Computational methods to predict therapeutic protein aggregation

Stability of Protein Pharmaceuticals: Recent Advances

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

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.

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.

Rational Design and Production of Bioactive Analogs of Recombinant Human Keratinocyte Growth Factor (rhKGF) with Reduced Aggregation Propensity

Recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The present study aimed to reduce aggregation propensity of rhKGF by engineering the aggregation hotspots. Initially, 21 mutants were designed based on the previously-identified aggregation-prone regions (APRs) and then four of them including mutants No. 4 (L91K, I119K), 7 (V13S, L91K), 14 (L91D, I119D), and 21 (A51E) were selected based on molecular dynamics (MD) simulations for further experimental studies. The recombinantly produced rhKGF and mutants were analyzed regarding secondary structure, thermal stability, aggregation propensity, and biological activity. Far-UV CD spectroscopy showed that the mutants have similar secondary structure with rhKGF. A51E mutant showed enhanced stability and decreased monomer loss under heat stress suggesting its reduced aggregation propensity compared to rhKGF. Mutant No. 14 showed higher stability and less aggregation tendency than mutant No. 4 indicating that only mutations decreasing pI of rhKGF are effective in reducing its aggregation tendency. All of the mutants were at least as potent as rhKGF in stimulating proliferation of MCF-7 epithelial cells. Our results identified A51E as an equally potent, more stable, and less aggregation-prone analog of rhKGF which could be a promising alternative drug candidate for the commercially available rhKGF (Palifermin).

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

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

Exploring the sequence features determining amyloidosis in human antibody light chains

The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, β-propensity, etc.) calculated for the CDR, FR or V_L regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called "V_LAmY-Pred" ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.

Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets

Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.

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

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

Potential aggregation hot spots in recombinant human keratinocyte growth factor: a computational study

The recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The high aggregation liability of rhKGF is manifested by loss of the monomeric state, and accumulation of the aggregated species even at moderate temperatures. Here, we analyzed the rhKGF for its vulnerability toward aggregation by detection of aggregation-prone regions (APRs) using several sequence-based computational tools including TANGO, ZipperDB, AGGRESCAN, Zyggregator, Camsol, PASTA, SALSA, WALTZ, SODA, Amylpred, AMYPDB, and structure-based tools including SolubiS, CamSol structurally corrected, Aggrescan3D and spatial aggregation propensity (SAP) algorithm. The sequence-based prediction of APRs in rhKGF indicated that they are mainly located at positions 10-30, 40-60, 61-66, 88-120, and 130-140. Mapping on the rhKGF structure revealed that most of these residues including F16-R25, I43, E45, R47-I56, F61, Y62, N66, L88-E91, E108-F110, A112, N114, T131, and H133-T140 are surface-exposed in the native state which can promote aggregation without major unfolding event, or the conformational change may occur in the oligomers. The other regions are buried in the native state and their contribution to non-native aggregation is mediated by a preceding unfolding event. The structure-based prediction of APRs using the SAP tool limited the number of identified APRs to the dynamically-exposed hydrophobic residues including V12, A50, V51, L88, I89, L90, I118, L135, and I139 mediating the native-state aggregation. Our analysis of APRs in rhKGF identified the regions determining the intrinsic aggregation propensity of the rhKGF which are the candidate positions for engineering the rhKGF to reduce its aggregation tendency.Communicated by Ramaswamy H. Sarma.

Local environment effects on charged mutations for developing aggregation-resistant monoclonal antibodies

Protein aggregation is a major concern in biotherapeutic applications of monoclonal antibodies. Introducing charged mutations is among the promising strategies to improve aggregation resistance. However, the impact of such mutations on solubilizing activity depends largely on the inserting location, whose mechanism is still not well understood. Here, we address this issue from a solvation viewpoint, and this is done by analyzing how the change in solvation free energy upon charged mutation is composed of individual contributions from constituent residues. To this end, we perform molecular dynamics simulations for a number of antibody mutants and carry out the residue-wise decomposition of the solvation free energy. We find that, in addition to the previously identified “global” principle emphasizing the key role played by the protein total net charge, a local net charge within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼15 Å from the mutation site exerts significant effects. For example, when the net charge of an antibody is positive, the global principle states that introducing a positively charged mutation will lead to more favorable solvation. Our finding further adds that an even more optimal mutation can be done at the site around which more positively charged residues and fewer negatively charged residues are present. Such a “local” design principle accounts for the location dependence of charged mutations, and will be useful in producing aggregation-resistant antibodies.

