Using protein engineering to understand and modulate 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.

Prediction of protein aggregation propensity employing SqFt-based logistic regression model

Here we present a novel machine-learning approach to predict protein aggregation propensity (PAP) which is a key factor in the formation of amyloid fibrils based on logistic regression (LR). Amyloid fibrils are associated with various neurodegenerative diseases (ND) such as Alzheimer's disease (AD) and Parkinson's disease (PD), which are caused by oxidative stress and impaired protein homeostasis. Accordingly, the paper uses a dataset of hexapeptides with known aggregation tendencies and eight physiochemical features to train and test the LR model. Also, it evaluates the performance of the LR model using F-measure and Matthews correlation coefficient (MCC) as metrics and compares it with other existing methods. Moreover, it investigates the effect of combining sequence and feature information in the prediction. In conclusion, the LR model with sequence and feature information achieves high F-measure (0.841) and MCC (0.6692), outperforming other methods and demonstrating its efficiency and reliability for PAP prediction. In addition, the overall performance of the concluded method was higher than the other known servers, for instance, Aggrescan, Metamyl, Foldamyloid, and PASTA 2.0. The LR model can be accessed at: https://github.com/KatherineEshari/Protein-aggregation-prediction.

Decreasing hydrophobicity or shielding hydrophobic areas of CH2 attenuates low pH-induced IgG4 aggregation

Protein aggregation is a major challenge in the development of therapeutic monoclonal antibodies (mAbs). Several stressors can cause protein aggregation, including temperature shifts, mechanical forces, freezing-thawing cycles, oxidants, reductants, and extreme pH. When antibodies are exposed to low pH conditions, aggregation increases dramatically. However, low pH treatment is widely used in protein A affinity chromatography and low pH viral inactivation procedures. In the development of an IgG4 subclass antibody, mAb1-IgG4 showed a strong tendency to aggregate when temporarily exposed to low pH conditions. Our findings showed that the aggregation of mAb1-IgG4 under low pH conditions is determined by the stability of the Fc. The CH2 domain is the least stable domain in mAb1-IgG4. The L309E, Q311D, and Q311E mutations in the CH2 domain significantly reduced the aggregation propensity, which could be attributed to a reduction in the hydrophobicity of the CH2 domain. Protein stabilizers, such as sucrose and mannose, could also attenuate low pH-induced mAb1-IgG4 aggregation by shielding hydrophobic areas and increasing protein stability. Our findings provide valuable strategies for managing the aggregation of protein therapeutics with a human IgG4 backbone.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Controlling amyloid formation of intrinsically disordered proteins and peptides: slowing down or speeding up?

The pathological assembly of intrinsically disordered proteins/peptides (IDPs) into amyloid fibrils is associated with a range of human pathologies, including neurodegeneration, metabolic diseases and systemic amyloidosis. These debilitating disorders affect hundreds of millions of people worldwide, and the number of people affected is increasing sharply. However, the discovery of therapeutic agents has been immensely challenging largely because of (i) the diverse number of aggregation pathways and the multi-conformational and transient nature of the related proteins or peptides and (ii) the under-development of experimental pipelines for the identification of disease-modifying molecules and their mode-of-action. Here, we describe current approaches used in the search for small-molecule modulators able to control or arrest amyloid formation commencing from IDPs and review recently reported accelerators and inhibitors of amyloid formation for this class of proteins. We compare their targets, mode-of-action and effects on amyloid-associated cytotoxicity. Recent successes in the control of IDP-associated amyloid formation using small molecules highlight exciting possibilities for future intervention in protein-misfolding diseases, despite the challenges of targeting these highly dynamic precursors of amyloid assembly.

