Proteins behaving badly: emerging technologies in profiling biopharmaceutical aggregation

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

CORDAX web server: an online platform for the prediction and 3D visualization of aggregation motifs in protein sequences

MOTIVATION

Proteins, the molecular workhorses of biological systems, execute a multitude of critical functions dictated by their precise three-dimensional structures. In a complex and dynamic cellular environment, proteins can undergo misfolding, leading to the formation of aggregates that take up various forms, including amorphous and ordered aggregation in the shape of amyloid fibrils. This phenomenon is closely linked to a spectrum of widespread debilitating pathologies, such as Alzheimer's disease, Parkinson's disease, type-II diabetes, and several other proteinopathies, but also hampers the engineering of soluble agents, as in the case of antibody development. As such, the accurate prediction of aggregation propensity within protein sequences has become pivotal due to profound implications in understanding disease mechanisms, as well as in improving biotechnological and therapeutic applications.

RESULTS

We previously developed Cordax, a structure-based predictor that utilizes logistic regression to detect aggregation motifs in protein sequences based on their structural complementarity to the amyloid cross-beta architecture. Here, we present a dedicated web server interface for Cordax. This online platform combines several features including detailed scoring of sequence aggregation propensity, as well as 3D visualization with several customization options for topology models of the structural cores formed by predicted aggregation motifs. In addition, information is provided on experimentally determined aggregation-prone regions that exhibit sequence similarity to predicted motifs, scores, and links to other predictor outputs, as well as simultaneous predictions of relevant sequence propensities, such as solubility, hydrophobicity, and secondary structure propensity.

AVAILABILITY AND IMPLEMENTATION

The Cordax webserver is freely accessible at https://cordax.switchlab.org/.

Targeting Efficient Features of Urate Oxidase to Increase Its Solubility

With the demand for mass production of protein drugs, solubility has become a serious issue. Extrinsic and intrinsic factors both affect this property. A homotetrameric cofactor-free urate oxidase (UOX) is not sufficiently soluble. To engineer UOX for optimum solubility, it is important to identify the most effective factor that influences solubility. The most effective feature to target for protein engineering was determined by measuring various solubility-related factors of UOX. A large library of homologous sequences was obtained from the databases. The data was reduced to six enzymes from different organisms. On the basis of various sequence- and structure-derived elements, the most and the least soluble enzymes were defined. To determine the best protein engineering target for modification, features of the most and least soluble enzymes were compared. Metabacillus fastidiosus UOX was the most soluble enzyme, while Agrobacterium globiformis UOX was the least soluble. According to the comparison-constant method, positive surface patches caused by arginine residue distribution are appropriate targets for modification. Two Arg to Ala mutations were introduced to the least soluble enzyme to test this hypothesis. These mutations significantly enhanced the mutant's solubility. While different algorithms produced conflicting results, it was difficult to determine which proteins were most and least soluble. Solubility prediction requires multiple algorithms based on these controversies. Protein surfaces should be investigated regionally rather than globally, and both sequence and structural data should be considered. Several other biotechnological products could be engineered using the data reduction and comparison-constant methods used in this study.

Multi-epitope vaccine candidates based on mycobacterial membrane protein large (MmpL) proteins against Mycobacterium ulcerans

Buruli ulcer (BU) is a neglected tropical disease. It is caused by the bacterium Mycobacterium ulcerans and is characterized by skin lesions. Several studies were performed testing the Bacillus Calmette-Guérin (BCG) vaccine in human and animal models and M. ulcerans-specific vaccines in animal models. However, there are currently no clinically accepted vaccines to prevent M. ulcerans infection. The aim of this study was to identify T-cell and B-cell epitopes from the mycobacterial membrane protein large (MmpL) proteins of M. ulcerans. These epitopes were analysed for properties including antigenicity, immunogenicity, non-allergenicity, non-toxicity, population coverage and the potential to induce cytokines. The final 8 CD8+, 12 CD4+ T-cell and 5 B-cell epitopes were antigenic, non-allergenic and non-toxic. The estimated global population coverage of the CD8+ and CD4+ epitopes was 97.71%. These epitopes were used to construct five multi-epitope vaccine constructs with different adjuvants and linker combinations. The constructs underwent further structural analyses and refinement. The constructs were then docked with Toll-like receptors. Three of the successfully docked complexes were structurally analysed. Two of the docked complexes successfully underwent molecular dynamics simulations (MDS) and post-MDS analysis. The complexes generated were found to be stable. However, experimental validation of the complexes is required.

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.