Toward Biotherapeutics Formulation Composition Engineering using Site-Identification by Ligand Competitive Saturation (SILCS)

Formulation of protein-based therapeutics employ advanced formulation and analytical technologies for screening various parameters such as buffer, pH, and excipients. At a molecular level, physico-chemical properties of a protein formulation depend on self-interaction between protein molecules, protein-solvent and protein-excipient interactions. This work describes a novel in silico approach, SILCS-Biologics, for structure-based modeling of protein formulations. SILCS Biologics is based on the Site-Identification by Ligand Competitive Saturation (SILCS) technology and enables modeling of interactions among different components of a formulation at an atomistic level while accounting for protein flexibility. It predicts potential hotspot regions on the protein surface for protein-protein and protein-excipient interactions. Here we apply SILCS-Biologics on a Fab domain of a monoclonal antibody (mAbN) to model Fab-Fab interactions and interactions with three amino acid excipients, namely, arginine HCl, proline and lysine HCl. Experiments on 100 mg/ml formulations of mAbN showed that arginine increased, lysine reduced, and proline did not impact viscosity. We use SILCS-Biologics modeling to explore a structure-based hypothesis for the viscosity modulating effect of these excipients. Current efforts are aimed at further validation of this novel computational framework and expanding the scope to model full mAb and other protein therapeutics.

Solubility, Stability, and Avidity of Recombinant Antibody Fragments Expressed in Microorganisms

Solubility of recombinant proteins (i.e., the extent of soluble versus insoluble expression in heterogeneous hosts) is the first checkpoint criterion for determining recombinant protein quality. However, even soluble proteins often fail to represent functional activity because of the involvement of non-functional, misfolded, soluble aggregates, which compromise recombinant protein quality. Therefore, screening of solubility and folding competence is crucial for improving the quality of recombinant proteins, especially for therapeutic applications. The issue is often highlighted especially in bacterial recombinant hosts, since bacterial cytoplasm does not provide an optimal environment for the folding of target proteins of mammalian origin. Antibody fragments, such as single-chain variable fragment (scFv), single-chain antibody (scAb), and fragment antigen binding (Fab), have been utilized for numerous applications such as diagnostics, research reagents, or therapeutics. Antibody fragments can be efficiently expressed in microorganisms so that they offer several advantages for diagnostic applications such as low cost and high yield. However, scFv and scAb fragments have generally lower stability to thermal stress than full-length antibodies, necessitating a judicious combination of designer antibodies, and bacterial hosts harnessed with robust chaperone function. In this review, we discuss efforts on not only the production of antibodies or antibody fragments in microorganisms but also scFv stabilization via (i) directed evolution of variants with increased stability using display systems, (ii) stabilization of the interface between variable regions of heavy (V_H) and light (V_L) chains through the introduction of a non-native covalent bond between the two chains, (iii) rational engineering of V_H-V_L pair, based on the structure, and (iv) computational approaches. We also review recent advances in stability design, increase in avidity by multimerization, and maintaining the functional competence of chimeric proteins prompted by various types of chaperones.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Nucleic acids in inclusion bodies obtained from E. coli cells expressing human interferon-gamma

BACKGROUND

Inclusion bodies (IBs) are protein aggregates in recombinant bacterial cells containing mainly the target recombinant protein. Although it has been shown that IBs contain functional proteins along with protein aggregates, their direct application as pharmaceuticals is hindered by their heterogeneity and hazardous contaminants with bacterial origin. Therefore, together with the production of soluble species, IBs remain the main source for manufacture of recombinant proteins with medical application. The quality and composition of the IBs affect the refolding yield and further purification of the recombinant protein. The knowledge whether nucleic acids are genuine components or concomitant impurities of the IBs is a prerequisite for the understanding of the IBs formation and for development of optimized protocols for recombinant protein refolding and purification. IBs isolated from Escherichia coli overexpressing human interferon-gamma (hIFNγ), a protein with therapeutic application, were used as a model.