Developability Assessments of Monoclonal Antibody Candidates to Minimize Aggregation During Large-Scale Ultrafiltration and Diafiltration (UF-DF) Processing

Therapeutic proteins are subjected to a variety of stresses during manufacturing, storage or administration, that often lead to undesired protein aggregation and particle formation. Ultrafiltration-diafiltration (UF-DF) processing of monoclonal antibodies (mAbs) is one such manufacturing step that has been shown to result in such physical degradation. In this work, we explore the use of different analytical techniques and lab-scale setups as methodologies to predict and rank-order the aggregation potential of four different mAbs during large-scale UF-DF processing. In the first part of the study, a suite of biophysical techniques was applied to assess differences in their inherent bulk protein properties including conformational and colloidal stability in a PBS buffer. Additionally, the inherent interfacial properties of these mAbs in PBS were measured using a Langmuir trough technique. In the next part of the study, several different scale-down lab models were evaluated including a lab bench-scale UF-DF setup, mechanical stress (shaking/stirring) studies in vials, and application of interfacial dilatational stress using a Langmuir trough to assess protein particle formation in different UF-DF processing buffers. Taken together, our results demonstrate the ability of a Langmuir-trough methodology to accurately predict the mAb instability profile observed during large scale UF-DF processing.

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

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

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

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

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

Observation of Oligomeric States Indicates a High Structural Flexibility Required for the Onset of Polyglutamine Fibrillization

Polyglutamine (polyQ) diseases are caused by misfolding and aggregation of expanded polyQ tracts in the affected protein. PolyQ fibrils have been studied in detail; however, less is known about oligomeric precursor states. By a combination of time-resolved temperature-jump (T-jump) infrared (IR) spectroscopy and an appropriately tailored polyQ model peptide, we succeeded in disentangling conformational dynamics in the heterogeneous ensemble of states evolving during aggregation. Individual structural elements could be differentiated by IR-specific signatures, i.e., hairpin monomers, β-structured oligomers, and disordered structure. Submillisecond dynamics were observed for early oligomeric states in contrast to the slow dynamics of fibril growth. We propose that a high structural flexibility of oligomers is required to initiate fibril formation, but not after a fibrillar structure has consolidated and the fibril just grows. Our study reveals that structural flexibility changes at different stages in the aggregation process, from fibril initiation to fibril growth.

Generating Ensembles of Dynamic Misfolding Proteins

The early stages of protein misfolding and aggregation involve disordered and partially folded protein conformers that contain a high degree of dynamic disorder. These dynamic species may undergo large-scale intra-molecular motions of intrinsically disordered protein (IDP) precursors, or flexible, low affinity inter-molecular binding in oligomeric assemblies. In both cases, generating atomic level visualization of the interconverting species that captures the conformations explored and their physico-chemical properties remains hugely challenging. How specific sub-ensembles of conformers that are on-pathway to aggregation into amyloid can be identified from their aggregation-resilient counterparts within these large heterogenous pools of rapidly moving molecules represents an additional level of complexity. Here, we describe current experimental and computational approaches designed to capture the dynamic nature of the early stages of protein misfolding and aggregation, and discuss potential challenges in describing these species because of the ensemble averaging of experimental restraints that arise from motions on the millisecond timescale. We give a perspective of how machine learning methods can be used to extract aggregation-relevant sub-ensembles and provide two examples of such an approach in which specific interactions of defined species within the dynamic ensembles of α-synuclein (αSyn) and β2-microgloblulin (β2m) can be captured and investigated.

Protocols for Rational Design of Protein Solubility and Aggregation Properties Using Aggrescan3D Standalone

Protein aggregation is a major hurdle in the development and manufacturing of protein-based therapeutics. Development of aggregation-resistant and stable protein variants can be guided by rational redesign using computational tools. Here, we describe the architecture and functionalities of the Aggrescan3D (A3D) standalone package for the rational design of protein solubility and aggregation properties based on three-dimensional protein structures. We present the case studies of the three therapeutic proteins, including antibodies, exploring the practical use of the A3D standalone tool. The case studies demonstrate that protein solubility can be easily improved by the A3D prediction of non-destabilizing amino acid mutations at the protein surfaces.

Expanding the toolbox for predictive parameters describing antibody stability considering thermodynamic and kinetic determinants

PURPOSE

Introduction of the activation energy (E_a) as a kinetic parameter to describe and discriminate monoclonal antibody (mAb) stability.

METHODS

E_a is derived from intrinsic fluorescence (IF) unfolding thermograms. An apparent irreversible three-state fit model based on the Arrhenius integral is developed to determine E_a of respective unfolding transitions. These activation energies are compared to the thermodynamic parameter of van´t Hoff enthalpies (∆H_vh). Using a set of 34 mAbs formulated in four different formulations, both the apparent thermodynamic and kinetic parameters together with apparent melting temperatures are correlated collectively with each other to storage stabilities to evaluate its predictive power with respect to long-term effects potentially reflected in shelf-life.