Protein quality control and aggregation in the endoplasmic reticulum: From basic to bedside

Endoplasmic reticulum (ER) is the largest membrane-bound compartment in all cells and functions as a key regulator in protein biosynthesis, lipid metabolism, and calcium balance. Mammalian endoplasmic reticulum has evolved with an orchestrated protein quality control system to handle defective proteins and ensure endoplasmic reticulum homeostasis. Nevertheless, the accumulation and aggregation of misfolded proteins in the endoplasmic reticulum may occur during pathological conditions. The inability of endoplasmic reticulum quality control system to clear faulty proteins and aggregates from the endoplasmic reticulum results in the development of many human disorders. The efforts to comprehensively understand endoplasmic reticulum quality control network and protein aggregation will benefit the diagnostics and therapeutics of endoplasmic reticulum storage diseases. Herein, we overview recent advances in mammalian endoplasmic reticulum protein quality control system, describe protein phase transition model, and summarize the approaches to monitor protein aggregation. Moreover, we discuss the therapeutic applications of enhancing endoplasmic reticulum protein quality control pathways in endoplasmic reticulum storage diseases.

Interlinked role of ASN, TDP-43 and Miro1 with parkinopathy: Focus on targeted approach against neuropathy in parkinsonism

Parkinsonism is a complex neurodegenerative disease that is difficult to differentiate because of its idiopathic and unknown origins. The hereditary parkinsonism known as autosomal recessive-juvenile parkinsonism (AR-JP) is marked by tremors, dyskinesias, dystonic characteristics, and manifestations that improve sleep but do not include dementia. This was caused by deletions and point mutations in PARK2 (chromosome 6q25.2-27). Diminished or unusual sensations (paresthesias), loss of neuron strength both in the CNS and peripheral nerves, and lack of motor coordination are the hallmarks of neuropathy in parkinsonism. The incidence of parkinsonism during oxidative stress and ageing is associated with parkinopathy. Parkinopathy is hypothesized to be triggered by mutation of the parkin (PRKN) gene and loss of normal physiological functions of PRKN proteins, which triggers their pathogenic aggregation due to conformational changes. Two important genes that control mitochondrial health are PRKN and phosphatase and tensin homologue deleted on chromosome 10-induced putative kinase 1 (PINK1). Overexpression of TAR DNA-binding protein-43 (TDP-43) increases the aggregation of insoluble PRKN proteins in OMM. Foreign α-synuclein (ASN) promotes parkinopathy via S-nitrosylation and hence has a neurotoxic effect on dopaminergic nerves. Miro1 (Miro GTPase1), a member of the RAS superfamily, is expressed in nerve cells. Due to PINK1/PRKN and Miro1's functional relationship, an excess of mitochondrial calcium culminates in the destruction of dopaminergic neurons. An interlinked understanding of TDP-43, PINK1/PRKN, ASN, and Miro1 signalling in the communication among astrocytes, microglia, neurons, and immune cells within the brain explored the pathway of neuronal death and shed light on novel strategies for the diagnosis and treatment of parkinsonism.

Quantitative Interpretation of Protein Diffusion Coefficients in Mixed Protiated-Deuteriated Aqueous Solvents

Diffusion-ordered nuclear magnetic resonance (NMR) spectroscopy is widely used for the analysis of mixtures, dispersing the signals of different species in a two-dimensional spectrum according to their diffusion coefficients. However, interpretation of these diffusion coefficients is typically purely qualitative, for example, to deduce which species are bigger or smaller. In studies of proteins in solution, important questions concern the molecular weight of the proteins, the presence or absence of aggregation, and the degree of folding. The Stokes–Einstein Gierer–Wirtz estimation (SEGWE) method has been previously developed to simplify the complex relationship between diffusion coefficient and molecular mass, allowing the prediction of a species’ diffusion coefficient in a pure solvent based on its molecular weight. Here, we show that SEGWE can be extended to successfully predict both peptide and protein diffusion coefficients in mixed protiated–deuteriated water samples and, hence, distinguish effectively between globular and disordered proteins.

Computational methods to predict protein aggregation

In most cases, protein aggregation stems from the establishment of non-native intermolecular contacts. The formation of insoluble protein aggregates is associated with many human diseases and is a major bottleneck for the industrial production of protein-based therapeutics. Strikingly, fibrillar aggregates are naturally exploited for structural scaffolding or to generate molecular switches and can be artificially engineered to build up multi-functional nanomaterials. Thus, there is a high interest in rationalizing and forecasting protein aggregation. Here, we review the available computational toolbox to predict protein aggregation propensities, identify sequential or structural aggregation-prone regions, evaluate the impact of mutations on aggregation or recognize prion-like domains. We discuss the strengths and limitations of these algorithms and how they can evolve in the next future.