RESULTS

IBs were isolated from E. coli LE392 cells transformed with a hIFNγ expressing plasmid under standard conditions and further purified by centrifugation on a sucrose cushion, followed by several steps of sonication and washings with non-denaturing concentrations of urea. The efficiency of the purification was estimated by SDS-PAGE gel electrophoresis and parallel microbiological testing for the presence of residual intact bacteria. Phenol/chloroform extraction showed that the highly purified IBs contain both DNA and RNA. The latter were studied by UV spectroscopy and agarose gel electrophoresis combined with enzymatic treatment and hybridization. DNA was observed as a diffuse fraction mainly in the range of 250 to 1000 bp. RNA isolated by TRIzol^® also demonstrated a substantial molecular heterogeneity. Hybridization with ³²P-labelled oligonucleotides showed that the IBs contain rRNA and are enriched of hIFNγ mRNA.

CONCLUSIONS

The results presented in this study indicate that the nucleic acids might be intrinsic components rather than co-precipitated impurities in the IBs. We assume that the nucleic acids are active participants in the aggregation of recombinant proteins and formation of the IBs that originate from the transcription and translation machinery of the microbial cell factory. Further studies are needed to ascertain this notion.

Using protein engineering to understand and modulate aggregation

Protein aggregation occurs through a variety of mechanisms, initiated by the unfolded, non-native, or even the native state itself. Understanding the molecular mechanisms of protein aggregation is challenging, given the array of competing interactions that control solubility, stability, cooperativity and aggregation propensity. An array of methods have been developed to interrogate protein aggregation, spanning computational algorithms able to identify aggregation-prone regions, to deep mutational scanning to define the entire mutational landscape of a protein's sequence. Here, we review recent advances in this exciting and emerging field, focussing on protein engineering approaches that, together with improved computational methods, hold promise to predict and control protein aggregation linked to human disease, as well as facilitating the manufacture of protein-based therapeutics.

A computational approach to get insights into multiple faces of additives in modulation of protein aggregation pathways

An enormous population worldwide is presently confronted with debilitating neurodegenerative diseases. The etiology of the disease is connected to protein aggregation and the events involved therein. Thus, a complete understanding of an inhibitor at different stages in the process is imperative for the formulation of a drug molecule. This review presents a detailed summary of the current status of different cosolvents. It further develops how the complex aggregation pathway can be simplified into three steps common to all proteins and the way computer simulations can be exploited to gain insights into the ways by which known inhibitors can affect all these stages. Computation of theoretical parameters in this regard and their correlation with experimental techniques is accentuated. In addition to providing an outline of the scope of different additives, this review showcases the way by which the problem of analyzing an effect of an additive can be addressed effectively via MD simulations.

Structure, heterogeneity and developability assessment of therapeutic antibodies

Increasing attention has been paid to developability assessment with the understanding that thorough evaluation of monoclonal antibody lead candidates at an early stage can avoid delays during late-stage development. The concept of developability is based on the knowledge gained from the successful development of approximately 80 marketed antibody and Fc-fusion protein drug products and from the lessons learned from many failed development programs over the last three decades. Here, we reviewed antibody quality attributes that are critical to development and traditional and state-of-the-art analytical methods to monitor those attributes. Based on our collective experiences, a practical workflow is proposed as a best practice for developability assessment including in silico evaluation, extended characterization and forced degradation using appropriate analytical methods that allow characterization with limited material consumption and fast turnaround time.

AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches

Protein aggregation is a phenomenon that has attracted considerable attention within the pharmaceutical industry from both a developability standpoint (to ensure stability of protein formulations) and from a research perspective for neurodegenerative diseases. Experimental identification of aggregation behavior in proteins can be expensive; and hence, the development of accurate computational approaches is crucial. The existing methods for predicting protein aggregation rely mostly on the primary sequence and are typically trained on amyloid-like proteins. However, the training bias toward beta amyloid peptides may worsen prediction accuracy of such models when applied to larger protein systems. Here, we present a novel algorithm to identify aggregation-prone regions in proteins termed "AggScore" that is based entirely on three-dimensional structure input. The method uses the distribution of hydrophobic and electrostatic patches on the surface of the protein, factoring in the intensity and relative orientation of the respective surface patches into an aggregation propensity function that has been trained on a benchmark set of 31 adnectin proteins. AggScore can accurately identify aggregation-prone regions in several well-studied proteins and also reliably predict changes in aggregation behavior upon residue mutation. The method is agnostic to an amyloid-specific aggregation context and thus may be applied to globular proteins, small peptides and antibodies.

Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility

The widespread use of monoclonal antibodies for therapeutic applications has led to intense interest in optimizing several of their natural properties (affinity, specificity, stability, solubility and effector functions) as well as introducing non-natural activities (bispecificity and cytotoxicity mediated by conjugated drugs). A common challenge during antibody optimization is that improvements in one property (e.g., affinity) can lead to deficits in other properties (e.g., stability). Here we review recent advances in understanding trade-offs between different antibody properties, including affinity, specificity, stability and solubility. We also review new approaches for co-optimizing multiple antibody properties and discuss how these methods can be used to rapidly and systematically generate antibodies for a wide range of applications.

Hydrodynamic radius coincides with the slip plane position in the electrokinetic behavior of lysozyme

The zeta potential (ζ) is the effective charge energy of a solvated protein, describing the magnitude of electrostatic interactions in solution. It is commonly used in the assessment of adsorption processes and dispersion stability. Predicting ζ from molecular structure would be useful to the structure-based molecular design of drugs, proteins, and other molecules that hold charge-dependent function while remaining suspended in solution. One challenge in predicting ζ is identifying the location of the slip plane (X_SP ), a distance from the protein surface where ζ is theoretically defined. This study tests the hypothesis that the X_SP can be estimated by the Stokes-Einstein hydrodynamic radius (R_h ), using globular hen egg white lysozyme as a model system. Although the X_SP and R_h differ in their theoretical definitions, with the X_SP being the position of the ζ during electrokinetic phenomena (e.g., electrophoresis) and the R_h being a radius pertaining to the edge of solvation during diffusion, they both represent the point where water and ions no longer adhere to a molecule. This work identifies the limited range of ionic strengths in which the X_SP can be determined using diffusivity measurements and the Stokes-Einstein equation. In addition, a computational protocol is developed for determining the ζ from a protein crystal structure. At low ionic strengths, a hyperdiffusivity regime exists, requiring direct measurement of electrophoretic mobility to determine ζ. This work, therefore, supports a basic tenant of EDL theory that the electric double layer during diffusion and electrophoresis are equivalent in the Stokes-Einstein regime.

Fibpredictor: a computational method for rapid prediction of amyloid fibril structures

Amyloid fibrils are important in diseases such as Alzheimer's disease and Parkinson's disease, and are also a common instability in peptide and protein drug products. Despite their importance, experimental structures of amyloid fibrils in atomistic detail are rare. To address this limitation, we have developed a novel, rapid computational method to predict amyloid fibril structures (Fibpredictor). The method combines β-sheet model building, β-sheet replication, and symmetry operations with side-chain prediction and statistical scoring functions. When applied to nine amyloid fibrils with experimentally determined structures, the method predicted the correct structures of amyloid fibrils and enriched those among the top-ranked structures. These models can be used as the initial heuristic structures for more complicated computational studies. Fibpredictor is available at http://nanohub.org/resources/fibpredictor .