RESULTS

E_a allows for the discrimination of (i) different parent mAbs, (ii) different variants that originate from parent mAbs, and (iii) different formulations. Interestingly, we observed that the E_a of the CH₂ unfolding transition shows strongest correlations with monomer and aggregate content after storage at accelerated and stress conditions when collectively compared to ∆H_vh and T_m of the CH₂ transition. Moreover, the predictive parameters determined for the CH₂ domain show generally stronger correlations with monomer and aggregate content than those derived for the Fab. Qualitative assessment by ranking E_a of the Fab domain showed good agreement with monomer content in storage stabilities of individual mAb sub-sets.

CONCLUSION

E_a from IF unfolding transitions can be used in addition to other commonly used thermodynamic predictive parameters to discriminate and characterize thermal stability of different mAbs in different formulations. Hence, it shows great potential for antibody engineering and formulation scientists.

Characterization of the structural forces governing the reversibility of the thermal unfolding of the human acidic fibroblast growth factor

Human acidic fibroblast growth factor (hFGF1) is an all beta-sheet protein that is involved in the regulation of key cellular processes including cell proliferation and wound healing. hFGF1 is known to aggregate when subjected to thermal unfolding. In this study, we investigate the equilibrium unfolding of hFGF1 using a wide array of biophysical and biochemical techniques. Systematic analyses of the thermal and chemical denaturation data on hFGF1 variants (Q54P, K126N, R136E, K126N/R136E, Q54P/K126N, Q54P/R136E, and Q54P/K126N/R136E) indicate that nullification of charges in the heparin-binding pocket can significantly increase the stability of wtFGF1. Triple variant (Q54P/K126N/R136E) was found to be the most stable of all the hFGF1 variants studied. With the exception of triple variant, thermal unfolding of wtFGF1 and the other variants is irreversible. Thermally unfolded triple variant refolds completely to its biologically native conformation. Microsecond-level molecular dynamic simulations reveal that a network of hydrogen bonds and salt bridges linked to Q54P, K126N, and R136E mutations, are responsible for the high stability and reversibility of thermal unfolding of the triple variant. In our opinion, the findings of the study provide valuable clues for the rational design of a stable hFGF1 variant that exhibits potent wound healing properties.

Exploring amyloid oligomers with peptide model systems

The assembly of amyloidogenic peptides and proteins, such as the β-amyloid peptide, α-synuclein, huntingtin, tau, and islet amyloid polypeptide, into amyloid fibrils and oligomers is directly linked to amyloid diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, frontotemporal dementias, and type II diabetes. Although amyloid oligomers have emerged as especially important in amyloid diseases, high-resolution structures of the oligomers formed by full-length amyloidogenic peptides and proteins have remained elusive. Investigations of oligomers assembled from fragments or stabilized β-hairpin segments of amyloidogenic peptides and proteins have allowed investigators to illuminate some of the structural, biophysical, and biological properties of amyloid oligomers. Here, we summarize recent advances in the application of these peptide model systems to investigate and understand the structures, biological properties, and biophysical properties of amyloid oligomers.

Role of CNC1 gene in TDP-43 aggregation-induced oxidative stress-mediated cell death in S. cerevisiae model of ALS

TDP-43 protein is found deposited as inclusions in the amyotrophic lateral sclerosis (ALS) patient's brain. The mechanism of neuron death in ALS is not fully deciphered but several TDP-43 toxicity mechanisms such as mis-regulation of autophagy, mitochondrial impairment and generation of oxidative stress etc., have been implicated. A predominantly nuclear protein, Cyclin C, can regulate the oxidative stress response via transcription of stress response genes and also by translocation to the cytoplasm for the activation of mitochondrial fragmentation-dependent cell death pathway. Using the well-established yeast TDP-43 proteinopathy model, we examined here whether upon TDP-43 aggregation, cell survival depends on the CNC1 gene that encodes the Cyclin C protein or other genes which encode proteins that function in conjunction with Cyclin C, such as DNM1, FIS1 and MED13. We show that the TDP-43's toxicity is significantly reduced in yeast deleted for CNC1 or DNM1 genes and remains unaltered by deletions of genes, FIS1 and MED13. Importantly, this rescue is observed only in presence of functional mitochondria. Also, deletion of the YBH3 gene involved in the mitochondria-dependent apoptosis pathway reduced the TDP-43 toxicity. Deletion of the VPS1 gene involved in the peroxisomal fission pathway did not mitigate the TDP-43 toxicity. Strikingly, Cyclin C-YFP was observed to relocate to the cytoplasm in response to TDP-43's co-expression which was prevented by addition of an anti-oxidant molecule, N-acetyl cysteine. Overall, the Cyclin C, Dnm1 and Ybh3 proteins are found to be important players in the TDP-43-induced oxidative stress-mediated cell death in the S. cerevisiae model.