The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing

Biopharmaceuticals have revolutionized the field of medicine in the types of active ingredient molecules and treatable indications. Adoption of Quality by Design and Process Analytical Technology (PAT) frameworks has helped the biopharmaceutical field to realize consistent product quality, process intensification, and real-time control. As part of the PAT strategy, Raman spectroscopy offers many benefits and is used successfully in bioprocessing from single-cell analysis to cGMP process control. Since first introduced in 2011 for industrial bioprocessing applications, Raman has become a first-choice PAT for monitoring and controlling upstream bioprocesses because it facilitates advanced process control and enables consistent process quality. This paper will discuss new frontiers in extending these successes in upstream from scale-down to commercial manufacturing. New reports concerning the use of Raman spectroscopy in the basic science of single cells and downstream process monitoring illustrate industrial recognition of Raman’s value throughout a biopharmaceutical product’s lifecycle. Finally, we draw upon a nearly 90-year history in biological Raman spectroscopy to provide the basis for laboratory and in-line measurements of protein quality, including higher-order structure and composition modifications, to support formulation development.

Helical Stabilization of Peptide Macrocycles by Stapled Architectures

Over the past two decades, significant efforts have invested in the development of strategies for the stabilization of macrocyclic peptides with α-helix structure by stapling their architectures. These strategies can be divided into two categories: side chain to side chain cross-linking and N-terminal helix nucleation. These stable macrocyclic peptides have been applied in PPI inhibitors and self-assembly materials. Compared with unmodified short peptides, stable α-helix macrocyclic polypeptides have better biophysical properties including higher serum stability, cell permeability, and higher target affinity. This chapter will systematically introduce approaches for helical stabilization of peptide macrocycles, such as ring-closing metathesis (RCM), lactamisation, cycloadditions, reversible reactions, thioether formation as well as newly found sulfonium center formation and the common use of helical stabilized macrocyclic peptides.

Size-based Degradation of Therapeutic Proteins - Mechanisms, Modelling and Control

Protein therapeutics are in great demand due to their effectiveness towards hard-to-treat diseases. Despite their high demand, these bio-therapeutics are very susceptible to degradation via aggregation, fragmentation, oxidation, and reduction, all of which are very likely to affect the quality and efficacy of the product. Mechanisms and modelling of these degradation (aggregation and fragmentation) pathways is critical for gaining a deeper understanding of stability of these products. This review aims to provide a summary of major developments that have occurred towards unravelling the mechanisms of size-based protein degradation (particularly aggregation and fragmentation), modelling of these size-based degradation pathways, and their control. Major caveats that remain in our understanding and control of size-based protein degradation have also been presented and discussed.

Zika virus capsid anchor forms cytotoxic amyloid-like fibrils

Capsid-anchor (CA) of Zika virus (ZIKV) is a small, single-pass transmembrane sequence that separates the capsid (C) protein from downstream pre-membrane (PrM) protein. During polyprotein processing, CA is cleaved-off from C and PrM and left as a membrane-embedded peptide. CA plays an essential role in the assembly and maturation of the virus. However, its independent folding behavior is still unknown. Therefore, in this study, we investigated the amyloid-forming propensity of CA at physiological conditions. We observed the aggregation behavior of CA peptide using dye-binding assays and ThT kinetics. The morphological analysis of CA aggregates explored by high-resolution microscopy (TEM, AFM) and Far-UV CD spectroscopy revealed characteristic amyloid-like fibrils rich in β-sheet secondary structure. Further, the effect on mammalian cells exhibited the cytotoxic nature of the CA amyloid-fibrils. Our findings collectively shed light on the amyloidogenic phenomenon of flaviviral protein, which may contribute to their infection.

Analyzing the driving forces of insulin stability in the basic amino acid solutions: A perspective from hydration dynamics

Amino acids having basic side chains, as additives, are known to increase the stability of native-folded state of proteins, but their relative efficiency and the molecular mechanism are still controversial and obscure as well. In the present work, extensive atomistic molecular dynamics simulations were performed to investigate the hydration properties of aqueous solutions of concentrated arginine, histidine, and lysine and their comparative efficiency on regulating the conformational stability of the insulin monomer. We identified that in the aqueous solutions of the free amino acids, the nonuniform relaxation of amino acid-water hydrogen bonds was due to the entrapment of water molecules within the amino acid clusters formed in solutions. Insulin, when tested with these solutions, was found to show rigid conformations, relative to that in pure water. We observed that while the salt bridges formed by the lysine as an additive contributed more toward the direct interactions with insulin, the cation-π was more prominent for the insulin-arginine interactions. Importantly, it was observed that the preferentially more excluded arginine, compared to histidine and lysine from the insulin surface, enriches the hydration layer of the protein. Our study reveals that the loss of configurational entropy of insulin in arginine solution, as compared to that in pure water, is more as compared to the entropy loss in the other two amino acid solutions, which, moreover, was found to be due to the presence of motionally bound less entropic hydration water of insulin in arginine solution than in histidine or lysine solution.