Autoimmune Responses to Soluble Aggregates of Amyloidogenic Proteins Involved in Neurodegenerative Diseases: Overlapping Aggregation Prone and Autoimmunogenic regions

Why do patients suffering from neurodegenerative diseases generate autoantibodies that selectively bind soluble aggregates of amyloidogenic proteins? Presently, molecular basis of interactions between the soluble aggregates and human immune system is unknown. By analyzing sequences of experimentally validated T-cell autoimmune epitopes, aggregating peptides, amyloidogenic proteins and randomly generated peptides, here we report overlapping regions that likely drive aggregation as well as generate autoantibodies against the aggregates. Sequence features, that make short peptides susceptible to aggregation, increase their incidence in human T-cell autoimmune epitopes by 4–6 times. Many epitopes are predicted to be significantly aggregation prone (aggregation propensities ≥10%) and the ones containing experimentally validated aggregating regions are enriched in hydrophobicity by 10–20%. Aggregate morphologies also influence Human Leukocyte Antigen (HLA) - types recognized by the aggregating regions containing epitopes. Most (88%) epitopes that contain amyloid fibril forming regions bind HLA-DR, while majority (63%) of those containing amorphous β-aggregating regions bind HLA-DQ. More than two-thirds (70%) of human amyloidogenic proteins contain overlapping regions that are simultaneously aggregation prone and auto-immunogenic. Such regions help clear soluble aggregates by generating selective autoantibodies against them. This can be harnessed for early diagnosis of proteinopathies and for drug/vaccine design against them.

Structural hot spots for the solubility of globular proteins

Natural selection shapes protein solubility to physiological requirements and recombinant applications that require higher protein concentrations are often problematic. This raises the question whether the solubility of natural protein sequences can be improved. We here show an anti-correlation between the number of aggregation prone regions (APRs) in a protein sequence and its solubility, suggesting that mutational suppression of APRs provides a simple strategy to increase protein solubility. We show that mutations at specific positions within a protein structure can act as APR suppressors without affecting protein stability. These hot spots for protein solubility are both structure and sequence dependent but can be computationally predicted. We demonstrate this by reducing the aggregation of human α-galactosidase and protective antigen of Bacillus anthracis through mutation. Our results indicate that many proteins possess hot spots allowing to adapt protein solubility independently of structure and function.

Mutations in aggregation prone regions of recombinant proteins often improve their solubility, although they might cause negative effects on their structure and function. Here, the authors identify proteins hot spots that can be exploited to optimize solubility without compromising stability.

Wetting of nonconserved residue-backbones: A feature indicative of aggregation associated regions of proteins

Aggregation is an irreversible form of protein complexation and often toxic to cells. The process entails partial or major unfolding that is largely driven by hydration. We model the role of hydration in aggregation using "Dehydrons." "Dehydrons" are unsatisfied backbone hydrogen bonds in proteins that seek shielding from water molecules by associating with ligands or proteins. We find that the residues at aggregation interfaces have hydrated backbones, and in contrast to other forms of protein-protein interactions, are under less evolutionary pressure to be conserved. Combining evolutionary conservation of residues and extent of backbone hydration allows us to distinguish regions on proteins associated with aggregation (non-conserved dehydron-residues) from other interaction interfaces (conserved dehydron-residues). This novel feature can complement the existing strategies used to investigate protein aggregation/complexation.

Advances in Antibody Design

The use of monoclonal antibodies as therapeutics requires optimizing several of their key attributes. These include binding affinity and specificity, folding stability, solubility, pharmacokinetics, effector functions, and compatibility with the attachment of additional antibody domains (bispecific antibodies) and cytotoxic drugs (antibody-drug conjugates). Addressing these and other challenges requires the use of systematic design methods that complement powerful immunization and in vitro screening methods. We review advances in designing the binding loops, scaffolds, domain interfaces, constant regions, post-translational and chemical modifications, and bispecific architectures of antibodies and fragments thereof to improve their bioactivity. We also highlight unmet challenges in antibody design that must be overcome to generate potent antibody therapeutics.