Acceleration of α-synuclein fibril formation and associated cytotoxicity stimulated by silica nanoparticles as a model of neurodegenerative diseases

A wide range of biophysical and theoretical analysis were employed to explore the formation of (α-syn) amyloid fibril formation as a model of Parkinson's disease in the presence of silica oxide nanoparticles (SiO₂ NPs). Also, different cellular and molecular assays such as MTT, LDH, caspase, ROS, and qPCR were performed to reveal the α-syn amyloid fibrils-associated cytotoxicity against SH-SY5Y cells. Fluorescence measurements showed that SiO₂ NPs accelerate the α-syn aggregation and exposure of hydrophobic moieties. Congo red absorbance, circular dichroism (CD), and transmission electron microscopy (TEM) analysis depicted the SiO₂ NPs accelerated the formation of α-syn amyloid fibrils. Molecular docking study showed that SiO₂ clusters preferably bind to the N-terminal of α-syn as the helix folding site. We also realized that SiO₂ NPs increase the cytotoxicity of α-syn amyloid fibrils through a significant decrease in cell viability, increase in membrane leakage, activation of caspase-9 and -3, elevation of ROS, and increase in the ratio of Bax/Bcl2 mRNA. The cellular assay indicated that α-syn amyloid fibrils formed in the presence of SiO₂ NPs induce their cytotoxic effects through the mitochondrial-mediated intrinsic apoptosis pathway. We concluded that these data may reveal some adverse effects of NPs on the progression of Parkinson's disease.

Looking Beyond the Core: The Role of Flanking Regions in the Aggregation of Amyloidogenic Peptides and Proteins

Amyloid proteins are involved in many neurodegenerative disorders such as Alzheimer’s disease [Tau, Amyloid β (Aβ)], Parkinson’s disease [alpha-synuclein (αSyn)], and amyotrophic lateral sclerosis (TDP-43). Driven by the early observation of the presence of ordered structure within amyloid fibrils and the potential to develop inhibitors of their formation, a major goal of the amyloid field has been to elucidate the structure of the amyloid fold at atomic resolution. This has now been achieved for a wide variety of sequences using solid-state NMR, microcrystallography, X-ray fiber diffraction and cryo-electron microscopy. These studies, together with in silico methods able to predict aggregation-prone regions (APRs) in protein sequences, have provided a wealth of information about the ordered fibril cores that comprise the amyloid fold. Structural and kinetic analyses have also shown that amyloidogenic proteins often contain less well-ordered sequences outside of the amyloid core (termed here as flanking regions) that modulate function, toxicity and/or aggregation rates. These flanking regions, which often form a dynamically disordered “fuzzy coat” around the fibril core, have been shown to play key parts in the physiological roles of functional amyloids, including the binding of RNA and in phase separation. They are also the mediators of chaperone binding and membrane binding/disruption in toxic amyloid assemblies. Here, we review the role of flanking regions in different proteins spanning both functional amyloid and amyloid in disease, in the context of their role in aggregation, toxicity and cellular (dys)function. Understanding the properties of these regions could provide new opportunities to target disease-related aggregation without disturbing critical biological functions.

