Design of therapeutic proteins with enhanced stability

Predicting purification process fit of monoclonal antibodies using machine learning

In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purification development often conflicts with timeline pressures and material constraints, limiting the number of molecules and process conditions that can reasonably be assessed. Recently, high-throughput batch-binding screen data along with improved molecular descriptors have enabled development of robust quantitative structure-property relationship (QSPR) models that predict monoclonal antibody chromatographic binding behavior from the amino acid sequence. Here, we describe a QSPR strategy for in silico monoclonal antibody purification process fit assessment. Principal Component Analysis is applied to extract a one-dimensional basis for comparison of molecular chromatographic binding behavior from multi-dimensional high-throughput batch-binding screen data. Kernel Ridge Regression is used to predict the first principal component for new molecular sequences. This workflow is demonstrated with a set of 97 monoclonal antibodies for five chromatography resins in two salt types across a range of pH and salt concentrations. Model development benchmarks four descriptor sets from biophysical structural models and protein language models. The investigation illustrates the value QSPR models can provide to purification process fit assessment, and selection of resins and operating conditions from sequence alone.

Assessing amyloid fibrils and amorphous aggregates: A review

Protein misfolding and aggregation play a central role in the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. These aggregates manifest either as structured amyloid fibrils enriched in β-sheet conformations or as irregular amorphous aggregates with diverse morphologies. Understanding their formation, structure, and behavior is critical for deciphering disease mechanisms and developing targeted diagnostics and therapeutics. This review presents an integrated overview of both conventional and advanced techniques used to detect, distinguish, and structurally characterize these protein aggregates. It covers a range of spectroscopic and spectrometric tools, such as fluorescence, Raman, and mass spectrometry that facilitate aggregate identification. Microscopy methods, including atomic force and electron microscopy, are highlighted for morphological analysis. The review also discusses in situ detection strategies using fluorescent dyes, conformation-specific antibodies, enzymatic reporters, and real-time imaging. Separation methods like centrifugation, electrophoresis, and chromatography are outlined alongside structural analysis tools such as X-ray diffraction. Furthermore, the growing utility of computational approaches and artificial intelligence in predicting aggregation propensities and integrating biological data is emphasized. By critically evaluating each method's capabilities and limitations, this review provides a practical and forward-looking resource for researchers studying the complex landscape of protein aggregation.

Mechanistic insights into the structure-based design of a CspZ-targeting Lyme disease vaccine

Borrelia burgdorferi (Bb) causes Lyme disease (LD), one of the most common vector-borne diseases in the Northern Hemisphere. Here, we solve the crystal structure of a mutated Bb vaccine antigen, CspZ-YA that lacks the ability to bind to host complement factor H (FH). We generate point mutants of CspZ-YA and identify CspZ-YAI183Y and CspZ-YAC187S to trigger more robust bactericidal responses. Compared to CspZ-YA, these CspZ-YA mutants require a lower immunization frequency to protect mice from LD-associated inflammation and bacterial colonization. Antigenicity of wild-type and mutant CspZ-YA proteins are similar, as measured using sera from infected people or immunized female mice. Structural comparison of CspZ-YA with CspZ-YAI183Y and CspZ-YAC187S shows enhanced interactions of two helices adjacent to the FH-binding sites in the mutants, consistent with their elevated thermostability. In line with these findings, protective CspZ-YA monoclonal antibodies show increased binding to CspZ-YA at a physiological temperature (37 °C). In summary, this proof-of-concept study applies structural vaccinology to enhance intramolecular interactions for the long-term stability of a Bb antigen while maintaining its protective epitopes, thus promoting LD vaccine development.

AI-augmented physics-based docking for antibody-antigen complex prediction

MOTIVATION

Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high standards for model quality are required, a necessity for efficient antibody design. Artificial intelligence (AI) has significantly impacted the landscape of structure prediction for antibodies, both alone and in complex with their antigens.

METHODS

We utilized AI-guided antibody modeling tools to generate ensembles displaying diversity in the complementarity-determining region (CDR) and integrated those into our previously published AlphaFold2-rescored docking pipeline, a strategy called AI-augmented physics-based docking. In this study, we also compare docking performance with AlphaFold and Boltz-1, the new state-of-the-art. We distinguish between two types of success tailored to specific downstream applications: (i) criteria sufficient for epitope mapping, where gross quality is adequate and can complement experimental techniques, and (ii) criteria for producing higher-quality models suitable for engineering purposes.