Early implementation of QbD in biopharmaceutical development: a practical example

In drug development, the “onus” of the low R&D efficiency has been put traditionally onto the drug discovery process (i.e., finding the right target or “binding” functionality). Here, we show that manufacturing is not only a central component of product success, but also that, by integrating manufacturing and discovery activities in a “holistic” interpretation of QbD methodologies, we could expect to increase the efficiency of the drug discovery process as a whole. In this new context, early risk assessment, using developability methodologies and computational methods in particular, can assist in reducing risks during development in a cost-effective way. We define specific areas of risk and how they can impact product quality in a broad sense, including essential aspects such as product efficacy and patient safety. Emerging industry practices around developability are introduced, including some specific examples of applications to biotherapeutics. Furthermore, we suggest some potential workflows to illustrate how developability strategies can be introduced in practical terms during early drug development in order to mitigate risks, reduce drug attrition and ultimately increase the robustness of the biopharmaceutical supply chain. Finally, we also discuss how the implementation of such methodologies could accelerate the access of new therapeutic treatments to patients in the clinic.

A high-affinity native human antibody neutralizes human cytomegalovirus infection of diverse cell types

Human cytomegalovirus (HCMV) is the most common infection causing poor outcomes among transplant recipients. Maternal infection and transplacental transmission are major causes of permanent birth defects. Although no active vaccines to prevent HCMV infection have been approved, passive immunization with HCMV-specific immunoglobulin has shown promise in the treatment of both transplant and congenital indications. Antibodies targeting the viral glycoprotein B (gB) surface protein are known to neutralize HCMV infectivity, with high-affinity binding being a desirable trait, both to compete with low-affinity antibodies that promote the transmission of virus across the placenta and to displace nonneutralizing antibodies binding nearby epitopes. Using a miniaturized screening technology to characterize secreted IgG from single human B lymphocytes, 30 antibodies directed against gB were previously cloned. The most potent clone, TRL345, is described here. Its measured affinity was 1 pM for the highly conserved site I of the AD-2 epitope of gB. Strain-independent neutralization was confirmed for 15 primary HCMV clinical isolates. TRL345 prevented HCMV infection of placental fibroblasts, smooth muscle cells, endothelial cells, and epithelial cells, and it inhibited postinfection HCMV spread in epithelial cells. The potential utility for preventing congenital transmission is supported by the blockage of HCMV infection of placental cell types central to virus transmission to the fetus, including differentiating cytotrophoblasts, trophoblast progenitor cells, and placental fibroblasts. Further, TRL345 was effective at controlling an ex vivo infection of human placental anchoring villi. TRL345 has been utilized on a commercial scale and is a candidate for clinical evaluation.

In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability

For mAbs to be viable therapeutics, they must be formulated to have low viscosity, be chemically stable, and have normal in vivo clearance rates. We explored these properties by observing correlations of up to 60 different antibodies of the IgG1 isotype. Unexpectedly, we observe significant correlations with simple physical properties obtainable from antibody sequences and by molecular dynamics simulations of individual antibody molecules. mAbs viscosities increase strongly with hydrophobicity and charge dipole distribution and decrease with net charge. Fast clearance correlates with high hydrophobicities of certain complementarity determining regions and with high positive or high negative net charge. Chemical degradation from tryptophan oxidation correlates with the average solvent exposure time of tryptophan residues. Aspartic acid isomerization rates can be predicted from solvent exposure and flexibility as determined by molecular dynamics simulations. These studies should aid in more rapid screening and selection of mAb candidates during early discovery.

Concentration dependent viscosity of monoclonal antibody solutions: explaining experimental behavior in terms of molecular properties

PURPOSE

Early identification of monoclonal antibody candidates whose development, as high concentration (≥100 mg/mL) drug products, could prove challenging, due to high viscosity, can help define strategies for candidate engineering and selection.

METHODS

Concentration dependent viscosities of 11 proprietary mAbs were measured. Sequence and structural features of the variable (Fv) regions were analyzed to understand viscosity behavior of the mAbs. Coarse-grained molecular simulations of two problematic mAbs were compared with that of a well behaved mAb.