Instability of therapeutic proteins - An overview of stresses, stabilization mechanisms and analytical techniques involved in lyophilized proteins

Solid-state is the preferred choice for storage of protein therapeutics to improve stability and preserve the biological activity by decreasing the physical and chemical degradation associated with liquid protein formulations. Lyophilization or freeze-drying is an effective drying method to overcome the instability problems of proteins. However, the processing steps (freezing, primary drying and secondary drying) involved in the lyophilization process can expose the proteins to various stress and harsh conditions, leading to denaturation, aggregation often a loss in activity of protein therapeutics. Stabilizers such as sugars and surfactants are often added to protect the proteins against physical stress associated with lyophilization process and storage conditions. Another way to curtail the degradation of proteins due to process related stress is by modification of the lyophilization process. Slow freezing, high nucleation temperature, decreasing the extent of supercooling, and annealing can minimize the formation of the interface (ice-water) by producing large ice crystals with less surface area, thereby preserving the native structure and stability of the proteins. Hence, a thorough understanding of formulation composition, lyophilization process parameters and the choice of analytical methods to characterize and monitor the protein instability is crucial for development of stable therapeutic protein products. This review provides an overview of various stress conditions that proteins might encounter during lyophilization process, mechanisms to improve the stability and analytical techniques to tackle the proteins instability during both freeze-drying and storage.

Particle Detection and Characterization for Biopharmaceutical Applications: Current Principles of Established and Alternative Techniques

Detection and characterization of particles in the visible and subvisible size range is critical in many fields of industrial research. Commercial particle analysis systems have proliferated over the last decade. Despite that growth, most systems continue to be based on well-established principles, and only a handful of new approaches have emerged. Identifying the right particle-analysis approach remains a challenge in research and development. The choice depends on each individual application, the sample, and the information the operator needs to obtain. In biopharmaceutical applications, particle analysis decisions must take product safety, product quality, and regulatory requirements into account. Biopharmaceutical process samples and formulations are dynamic, polydisperse, and very susceptible to chemical and physical degradation: improperly handled product can degrade, becoming inactive or in specific cases immunogenic. This article reviews current methods for detecting, analyzing, and characterizing particles in the biopharmaceutical context. The first part of our article represents an overview about current particle detection and characterization principles, which are in part the base of the emerging techniques. It is very important to understand the measuring principle, in order to be adequately able to judge the outcome of the used assay. Typical principles used in all application fields, including particle–light interactions, the Coulter principle, suspended microchannel resonators, sedimentation processes, and further separation principles, are summarized to illustrate their potentials and limitations considering the investigated samples. In the second part, we describe potential technical approaches for biopharmaceutical particle analysis as some promising techniques, such as nanoparticle tracking analysis (NTA), micro flow imaging (MFI), tunable resistive pulse sensing (TRPS), flow cytometry, and the space- and time-resolved extinction profile (STEP^®) technology.

Aggrescan3D standalone package for structure-based prediction of protein aggregation properties

SUMMARY

Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation-it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools.

AVAILABILITY AND IMPLEMENTATION

A3D standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. The A3D standalone source code, wiki with documentation and examples of use, and installation instructions for Linux, macOS and Windows are available in the A3D standalone repository at https://bitbucket.org/lcbio/aggrescan3d.

Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility

Protein aggregation is a hallmark of a growing number of human disorders and constitutes a major bottleneck in the manufacturing of therapeutic proteins. Therefore, there is a strong need of in-silico methods that can anticipate the aggregative properties of protein variants linked to disease and assist the engineering of soluble protein-based drugs. A few years ago, we developed a method for structure-based prediction of aggregation properties that takes into account the dynamic fluctuations of proteins. The method has been made available as the Aggrescan3D (A3D) web server and applied in numerous studies of protein structure-aggregation relationship. Here, we present a major update of the A3D web server to version 2.0. The new features include: extension of dynamic calculations to significantly larger and multimeric proteins, simultaneous prediction of changes in protein solubility and stability upon mutation, rapid screening for functional protein variants with improved solubility, a REST-ful service to incorporate A3D calculations in automatic pipelines, and a new, enhanced web server interface. A3D 2.0 is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/

Advances in the development of imaging probes and aggregation inhibitors for alpha-synuclein

Abnormal protein aggregation has been linked to many neurodegenerative diseases, including Parkinson’s disease (PD). The main pathological hallmark of PD is the formation of Lewy bodies (LBs) and Lewy neurites, both of which contain the presynaptic protein alpha-synuclein (α-syn). Under normal conditions, native α-syn exists in a soluble unfolded state but undergoes misfolding and aggregation into toxic aggregates under pathological conditions. Toxic α-syn species, especially oligomers, can cause oxidative stress, membrane penetration, synaptic and mitochondrial dysfunction, as well as other damage, leading to neuronal death and eventually neurodegeneration. Early diagnosis and treatments targeting PD pathogenesis are urgently needed. Given its critical role in PD, α-syn is an attractive target for the development of both diagnostic tools and effective therapeutics. This review summarizes the progress toward discovering imaging probes and aggregation inhibitors for α-syn. Relevant strategies and techniques in the discovery of α-syn-targeted drugs are also discussed.

Considerations for the Design of Antibody-Based Therapeutics

Antibody-based proteins have become an important class of biologic therapeutics, due in large part to the stability, specificity, and adaptability of the antibody framework. Indeed, antibodies not only have the inherent ability to bind both antigens and endogenous immune receptors but also have proven extremely amenable to protein engineering. Thus, several derivatives of the monoclonal antibody format, including bispecific antibodies, antibody-drug conjugates, and antibody fragments, have demonstrated efficacy for treating human disease, particularly in the fields of immunology and oncology. Reviewed here are considerations for the design of antibody-based therapeutics, including immunological context, therapeutic mechanisms, and engineering strategies. First, characteristics of antibodies are introduced, with emphasis on structural domains, functionally important receptors, isotypic and allotypic differences, and modifications such as glycosylation. Then, aspects of therapeutic antibody design are discussed, including identification of antigen-specific variable regions, choice of expression system, use of multispecific formats, and design of antibody derivatives based on fragmentation, oligomerization, or conjugation to other functional moieties. Finally, strategies to enhance antibody function through protein engineering are reviewed while highlighting the impact of fundamental biophysical properties on protein developability.

Third generation antibody discovery methods: in silico rational design

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

2D polarization imaging as a low-cost fluorescence method to detect α-synuclein aggregation ex vivo in models of Parkinson's disease

A hallmark of Parkinson's disease is the formation of large protein-rich aggregates in neurons, where α-synuclein is the most abundant protein. A standard approach to visualize aggregation is to fluorescently label the proteins of interest. Then, highly fluorescent regions are assumed to contain aggregated proteins. However, fluorescence brightness alone cannot discriminate micrometer-sized regions with high expression of non-aggregated proteins from regions where the proteins are aggregated on the molecular scale. Here, we demonstrate that 2-dimensional polarization imaging can discriminate between preformed non-aggregated and aggregated forms of α-synuclein, and detect increased aggregation in brain tissues of transgenic mice. This imaging method assesses homo-FRET between labels by measuring fluorescence polarization in excitation and emission simultaneously, which translates into higher contrast than fluorescence anisotropy imaging. Exploring earlier aggregation states of α-synuclein using such technically simple imaging method could lead to crucial improvements in our understanding of α-synuclein-mediated pathology in Parkinson's Disease.

SAR by kinetics for drug discovery in protein misfolding diseases

Protein oligomers are increasingly recognized as the most cytotoxic forms of protein aggregates. It has been very challenging, however, to target these oligomers with therapeutic compounds, because of their dynamic and transient nature. To overcome this problem, we present here a “structure–kinetic-activity relationship” (SKAR) approach, which enables the discovery and systematic optimization of compounds that reduce the number of oligomers produced during an aggregation reaction. We illustrate this strategy for the amyloid beta peptide (Aβ), which is closely associated with Alzheimer’s disease, by developing a rhodanine compound capable of dramatically reducing the production of Aβ oligomers. As this strategy is general, it can be applied to oligomers of any protein.