RESULTS

We highlight that the quality of the ensemble is crucial for docking performance, that including too many models can be detrimental, and that prioritization of models is essential for achieving good performance. In a scenario analogous to docking using a crystallized antigen, our results robustly demonstrate the advantages of AI-augmented docking over AlphaFold2, further accentuated when higher standards in quality are imposed. Docking also shows improvements over Boltz-1, but those are less pronounced. Docking performance is still noticeably lower than AlphaFold3 in both epitope mapping and antibody design use cases. We observe a strong dependence on CDR-H3 loop length for physics-based tools on their ability to successfully predict. This helps define an applicability range where physics-based docking can be competitive to the newer generation of AI tools.

AVAILABILITY AND IMPLEMENTATION

The AF2 rescoring scripts are available at github.com/gaudreaultfnrc/AF2-Rescoring.

Modification of aggregation-prone regions of Arabidopsis glutamyl-tRNA reductase leads to increased stability while maintaining enzyme activity

The aggregation-prone region (APR) is a hydrophobic polypeptide motif that promotes protein aggregation, most commonly in the unfolded or misfolded state. It has been described that chaperones can shield the APRs of proteins, thereby preventing aggregate formation during de novo protein synthesis and stress response. Glutamyl-tRNA reductase (GluTR) is a key enzyme in tetrapyrrole biosynthesis (TBS) which catalyzes the rate-limiting step of 5-aminolevulinic acid synthesis. The GluTR sequence contains two APRs located at the N-terminus, which are suggested to be associated with the dysregulation of protein homeostasis during folding and refolding processes or under stress conditions. It remains open if these APRs directly contribute to GluTR aggregation in vivo, and how their removal or the modification might impact the aggregation and stability. In this study, we altered and removed the GluTR-APRs to investigate their effects on the stability and enzymatic activity of GluTR. Deletion of the APRs has been shown to be highly disruptive to the structure of GluTR, and a substitution mutation of V→P in each APR has also lowered the GluTR stability and activity. In contrast, the mutation V→T resulted in a modest reduction (18-30%) in GluTR aggregation in vitro, which was associated with a 27% improvement in GluTR stability in vivo relative to the wild-type enzyme. These results indicate that a point mutation in APR can improve GluTR stability without significantly affecting enzyme activity, thus imposing a potential direction for bioengineering of GluTR to improve productivity of the TBS pathway in plants.

Molecular convergence of neutralizing antibodies in human revealed by repeated rabies vaccination

Rabies vaccines require repeated immunization to robustly elicit neutralizing antibodies that prevent fatal diseases. Here, we analyzed rabies glycoprotein antibody repertoires at both polyclonal and monoclonal levels following repeated vaccination. Booster vaccination dramatically elevated the neutralizing activity of recalled antibodies, primarily targeting an immunodominant site III epitope with hydrophilic and rugged structures. Strikingly, the majority of site III-directed antibodies in the recall response used a convergent VH gene (IGHV3-30), and they exhibited more hydrophilic and shorter paratopes than non-site III antibodies, providing physicochemical advantages for binding to site III. Additionally, several amino acids on heavy chain CDR3 were identified as key sites for acquiring an ultrapotent neutralizing activity through site III binding. Our in-depth analysis of antibody repertoires revealed the molecular signatures of neutralizing antibodies generated by repeated rabies vaccination, possibly as a result of adaptive convergence.

AggNet: Advancing protein aggregation analysis through deep learning and protein language model

Protein aggregation is critical to various biological and pathological processes. Besides, it is also an important property in biotherapeutic development. However, experimental methods to profile protein aggregation are costly and labor-intensive, driving the need for more efficient computational alternatives. In this study, we introduce "AggNet," a novel deep learning framework based on the protein language model ESM2 and AlphaFold2, which utilizes physicochemical, evolutionary, and structural information to discriminate amyloid and non-amyloid peptides and identify aggregation-prone regions (APRs) in diverse proteins. Benchmark comparisons show that AggNet outperforms existing methods and achieves state-of-the-art performance on protein aggregation prediction. Also, the predictive ability of AggNet is stable across proteins with different secondary structures. Feature analysis and visualizations prove that the model effectively captures peptides' physicochemical properties effectively, thereby offering enhanced interpretability. Further validation through a case study on MEDI1912 confirms AggNet's practical utility in analyzing protein aggregation and guiding mutation for aggregation mitigation. This study enhances computational tools for predicting protein aggregation and highlights the potential of AggNet in protein engineering. Finally, to improve the accessibility of AggNet, the source code can be accessed at: https://github.com/Hill-Wenka/AggNet.