Visualizing and trapping transient oligomers in amyloid assembly pathways

Oligomers which form during amyloid fibril assembly are considered to be key contributors towards amyloid disease. However, understanding how such intermediates form, their structure, and mechanisms of toxicity presents significant challenges due to their transient and heterogeneous nature. Here, we discuss two different strategies for addressing these challenges: use of (1) methods capable of detecting lowly-populated species within complex mixtures, such as NMR, single particle methods (including fluorescence and force spectroscopy), and mass spectrometry; and (2) chemical and biological tools to bias the amyloid energy landscape towards specific oligomeric states. While the former methods are well suited to following the kinetics of amyloid assembly and obtaining low-resolution structural information, the latter are capable of producing oligomer samples for high-resolution structural studies and inferring structure-toxicity relationships. Together, these different approaches should enable a clearer picture to be gained of the nature and role of oligomeric intermediates in amyloid formation and disease.

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Methods to study structure, toxicity, and kinetics of transient amyloid oligomers.

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NMR and single particle methods can characterize lowly-populated oligomers.

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Chemical tools/antibodies stabilize oligomers for structural and toxicity studies

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A combination of methods is needed to fully characterize amyloid assembly pathways.

Relevance of Electrostatic Charges in Compactness, Aggregation, and Phase Separation of Intrinsically Disordered Proteins

The abundance of intrinsic disorder in the protein realm and its role in a variety of physiological and pathological cellular events have strengthened the interest of the scientific community in understanding the structural and dynamical properties of intrinsically disordered proteins (IDPs) and regions (IDRs). Attempts at rationalizing the general principles underlying both conformational properties and transitions of IDPs/IDRs must consider the abundance of charged residues (Asp, Glu, Lys, and Arg) that typifies these proteins, rendering them assimilable to polyampholytes or polyelectrolytes. Their conformation strongly depends on both the charge density and distribution along the sequence (i.e., charge decoration) as highlighted by recent experimental and theoretical studies that have introduced novel descriptors. Published experimental data are revisited herein in the frame of this formalism, in a new and possibly unitary perspective. The physicochemical properties most directly affected by charge density and distribution are compaction and solubility, which can be described in a relatively simplified way by tools of polymer physics. Dissecting factors controlling such properties could contribute to better understanding complex biological phenomena, such as fibrillation and phase separation. Furthermore, this knowledge is expected to have enormous practical implications for the design, synthesis, and exploitation of bio-derived materials and the control of natural biological processes.

Aggregation and Amyloidogenicity of the Nuclear Coactivator Binding Domain of CREB-Binding Protein

The nuclear coactivator binding domain (NCBD) of transcriptional co-regulator CREB-binding protein (CBP) is an example of conformationally malleable proteins that can bind to structurally unrelated protein targets and adopt distinct folds in the respective protein complexes. Here, we show that the folding landscape of NCBD contains an alternative pathway that results in protein aggregation and self-assembly into amyloid fibers. The initial steps of such protein misfolding are driven by intermolecular interactions of its N-terminal α-helix bringing multiple NCBD molecules into contact. These oligomers then undergo slow but progressive interconversion into β-sheet-containing aggregates. To reveal the concealed aggregation potential of NCBD we used a chemically synthesized mirror-image d-NCBD form. The addition of d-NCBD promoted self-assembly into amyloid precipitates presumably due to formation of thermodynamically more stable racemic β-sheet structures. The unexpected aggregation of NCBD needs to be taken into consideration given the multitude of protein-protein interactions and resulting biological functions mediated by CBP.

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

Protein biopharmaceuticals are highly successful, but their utility is compromised by their propensity to aggregate during manufacture and storage. As aggregation can be triggered by non-native states, whose population is not necessarily related to thermodynamic stability, prediction of poorly-behaving biologics is difficult, and searching for sequences with desired properties is labour-intensive and time-consuming. Here we show that an assay in the periplasm of E. coli linking aggregation directly to antibiotic resistance acts as a sensor for the innate (un-accelerated) aggregation of antibody fragments. Using this assay as a directed evolution screen, we demonstrate the generation of aggregation resistant scFv sequences when reformatted as IgGs. This powerful tool can thus screen and evolve ‘manufacturable’ biopharmaceuticals early in industrial development. By comparing the mutational profiles of three different immunoglobulin scaffolds, we show the applicability of this method to investigate protein aggregation mechanisms important to both industrial manufacture and amyloid disease.

Protein aggregation remains a significant challenge for manufacturing of protein biopharmaceuticals. Here, the authors demonstrate the use of directed evolution and an assay for in vivo innate protein aggregation-propensity to generate aggregation-resistant scFv fragments.