RESULTS

Net charge, ξ-potential and pI of Fv regions were found to correlate with viscosities of highly concentrated antibody solutions. Negative net charges on the Fv regions of two mAbs with poor viscosity behaviors facilitate attractive self-associations, causing them to diffuse slower than a well-behaved mAb with positive net charge on its Fv region. An empirically derived equation that connects aggregation propensity and pI of the Fv region with high concentration viscosity of the whole mAb was developed.

CONCLUSIONS

An Fv region-based qualitative screening profile was devised to flag mAb candidates whose development, as high concentration drug products, could prove challenging. This screen can facilitate developability risk assessment and mitigation strategies for antibody based therapeutics via rapid high throughput material-free screening.

Assessment of physical stability of an antibody drug conjugate by higher order structure analysis: impact of thiol- maleimide chemistry

PURPOSE

To provide a systematic biophysical approach towards a better understanding of impact of conjugation chemistry on higher order structure and physical stability of an antibody drug conjugate (ADC).

METHODS

ADC was prepared using thiol-maleimide chemistry. Physical stabilities of ADC and its parent IgG1 mAb were compared using calorimetric, spectroscopic and molecular modeling techniques.

RESULTS

ADC and mAb respond differently to thermal stress. Both the melting temperatures and heat capacities are substantially lower for the ADC. Spectroscopic experiments show that ADC and mAb have similar secondary and tertiary structures, but these are more easily destabilized by thermal stress on the ADC indicating reduced conformational stability. Molecular modeling calculations suggest a substantial decrease in the conformational energy of the mAb upon conjugation. The local surface around the conjugation sites also becomes more hydrophobic in the ADC, explaining the lower colloidal stability and greater tendency of the ADC to aggregate.

CONCLUSIONS

Computational and biophysical analyses of an ADC and its parent mAb have provided insights into impact of conjugation on physical stability and pinpointed reasons behind lower structural stability and increased aggregation propensity of the ADC. This knowledge can be used to design appropriate formulations to stabilize the ADC.

Optimal charged mutations in the complementarity-determining regions that prevent domain antibody aggregation are dependent on the antibody scaffold

Therapeutic antibodies need to be highly resistant to aggregation due to the high concentrations required for subcutaneous delivery and the potential immunogenicity of antibody aggregates. Human antibody fragments-such as single-domain antibodies (VH or VL)-are typically much less soluble than full-length antibodies. Nevertheless, some aggregation-resistant VH domains have been discovered that are negatively charged at neutral pH and/or enriched in negatively charged residues within the complementarity-determining regions (CDRs). To better understand how to engineer diverse domain antibodies to resist aggregation, we have investigated the solubilizing activity of positively and negatively charged mutations within hydrophobic CDRs of multiple VH scaffolds that differ in their net charge. We find that negatively charged mutations inserted near the edges of hydrophobic CDRs are more effective than positively charged ones at inhibiting aggregation for VH scaffolds that are negatively or near-neutrally charged. In contrast, positively charged CDR mutations prevent aggregation better than negatively charged ones for a VH scaffold that is highly positively charged. Our findings suggest that the net charge of the antibody scaffold is a key determinant of the optimal CDR mutations for preventing aggregation. We expect that our findings will improve the design of aggregation-resistant antibodies with single- and multidomain scaffolds.

On the role of aggregation prone regions in protein evolution, stability, and enzymatic catalysis: insights from diverse analyses