Active Site Studies to Explain Kinetics of Lipases in Organic Solvents Using Molecular Dynamics Simulations

This study investigates the intricate dynamics underlying lipase performance in organic solvents using comprehensive molecular dynamics (MD) simulations, supported by enzyme kinetics data. The study reveals that a single criterion can neither predict nor explain lipase activity in organic solvents, indicating the need for a comprehensive approach. Three lipases were included in this study: Candida antarctica lipase B (CALB), Rhizomucor miehei lipase (RML), and Thermomyces lanuginosus lipase (TLL). The lipases were investigated in acetonitrile, methyl tert-butyl ether, and hexane with increasing water activity. Computational investigations reveal that CALB's activity is negatively correlated to water cluster formations on its surface. In contrast, TLL's and RML's activity profiles show no negative effects of high water activity. However, TLL's and RML's activities are highly correlated to the conformation and stability of their active site regions. This study may pave the way for tailored applications of lipases, highlighting some of the factors that should be considered when lipase-catalyzed reactions are designed.

Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-Based Surface Descriptors

Monoclonal antibodies (mAbs) have found extensive applications and development in treating various diseases. From the pharmaceutical industry's perspective, the journey from the design and development of mAbs to clinical testing and large-scale production is a highly time-consuming and resource-intensive process. During the research and development phase, assessing and optimizing the developability of mAbs is of paramount importance to ensure their success as candidates for therapeutic drugs. The critical factors influencing mAb development are their biophysical properties, such as aggregation propensity, solubility, and viscosity. This study utilized a data set comprising 12 biophysical properties of 137 antibodies from a previous study (Proc Natl Acad Sci USA. 114(5):944-949, 2017). We employed full-length antibody molecular dynamics simulations and machine learning techniques to predict experimental data for these 12 biophysical properties. Additionally, we utilized a newly developed deep learning model called DeepSP, which directly predicts the dynamical and structural properties of spatial aggregation propensity and spatial charge map in different antibody regions from sequences. Our research findings indicate that the machine learning models we developed outperform previous methods in predicting most biophysical properties. Furthermore, the DeepSP model yields similar predictive results compared to molecular dynamic simulations while significantly reducing computational time. The code and parameters are freely available at https://github.com/Lailabcode/AbDev. Also, the webapp, AbDev, for 12 biophysical properties prediction has been developed and provided at https://devpred.onrender.com/AbDev.

Parametrically guided design of beta barrels and transmembrane nanopores using deep learning

Francis Crick's global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less successful, likely due to the deviations from ideal barrel geometry required to maintain inter-strand hydrogen bonding without introducing backbone strain. Instead, beta barrels have been designed using 2D structural blueprints; while this approach has successfully generated new fluorescent proteins, transmembrane nanopores, and other structures, it requires expert knowledge and provides only indirect control over the global shape. Here we show that the simplicity and control over shape and structure provided by parametric representations can be generalized beyond coiled coils by taking advantage of the rich sequence-structure relationships implicit in RoseTTAFold based design methods. Starting from parametrically generated barrel backbones, both RFjoint inpainting and RFdiffusion readily incorporate backbone irregularities necessary for proper folding with minimal deviation from the idealized barrel geometries. We show that for beta barrels across a broad range of beta sheet parameterizations, these methods achieve high in silico and experimental success rates, with atomic accuracy confirmed by an X-ray crystal structure of a novel barrel topology, and de novo designed 12, 14, and 16 stranded transmembrane nanopores with conductances ranging from 200 to 500 pS. By combining the simplicity and control of parametric generation with the high success rates of deep learning based protein design methods, our approach makes the design of proteins where global shape confers function, such as beta barrel nanopores, more precisely specifiable and accessible.

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, challenges such as achieving target selectivity persist in VGIC drug development. Recent progress in deep learning, particularly diffusion models, has enabled the computational design of protein binders for any clinically relevant protein based solely on its structure. These developments coincide with a surge in experimental structural data for VGICs, providing a rich foundation for computational design efforts. This review explores the recent advancements in computational protein design using deep learning and diffusion methods, focusing on their application in designing protein binders to modulate VGIC activity. We discuss the potential use of these methods to computationally design protein binders targeting different regions of VGICs, including the pore domain, voltage-sensing domains, and interface with auxiliary subunits. We provide a comprehensive overview of the different design scenarios, discuss key structural considerations, and address the practical challenges in developing VGIC-targeting protein binders. By exploring these innovative computational methods, we aim to provide a framework for developing novel strategies that could significantly advance VGIC pharmacology and lead to the discovery of effective and safe therapeutics.

