author(s). Published with license by Taylor & Francis Group, LLC. High physical stability is required for the development of monoclonal antibodies (mAbs) into successful therapeutic products. Developability assays are used to predict physical stability issues such as high viscosity and poor conformational stability, but protein aggregation remains a challenging property to predict. Among different types of stresses, air–water and solid–liquid interfaces are well known to potentially trigger protein instability and induce aggregation. Yet, in contrast to the increasing number of developability assays to evaluate bulk properties, there is still a lack of experimental methods to evaluate antibody stability against interfaces. Here, we investigate the potential of a hydrophobic nanoparticle surface-mediated stress assay to assess the stability of mAbs during the early stages of development. We evaluate this surface-mediated accelerated stability assay on a rationally designed library of 14 variants of a humanized IgG4, featuring a broad span of solubility values and other developability properties. The assay could identify variants characterized by high instability against agitation in the presence of air–water interfaces. Remarkably, for the set of investigated molecules, we observe strong correlations between the extent of aggregation induced by the surface-mediated stress assay and other developability properties of the molecules, such as aggregation upon storage at 45°C, self-association (evaluated by affinity-capture self-interaction nanoparticle spectroscopy) and nonspecific interactions (estimated by cross-interaction chromatography, stand-up monolayer chromatography (SMAC), SMAC*). This highly controlled surface-mediated stress assay has the potential to complement and increase the ability of the current set of screening techniques to assess protein aggregation and developability potential of mAbs during the early stages of drug development. Abbreviations:AC-SINS: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy; AMS: Ammonium sulfate precipitation; ANS: 1-anilinonaphtalene-8-sulfonate; CIC: Cross-interaction chromatography; DLS: Dynamic light scattering; HIC: Hydrophobic interaction chromatography; HNSSA: Hydrophobic nanoparticles surface-stress assay; mAb: Monoclonal antibody; NP: Nanoparticle; SEC: Size exclusion chromatography; SMAC: Stand-up monolayer chromatography; WT: Wild type.ISSN:1942-0862ISSN:1942-087

Nonspecificity fingerprints for clinical-stage antibodies in solution.

Monoclonal antibodies (mAbs) have successfully been developed for the treatment of a wide range of diseases. The clinical success of mAbs does not solely rely on optimal potency and safety but also require good biophysical properties to ensure a high developability potential. In particular, nonspecific interactions are a key developability parameter to monitor during discovery and development. Despite an increased focus on the detection of nonspecific interactions, their underlying physicochemical origins remain poorly understood. Here, we employ solution-based microfluidic technologies to characterize a set of clinical-stage mAbs and their interactions with commonly used nonspecificity ligands to generate nonspecificity fingerprints, providing quantitative data on the underlying physical chemistry. Furthermore, the solution-based analysis enables us to measure binding affinities directly, and we evaluate the contribution of avidity in nonspecific binding by mAbs. We find that avidity can increase the apparent affinity by two orders of magnitude. Notably, we find that a subset of these highly developed mAbs show nonspecific electrostatic interactions, even at physiological pH and ionic strength, and that they can form microscale particles with charge-complementary polymers. The group of mAb constructs flagged here for nonspecificity are among the worst performers in independent reports of surface and column-based screens. The solution measurements improve on the state-of-the-art by providing a stand-alone result for individual mAbs without the need to benchmark against cohort data. Based on our findings, we propose a quantitative solution-based nonspecificity score, which can be integrated in the development workflow for biological therapeutics and more widely in protein engineering