To develop effective therapeutic strategies for protein misfolding diseases, a promising route is to identify compounds that inhibit the formation of protein oligomers. To achieve this goal, we report a structure−activity relationship (SAR) approach based on chemical kinetics to estimate quantitatively how small molecules modify the reactive flux toward oligomers. We use this estimate to derive chemical rules in the case of the amyloid beta peptide (Aβ), which we then exploit to optimize starting compounds to curtail Aβ oligomer formation. We demonstrate this approach by converting an inactive rhodanine compound into an effective inhibitor of Aβ oligomer formation by generating chemical derivatives in a systematic manner. These results provide an initial demonstration of the potential of drug discovery strategies based on targeting directly the production of protein oligomers.

Engaging with Raman Spectroscopy to Investigate Antibody Aggregation

In the last decade, a number of studies have successfully demonstrated Raman spectroscopy as an emerging analytical technique for monitoring antibody aggregation, especially in the context of drug development and formulation. Raman spectroscopy is a robust method for investigating protein conformational changes, even in highly concentrated antibody solutions. It is non-destructive, reproducible and can probe samples in an aqueous environment. In this review, we focus on the application and challenges associated with using Raman spectroscopy as a tool to study antibody aggregates.

Development of orthogonal NISTmAb size heterogeneity control methods

The NISTmAb is a monoclonal antibody Reference Material from the National Institute of Standards and Technology; it is a class-representative IgG1κ intended to serve as a pre-competitive platform for harmonization and technology development in the biopharmaceutical industry. The publication series of which this paper is a part describes NIST's overall control strategy to ensure NISTmAb quality and availability over its lifecycle. In this paper, the development of a control strategy for monitoring NISTmAb size heterogeneity is described. Optimization and qualification of size heterogeneity measurement spanning a broad size range are described, including capillary electrophoresis-sodium dodecyl sulfate (CE-SDS), size exclusion chromatography (SEC), dynamic light scattering (DLS), and flow imaging analysis. This paper is intended to provide relevant details of NIST's size heterogeneity control strategy to facilitate implementation of the NISTmAb as a test molecule in the end user's laboratory. Graphical abstract Representative size exclusion chromatogram of the NIST monoclonal antibody (NISTmAb). The NISTmAb is a publicly available research tool intended to facilitate advancement of biopharmaceutical analytics. HMW = high molecular weight (trimer and dimer), LMW = low molecular weight (2 fragment peaks). Peak labeled buffer is void volume of the column from L-histidine background buffer.

AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures

Protein aggregation is responsible for the onset and spread of many human diseases, ranging from neurodegenerative disorders to cancer and diabetes. Moreover, it is one of the major bottlenecks for the production of protein-based therapeutics such as antibodies or enzymes. AGGRESCAN3D (A3D) is a web server aimed to identify and evaluate structural aggregation prone regions, overcoming the limitations of sequence-based algorithms in the prediction of the aggregation propensity of globular proteins. A3D allows the redesign of protein solubility by predicting in silico the impact of mutations and protein conformational fluctuations on the aggregation of native polypeptides.

Insights into protein misfolding and aggregation enabled by solid-state NMR spectroscopy

The aggregation of proteins and peptides into a variety of insoluble, and often non-native, aggregated states plays a central role in many devastating diseases. Analogous processes undermine the efficacy of polypeptide-based biological pharmaceuticals, but are also being leveraged in the design of biologically inspired self-assembling materials. This Trends article surveys the essential contributions made by recent solid-state NMR (ssNMR) studies to our understanding of the structural features of polypeptide aggregates, and how such findings are informing our thinking about the molecular mechanisms of misfolding and aggregation. A central focus is on disease-related amyloid fibrils and oligomers involved in neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntington's disease. SSNMR-enabled structural and dynamics-based findings are surveyed, along with a number of resulting emerging themes that appear common to different amyloidogenic proteins, such as their compact alternating short-β-strand/β-arc amyloid core architecture. Concepts, methods, future prospects and challenges are discussed.

Sequence composition predicts immunoglobulin superfamily members that could share the intrinsically disordered properties of antibody CH1 domains

Antibodies are central to the growing sector of protein therapeutics, and increasingly they are being manipulated as fragments and combinations. An improved understanding of the properties of antibody domains in isolation would aid in their engineering. We have conducted an analysis of sequence and domain interactions for IgG antibodies and Fab fragments in the structural database. Of sequence-related properties studied, relative lysine to arginine content was found to be higher in CH1 and CL than in variable domains. As earlier work shows that lysine is favoured over arginine in more soluble proteins, this suggests that individual domains may not be optimised for greater solubility, giving scope for fragment engineering. Across other sequence-based features, CH1 is anomalous. A sequence-based scheme predicts CH1 to be folded, although it is known that CH1 folding is linked to IgG assembly and secretion. Calculations indicate that charge interactions in CH1 domains contribute less to folded state stability than in other Fab domains. Expanding to the immunoglobulin superfamily reveals that a subset of non-antibody domains shares sequence composition properties with CH1, leading us to suggest that some of these may also couple folding, assembly and secretion.