A multifaceted approach to understanding protein-buffer interactions in biopharmaceuticals

The excipient selection process plays a crucial role in biopharmaceutical formulation development to ensure the long-term stability of the drug product. Though there are numerous options approved by regulatory authorities, only a subset is commonly utilized. Previous research has proposed various stabilization mechanisms, including protein-excipient interactions. However, identifying these interactions remains challenging due to their weak and transient nature. In this study, we present a comprehensive approach to identify such interactions. Using the 1HT2 CPMG (Carr-Purcel-Meiboom-Gill) filter experiment we identified interactions of rituximab with certain buffers and amino acids, shedding light on its Fc fragment instability that manifested during the enzymatic cleavage of the antibody. Moreover, chemometric analyses of 2D NMR fingerprints revealed interactions of selected excipients with antibody fragments. Furthermore, molecular dynamics simulations revealed potential interacting hotspots without NMR spectra assignment. Our results highlight the importance of an orthogonal methods approach to uncovering these critical interactions, advancing our understanding of excipient stabilization mechanisms and rational formulation design in biopharmaceutics.

Hybrid Mass Spectrometry Applied across the Production of Antibody Biotherapeutics

Post expression from the host cells, biotherapeutics undergo downstream processing steps before final formulation. Mass spectrometry and biophysical characterization methods are valuable for examining conformational and stoichiometric changes at these stages, although typically not used in biomanufacturing, where stability is assessed via bulk property studies. Here we apply hybrid MS methods to understand how solution condition changes impact the structural integrity of a biopharmaceutical across the processing pipeline. As an exemplar product, we use the model IgG1 antibody, mAb4. Flexibility, stability, aggregation propensity, and bulk properties are evaluated in relation to perfusion media, purification stages, and formulation solutions. Comparisons with Herceptin, an extensively studied IgG1 antibody, were conducted in a mass spectrometry-compatible solution. Despite presenting similar charge state distributions (CSD) in native MS, mAb4, and Herceptin show distinct unfolding patterns in activated ion mobility mass spectrometry (aIM-MS) and differential scanning fluorimetry (DSF). Herceptin's greater structural stability and aggregation onset temperature (Tagg) are attributed to heavier glycosylation and kappa-class light chains, unlike the lambda-class light chains in mAb4. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) revealed that mAb4 undergoes substantial structural changes during purification, marked by high flexibility, low melting temperature (Tm), and prevalent repulsive protein-protein interactions but transitions to a compact and stable structure in high-salt and formulated environments. Notably, in formulation, the third constant domain (CH3) of the heavy chain retains flexibility and is a region of interest for aggregation. Future work could translate features of interest from comprehensive studies like this to targeted approaches that could be utilized early in the development stage to aid in decision-making regarding targeted mutations or to guide the design space of bioprocesses and formulation choices.

Biophysical Analysis of Therapeutic Antibodies in the Early Development Pipeline

The successful progression of therapeutic antibodies and other biologics from the laboratory to the clinic depends on their possession of "drug-like" biophysical properties. The techniques and the resultant biophysical and biochemical parameters used to characterize their ease of manufacture can be broadly defined as developability. Focusing on antibodies, this review firstly discusses established and emerging biophysical techniques used to probe the early-stage developability of biologics, aimed towards those new to the field. Secondly, we describe the inter-relationships and redundancies amongst developability assays and how in silico methods aid the efficient deployment of developability to bring a new generation of cost-effective therapeutic proteins from bench to bedside more quickly and sustainably.

DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability

Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are computationally demanding. DeepSCM, a deep learning surrogate model based on sequence information to predict SCM, was recently developed to screen high-concentration antibody viscosity. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87. DeepSP descriptors were employed as features to build machine learning models to predict the aggregation rate of 21 antibodies, and the performance is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody stability using sequences only. DeepSP is freely available as an online tool via https://deepspwebapp.onrender.com and the codes and parameters are freely available at https://github.com/Lailabcode/DeepSP.