The various roles that aggregation prone regions (APRs) are capable of playing in proteins are investigated here via comprehensive analyses of multiple non-redundant datasets containing randomly generated amino acid sequences, monomeric proteins, intrinsically disordered proteins (IDPs) and catalytic residues. Results from this study indicate that the aggregation propensities of monomeric protein sequences have been minimized compared to random sequences with uniform and natural amino acid compositions, as observed by a lower average aggregation propensity and fewer APRs that are shorter in length and more often punctuated by gate-keeper residues. However, evidence for evolutionary selective pressure to disrupt these sequence regions among homologous proteins is inconsistent. APRs are less conserved than average sequence identity among closely related homologues (≥80% sequence identity with a parent) but APRs are more conserved than average sequence identity among homologues that have at least 50% sequence identity with a parent. Structural analyses of APRs indicate that APRs are three times more likely to contain ordered versus disordered residues and that APRs frequently contribute more towards stabilizing proteins than equal length segments from the same protein. Catalytic residues and APRs were also found to be in structural contact significantly more often than expected by random chance. Our findings suggest that proteins have evolved by optimizing their risk of aggregation for cellular environments by both minimizing aggregation prone regions and by conserving those that are important for folding and function. In many cases, these sequence optimizations are insufficient to develop recombinant proteins into commercial products. Rational design strategies aimed at improving protein solubility for biotechnological purposes should carefully evaluate the contributions made by candidate APRs, targeted for disruption, towards protein structure and activity.

Biotechnology requires the large-scale expression, yield, and storage of recombinant proteins. Each step in protein production has the potential to cause aggregation as proteins, not evolved to exist outside the cell, endure the various steps involved in commercial manufacturing processes. Mechanistic studies into protein aggregation have revealed that certain sequence regions contribute more to the aggregation propensity of a protein than other sequence regions do. Efforts to disrupt these regions have thus far indicated that rational sequence engineering is a useful technique to reduce the aggregation of biotechnologically relevant proteins. To improve our ability to rationally engineer proteins with enhanced expression, solubility, and shelf-life we conducted extensive analyses of aggregation prone regions (APRs) within protein sequences to characterize the various roles these regions play in proteins. Findings from this work indicate that protein sequences have evolved by minimizing their aggregation propensities. However, we also found that many APRs are conserved in protein families and are essential to maintain protein stability and function. Therefore, the contributions that APRs, targeted for disruption, make towards protein stability and function should be carefully evaluated when improving protein solubility via rational design.

N-terminal truncation of an isolated human IgG1 CH2 domain significantly increases its stability and aggregation resistance

Isolated human immunoglobulin G (IgG) CH2 domains are promising scaffolds for novel candidate therapeutics. Unlike other human IgG domains, CH2 is not involved in strong interchain interactions, and isolated CH2 is relatively stable. However, isolated single CH2 is prone to aggregation. In native IgG and Fc molecules, the N-terminal residues of CH2 from the two heavy chains interact with each other and form hinge regions. By contrast, the N-terminal residues are highly disordered in isolated CH2. We have hypothesized that the removal of the CH2 N-terminal residues may not only increase its stability but also its aggregation resistance. To test this hypothesis we constructed a shortened variant of IgG1 CH2 (CH2s) where the first seven residues of the N-terminus were deleted. We found that the thermal stability of CH2s was increased by 5 °C compared to CH2. Importantly, we demonstrated that CH2s is significantly less prone to aggregation than CH2 as measured by Thioflavin T (ThT) fluorescence, turbidity, and light scattering. We also found that the CH2s exhibited pH-dependent binding to a soluble single-chain human neonatal Fc receptor (shFcRn) which was significantly stronger than the very weak binding of CH2 to shFcRn as measured by flow cytometry. Computer modeling suggested a possible mode of CH2 aggregation involving its N-terminal residues. Therefore, deletion of the N-terminal residues could increase drugability of CH2-based therapeutic candidates. This strategy to increase stability and aggregation resistance could also be applicable to other Ig-related proteins.

Proteins behaving badly: emerging technologies in profiling biopharmaceutical aggregation

Over recent decades biotechnology has made significant advances owing to the emergence of powerful biochemical and biophysical instrumentation. The development of such technologies has enabled high-throughput assessment of compounds, the implementation of recombinant DNA technology, and large-scale manufacture of monoclonal antibodies. Such innovations have ultimately resulted in the current experienced biopharmaceutical stronghold in the therapeutic market. Yet aggregate prediction and profiling remains a challenge in the formulation of biopharmaceuticals due to artifacts associated with each analytical method. We review some emerging trends and novel technologies that offer a promising potential for accurately predicting and profiling protein aggregation at various stages of biopharmaceutical product design.