Microfluidic-Mass Spectrometry Interfaces for Translational Proteomics

Interfacing mass spectrometry (MS) with microfluidic chips (μchip-MS) holds considerable potential to transform a clinician's toolbox, providing translatable methods for the early detection, diagnosis, monitoring, and treatment of noncommunicable diseases by streamlining and integrating laborious sample preparation workflows on high-throughput, user-friendly platforms. Overcoming the limitations of competitive immunoassays - currently the gold standard in clinical proteomics - μchip-MS can provide unprecedented access to complex proteomic assays having high sensitivity and specificity, but without the labor, costs, and complexities associated with conventional MS sample processing. This review surveys recent μchip-MS systems for clinical applications and examines their emerging role in streamlining the development and translation of MS-based proteomic assays by alleviating many of the challenges that currently inhibit widespread clinical adoption.

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

The production of active pharmaceutical ingredients (APIs) is currently undergoing its biggest transformation in a century. The changes are based on the rapid and dramatic introduction of protein- and macromolecule-based drugs (collectively known as biopharmaceuticals) and can be traced back to the huge investment in biomedical science (in particular in genomics and proteomics) that has been ongoing since the 1970s. Biopharmaceuticals (or biologics) are manufactured using biological-expression systems (such as mammalian, bacterial, insect cells, etc.) and have spawned a large (>€35 billion sales annually in Europe) and growing biopharmaceutical industry (BioPharma). The structural and chemical complexity of biologics, combined with the intricacy of cell-based manufacturing, imposes a huge analytical burden to correctly characterize and quantify both processes (upstream) and products (downstream). In small molecule manufacturing, advances in analytical and computational methods have been extensively exploited to generate process analytical technologies (PAT) that are now used for routine process control, leading to more efficient processes and safer medicines. In the analytical domain, biologic manufacturing is considerably behind and there is both a huge scope and need to produce relevant PAT tools with which to better control processes, and better characterize product macromolecules. Raman spectroscopy, a vibrational spectroscopy with a number of useful properties (nondestructive, non-contact, robustness) has significant potential advantages in BioPharma. Key among them are intrinsically high molecular specificity, the ability to measure in water, the requirement for minimal (or no) sample pre-treatment, the flexibility of sampling configurations, and suitability for automation. Here, we review and discuss a representative selection of the more important Raman applications in BioPharma (with particular emphasis on mammalian cell culture). The review shows that the properties of Raman have been successfully exploited to deliver unique and useful analytical solutions, particularly for online process monitoring. However, it also shows that its inherent susceptibility to fluorescence interference and the weakness of the Raman effect mean that it can never be a panacea. In particular, Raman-based methods are intrinsically limited by the chemical complexity and wide analyte-concentration-profiles of cell culture media/bioprocessing broths which limit their use for quantitative analysis. Nevertheless, with appropriate foreknowledge of these limitations and good experimental design, robust analytical methods can be produced. In addition, new technological developments such as time-resolved detectors, advanced lasers, and plasmonics offer potential of new Raman-based methods to resolve existing limitations and/or provide new analytical insights.

Viscosity-Reducing Bulky-Salt Excipients Prevent Gelation of Protein, but Not Carbohydrate, Solutions

The problem of gelation of concentrated protein solutions, which poses challenges for both downstream protein processing and liquid formulations of pharmaceutical proteins, is addressed herein by employing previously discovered viscosity-lowering bulky salts. Procainamide-HCl and the salt of camphor-10-sulfonic acid with L-arginine (CSA-Arg) greatly retard gelation upon heating and subsequent cooling of the model proteins gelatin and casein in water: Whereas in the absence of additives the proteins form aqueous gels within several hours at room temperature, procainamide-HCl for both proteins and also CSA-Arg for casein prevent gel formation for months under the same conditions. The inhibition of gelation by CSA-Arg stems exclusively from the CSA moiety: CSA-Na was as effective as CSA-Arg, while Arg-HCl was marginally or not effective. The tested bulky salts did not inhibit (and indeed accelerated) temperature-induced gel formation in aqueous solutions of all examined carbohydrates-starch, agarose, alginate, gellan gum, and carrageenan.