Hydrophobic interaction chromatography in continuous flow-through mode for product-related variant removal

Product-related impurities are challenging to remove during monoclonal antibody (mAb) purification process due to molecular similarity. Frontal chromatography on hydrophobic interaction resins has demonstrated its capability to effectively remove such impurities. However, process improvements geared towards purity level comes as a trade-off with the yield loss. In this work, we present a hydrophobic interaction chromatography process using multicolumn continuous chromatography (MCC) concept and frontal analysis to remove a high prevalence product related impurity. This design uses a two-column continuous system where the two columns are directly connected during product chase step to capture product wash loss without any in-process adjustment. This polish MCC operation resulted in a 10 % increase in yield while maintaining 99 % purity, despite the presence of 20 % product-related impurities in the feed material. One challenge associated with polish MCC design is that the accumulation of the impurities renders a non-steady state recycling. To surmount this issue and ensure a robust process, a mechanistic model was developed and validated to predict multicomponent breakthrough. This model was capable to predict multiple cycle behavior and accounts for increased impurity concentration. Assisted by the model, the optimized operation parameters and conditions can be determined to account for variation in product load quality. The simulated results demonstrate an effective doubling of productivity compared to conventional batch chromatography.

Protein language models learn evolutionary statistics of interacting sequence motifs

Protein language models (pLMs) have emerged as potent tools for predicting and designing protein structure and function, and the degree to which these models fundamentally understand the inherent biophysics of protein structure stands as an open question. Motivated by a finding that pLM-based structure predictors erroneously predict nonphysical structures for protein isoforms, we investigated the nature of sequence context needed for contact predictions in the pLM Evolutionary Scale Modeling (ESM-2). We demonstrate by use of a "categorical Jacobian" calculation that ESM-2 stores statistics of coevolving residues, analogously to simpler modeling approaches like Markov Random Fields and Multivariate Gaussian models. We further investigated how ESM-2 "stores" information needed to predict contacts by comparing sequence masking strategies, and found that providing local windows of sequence information allowed ESM-2 to best recover predicted contacts. This suggests that pLMs predict contacts by storing motifs of pairwise contacts. Our investigation highlights the limitations of current pLMs and underscores the importance of understanding the underlying mechanisms of these models.

Molecular Dynamics Simulations Reveal How Competing Protein-Surface Interactions for Glycine, Citrate, and Water Modulate Stability in Antibody Fragment Formulations

The design of stable formulations remains a major challenge for protein therapeutics, particularly the need to minimize aggregation. Experimental formulation screens are typically based on thermal transition midpoints (Tm), and forced degradation studies at elevated temperatures. Both approaches give limited predictions of long-term storage stability, particularly at low temperatures. Better understanding of the mechanisms of action for formulation of excipients and buffers could lead to improved strategies for formulation design. Here, we identified a complex impact of glycine concentration on the experimentally determined stability of an antibody Fab fragment and then used molecular dynamics simulations to reveal mechanisms that underpin these complex behaviors. Tm values increased monotonically with glycine concentration, but associated ΔSvh measurements revealed more complex changes in the native ensemble dynamics, which reached a maximum at 30 mg/mL. The aggregation kinetics at 65 °C were similar at 0 and 20 mg/mL glycine, but then significantly slower at 50 mg/mL. These complex behaviors indicated changes in the dominant stabilizing mechanisms as the glycine concentration was increased. MD revealed a complex balance of glycine self-interaction, and differentially preferred interactions of glycine with the Fab as it displaced hydration-shell water, and surface-bound water and citrate buffer molecules. As a result, glycine binding to the Fab surface had different effects at different concentrations, and led from preferential interactions at low concentrations to preferential exclusion at higher concentrations. During preferential interaction, glycine displaced water from the Fab hydration shell, and a small number of water and citrate molecules from the Fab surface, which reduced the protein dynamics as measured by root-mean-square fluctuation (RMSF) on the short time scales of MD. By contrast, the native ensemble dynamics increased according to ΔSvh, suggesting increased conformational changes on longer time scales. The aggregation kinetics did not change at low glycine concentrations, and so the opposing dynamics effects either canceled out or were not directly relevant to aggregation. During preferential exclusion at higher glycine concentrations, glycine could only bind to the Fab surface through the displacement of citrate buffer molecules already favorably bound on the Fab surface. Displacement of citrate increased the flexibility (RMSF) of the Fab, as glycine formed fewer bridging hydrogen bonds to the Fab surface. Overall, the slowing of aggregation kinetics coincided with reduced flexibility in the Fab ensemble at the very highest glycine concentrations, as determined by both RMSF and ΔSvh, and occurred at a point where glycine binding displaced neither water nor citrate. These final interactions with the Fab surface were driven by mass action and were the least favorable, leading to a macromolecular crowding effect under the regime of preferential exclusion that stabilized the dynamics of Fab.

Mechanistic insights into structure-based design of a Lyme disease subunit vaccine

The quality of protective immunity plays a critical role in modulating vaccine efficacy, with native antigens often not able to trigger sufficiently strong immune responses for pathogen killing. This warrants creation of structure-based vaccine design, leveraging high-resolution antigen structures for mutagenesis to improve protein stability and efficient immunization strategies. Here, we investigated the mechanisms underlying structure-based vaccine design using CspZ-YA, a vaccine antigen from Borrelia burgdorferi, the bacteria causing Lyme disease (LD), the most common vector-borne disease in the Northern Hemisphere. Compared to wild-type CspZ-YA, we found CspZ-YAI183Y and CspZ-YAC187S required lower immunization frequency to protect mice from LD-associated manifestations and bacterial colonization. We observed indistinguishable human and mouse antigenicity between wild-type and mutant CspZ-YA proteins after native infection or active immunization. This supports our newly generated, high-resolution structures of CspZ-YAI183Y and CspZ-YAC187S, showing no altered surface epitopes after mutagenesis. However, CspZ-YAI183Y and CspZ-YAC187S favored the interactions between helices H and I, consistent with their elevated thermostability. Such findings are further strengthened by increasing ability of protective CspZ-YA monoclonal antibodies in binding to CspZ-YA at a physiological temperature (37°C). Overall, this study demonstrated enhanced intramolecular interactions improved long-term stability of antigens while maintaining protective epitopes, providing a mechanism for structure-based vaccine design. These findings can ultimately be extended to other vaccine antigens against newly emerging pathogens for the improvement of protective immunity.

Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity

Aggrescan4D (A4D) is an advanced computational tool designed for predicting protein aggregation, leveraging structural information and the influence of pH. Building upon its predecessor, Aggrescan3D (A3D), A4D has undergone numerous enhancements aimed at assisting the improvement of protein solubility. This manuscript reviews A4D's updated functionalities and explains the fundamental principles behind its pH-dependent calculations. Additionally, it presents an antibody case study to evaluate its performance in comparison with other structure-based predictors. Notably, A4D integrates advanced protein engineering protocols with pH-dependent calculations, enhancing its utility in advising solubility-enhancing mutations. A4D considers the impact of structural flexibility on aggregation propensities, and includes a large set of precalculated predictions. These capabilities should help to open new avenues for both understanding and managing protein aggregation. A4D is accessible through a dedicated web server at https://biocomp.chem.uw.edu.pl/a4d/.

A systemic approach to identifying sequence frameworks that decrease mAb production in a transient Chinese hamster ovary cell expression system

Monoclonal antibodies (mAbs) are often engineered at the sequence level for improved clinical performance yet are rarely evaluated prior to candidate selection for their "developability" characteristics, namely expression, which can necessitate additional resource investments to improve the manufacturing processes for problematic mAbs. A strong relationship between primary sequence and expression has emerged, with slight differences in amino acid sequence resulting in titers differing by up to an order of magnitude. Previous work on these "difficult-to-express" (DTE) mAbs has shown that these phenotypes are driven by post-translational bottlenecks in antibody folding, assembly, and secretion processes. However, it has been difficult to translate these findings across cell lines and products. This work presents a systematic approach to study the impact of sequence variation on mAb expression at a larger scale and under more industrially relevant conditions. The analysis found 91 mutations that decreased transient expression of an IgG1κ in Chinese hamster ovary (CHO) cells and revealed that mutations at inaccessible residues, especially those leading to decreases in residue hydrophobicity, are not favorable for high expression. This workflow can be used to better understand sequence determinants of mAb expression to improve candidate selection procedures and reduce process development timelines.