Nanoparticle tracking analysis of particle size and concentration detection in suspensions of polymer and protein samples: Influence of experimental and data evaluation parameters

Nanoparticle Tracking Analysis (NTA) is an emerging technique for detecting simultaneously sub-micron particle size distributions and particle concentrations of a sample. This study deals with the performance evaluation for the detection and characterisation of various particles by NTA. Our investigation focusses on the NTA measurement parameter set-ups, as will be shown in this study, are very crucial parameters to correctly analyse and interpret the data. In order to achieve this, we used (i) polystyrene standard particles as well as (ii) protein particles. We show the highly precise and reproducible detection of particle size and concentration in monodisperse polystyrene particle systems, under specified and constant parameter settings. On the other hand, our results exemplify potential risks and errors while setting inadequate parameters with regards to the results and thus interpretation thereof. In particular changes of the parameters, camera level (CL) and detection threshold (DT), led to significant changes in the determined particle concentration. We propose defined and specified "optimal" camera levels for monodisperse particle suspension characterisations in the size range of 20-1000nm. We illustrate that the results of polydisperse polystyrene standard particle solution measurements, highly depend on the used parameter settings, which are rarely published with the data. Changes in these settings led to the "appearance" or "disappearance" of particle populations ("peaks") for polydisperse systems. Thus, a correct evaluation of the particle size populations in the sample becomes very challenging. For the use of NTA in biopharmaceutical analysis, proteinaceous samples were investigated. We analysed protein particle suspensions and compared unstressed and stressed (formation of aggregates) protein samples similar to polystyrene particle analysis. We also studied these samples in two different measuring modes (general capture mode and live monitoring mode) that the commercially available analysis software is offering. Our results stated the live monitoring mode as more suitable for protein samples, as the results were more reproducible and less operator-depending. In conclusion, NTA is a potential technique and unique in quantitative evaluation of particle suspensions in the subvisible size range, especially for monodisperse suspensions. We strongly urge on not underestimating the influence of the measuring parameters on the obtained results, which should be presented with the data in order to better judge and interpret the NTA results.

ESI-IMS-MS: A method for rapid analysis of protein aggregation and its inhibition by small molecules

Electrospray ionisation-ion mobility spectrometry-mass spectrometry (ESI-IMS-MS) is a powerful method for the study of conformational changes in protein complexes, including oligomeric species populated during protein self-aggregation into amyloid fibrils. Information on the mass, stability, cross-sectional area and ligand binding capability of each transiently populated intermediate, present in the heterogeneous mixture of assembling species, can be determined individually in a single experiment in real-time. Determining the structural characterisation of oligomeric species and alterations in self-assembly pathways observed in the presence of small molecule inhibitors is of great importance, given the urgent demand for effective therapeutics. Recent studies have demonstrated the capability of ESI-IMS-MS to identify small molecule modulators of amyloid assembly and to determine the mechanism by which they interact (positive, negative, non-specific binding, or colloidal) in a high-throughput format. Here, we demonstrate these advances using self-assembly of Aβ40 as an example, and reveal two new inhibitors of Aβ40 fibrillation.

Zinc Modulates Self-Assembly of Bacillus thermocatenulatus Lipase

Thermoalkalophilic lipases are prone to aggregation from their dimer interface to which structural zinc is very closely located. Structural zinc sites have been shown to induce protein aggregation, but the interaction between zinc and aggregation tendency in thermoalkalophilic lipases remains elusive. Here we delineate the interplay between zinc and aggregation of the lipase from Bacillus thermocatenulatus (BTL2), which is taken to be a representative of thermoalkalophilic lipase. Results showed that zinc removal disrupted the BTL2 dimer, leading to monomer formation and reduced thermostability manifesting as a link between zinc and dimerization that leads to thermostability, while zinc addition induced aggregation. Biochemical and kinetic characterizations of zinc-induced aggregates showed that the aggregates obtained from the early and late stages of aggregation had differential characteristics. In the early stages, the aggregates were soluble and possessed native-like structures, while in the late stages, the aggregates became insoluble and showed fibrillar characteristics with binding affinities for Congo red and thioflavin T. The impact of temperature on zinc-induced aggregation was further investigated, and it was found that the native-like early aggregates could completely dissociate into functional lipase forms at high temperatures while dissociation of the late aggregates was limited. To this end, we report that the zinc-induced aggregation of BTL2 can be reversed by temperature switches and initiated by ordered aggregates in the early stages that gain fibrillar-like features over time. Insights revealed by this work contributes to the knowledge of aggregation mechanisms that exist in thermophilic proteins, reflecting the potential use of metal addition and/or removal to fine-tune aggregation tendency.