High throughput screening for rapid and reliable prediction of monovalent antibody binding behavior in flowthrough mode

Flowthrough (FT) anion exchange (AEX) chromatography is a widely used polishing step for the purification of monoclonal antibody (mAb) formats. To accelerate downstream process development, high throughput screening (HTS) tools have proven useful. In this study, the binding behavior of six monovalent mAbs (mvAbs) was investigated by HTS in batch binding mode on different AEX and mixed-mode resins at process-relevant pH and NaCl concentrations. The HTS entailed the evaluation of mvAb partition coefficients (Kp) and visualization of results in surface-response models. Interestingly, the HTS data grouped the mvAbs into either a strong-binding group or a weak-binding/FT group independent of theoretical Isoelectric point. Mapping the charged and hydrophobic patches by in silico protein surface property analyses revealed that the distribution of patches play a major role in predicting FT behavior. Importantly, the conditions identified by HTS were successfully verified by 1 mL on-column experiments. Finally, employing the optimal FT conditions (7-9 mS/cm and pH 7.0) at a mini-pilot scale (CV = 259 mL) resulted in 99% yield and a 21-23-fold reduction of host cell protein to <100 ppm, depending on the varying host cell protein (HCP) levels in the load. This work opens the possibility of using HTS in FT mode to accelerate downstream process development for mvAb candidates in early research.

Comparison of "framework Shuffling" and "CDR Grafting" in humanization of a PD-1 murine antibody

Introduction

Humanization is typically adopted to reduce the immunogenicity of murine antibodies generated by hybridoma technology when used in humans.

Methods

Two different strategies of antibody humanization are popularly employed, including "complementarity determining region (CDR) grafting" and "framework (FR) shuffling" to humanize a murine antibody against human programmed death-1 (PD-1), XM PD1. In CDR-grafting humanization, the CDRs of XM PD-1, were grafted into the human FR regions with high homology to the murine FR counterparts, and back mutations of key residues were performed to retain the antigen-binding affinities. While in FR-shuffling humanization, a combinatorial library of the six murine CDRs in-frame of XM PD-1 was constructed to a pool of human germline FRs for high-throughput screening for the most favorable variants. We evaluated many aspects which were important during antibody development of the molecules obtained by the two methods, including antibody purity, thermal stability, binding efficacy, predicted humanness, and immunogenicity, along with T cell epitope prediction for the humanized antibodies.

Results

While the ideal molecule was not achieved through CDR grafting in this particular instance, FR-shuffling proved successful in identifying a suitable candidate. The study highlights FR-shuffling as an effective complementary approach that potentially increases the success rate of antibody humanization. It is particularly noted for its accessibility to those with a biological rather than a computational background.

Discussion

The insights from this comparison are intended to assist other researchers in selecting appropriate humanization strategies for drug development, contributing to broader application and understanding in the field.

A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage and its implications

The manufacturability assessment and optimization of bispecific antibodies (bsAbs) during the discovery stage are crucial for the success of the drug development process, impacting the speed and cost of advancing such therapeutics to the Investigational New Drug (IND) stage and ultimately to the market. The complexity of bsAbs creates challenges in employing effective evaluation methods to detect developability risks in early discovery stage, and poses difficulties in identifying the root causes and implementing subsequent engineering solutions. This study presents a case of engineering a bsAb that displayed a normal solution appearance during the discovery phase but underwent significant precipitation when subjected to agitation stress during 15 L Chemistry, Manufacturing, and Control (CMC) production Leveraging analytical tools, structural analysis, in silico prediction, and wet-lab validations, the key molecular origins responsible for the observed precipitation were identified and addressed. Sequence engineering to reduce protein surface hydrophobicity and enhance conformational stability proved effective in resolving agitation-induced aggregation. The refined bsAb sequences enabled successful mass production in CMC department. The findings of this case study contribute to the understanding of the fundamental mechanism of agitation-induced aggregation and offer a potential protein engineering procedure for addressing similar issues in bsAb. Furthermore, this case study emphasizes the significance of a close partnership between Discovery and CMC teams. Integrating CMC's rigorous evaluation methods with Discovery's engineering capability can facilitate a streamlined development process for bsAb molecules.

Druggability properties of a L309K mutation in the antibody CH2 domain

In the early stages of antibody drug development, it is imperative to conduct a comprehensive assessment and enhancement of the druggability attributes of potential molecules by considering their fundamental physicochemical properties. This study specifically concentrates on the surface-exposed hydrophobic region of the candidate antibody aPDL1-WT and explores the effectiveness of the L309K mutation strategy. The resulting aPDL1-LK variant demonstrates a notable enhancement over the original antibody in addressing the issue of aggregation and formation of large molecular impurities under accelerated high-temperature conditions. The mutated molecule, aPDL1-LK, exhibits excellent physicochemical properties such as hydrophilicity, conformational stability, charge variant stability, post-translational modifications, and serum stability. In terms of biological function, aPDL1-LK maintains the same glycosylation pattern as the original antibody and shows no significant difference in affinity for antigen hPDL1 protein, CD16a-F158, CD64, CD32a-H131, and complement C1q, compared to aPDL1-WT. The L309K mutation results in an approximately twofold reduction in its affinity for CD16a-V158 and CD32a-R131. In vitro biological assays, including antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and complement-dependent cytotoxicity (CDC), reveal that the L309K mutation may decrease CD16a-V158-mediated ADCC activity due to the mutation-induced decrease in ligand affinity, while not affect CD32a-R131-mediated ADCP activity. In conclusion, the L309K mutation offers a promising strategy to enhance the druggability properties of candidate antibodies.

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.

Adsorption of monoclonal antibody fragments at the water-oil interface: A coarse-grained molecular dynamics study

Monoclonal antibodies (mAbs) can undergo structural changes due to interaction with oil-water interfaces during storage. Such changes can lead to aggregation, resulting in a loss of therapeutic efficacy. Therefore, understanding the microscopic mechanism controlling mAb adsorption is crucial to developing strategies that can minimize the impact of interfaces on the therapeutic properties of mAbs. In this study, we used MARTINI coarse-grained molecular dynamics simulations to investigate the adsorption of the Fab and Fc domains of the monoclonal antibody COE3 at the oil-water interface. Our aim was to determine the regions on the protein surface that drive mAb adsorption. We also investigate the role of protein concentration on protein orientation and protrusion to the oil phase. While our structural analyses compare favorably with recent neutron reflectivity measurements, we observe some differences. Unlike the monolayer at the interface predicted by neutron reflectivity experiments, our simulations indicate the presence of a secondary diffused layer near the interface. We also find that under certain conditions, protein-oil interaction can lead to a considerable distortion in the protein structure, resulting in enhanced adsorption behavior.

Prediction of protein aggregation

The scientific community is very interested in protein aggregation because of its involvement in several neurodegenerative diseases and its significance in industry. Remarkably, fibrillar aggregates are utilized naturally for constructing structural scaffolds or creating biological switches and may be intentionally designed to construct versatile nanomaterials. Consequently, there is a significant need to rationalize and predict protein aggregation. Researchers have developed various computational methodologies and algorithms to predict protein aggregation and understand its underlying mechanics. This chapter aims to summarize the significant advancements in computational methods, accessible resources, and prospective developments in the field of in silico research. We assess the existing computational tools for predicting protein aggregation propensities, detecting areas that are prone to sequential and structural aggregation, analyzing the effects of mutations on protein aggregation, or identifying prion-like domains.

Prediction of polyspecificity from antibody sequence data by machine learning

Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of their diversity and specificity, antibodies make up for a large part of recently developed biologic drugs. For therapeutic use antibodies need to fulfill several criteria to be safe and efficient. Polyspecific antibodies can bind structurally unrelated molecules in addition to their main target, which can lead to side effects and decreased efficacy in a therapeutic setting, for example via reduction of effective drug levels. Therefore, we created a neural-network-based model to predict polyspecificity of antibodies using the heavy chain variable region sequence as input. We devised a strategy for enriching antibodies from an immunization campaign either for antigen-specific or polyspecific binding properties, followed by generation of a large sequencing data set for training and cross-validation of the model. We identified important physico-chemical features influencing polyspecificity by investigating the behaviour of this model. This work is a machine-learning-based approach to polyspecificity prediction and, besides increasing our understanding of polyspecificity, it might contribute to therapeutic antibody development.

Therapeutic proteins: developments, progress, challenges, and future perspectives

Proteins are considered magic molecules due to their enormous applications in the health sector. Over the past few decades, therapeutic proteins have emerged as a promising treatment option for various diseases, particularly cancer, cardiovascular disease, diabetes, and others. The formulation of protein-based therapies is a major area of research, however, a few factors still hinder the large-scale production of these therapeutic products, such as stability, heterogenicity, immunogenicity, high cost of production, etc. This review provides comprehensive information on various sources and production of therapeutic proteins. The review also summarizes the challenges currently faced by scientists while developing protein-based therapeutics, along with possible solutions. It can be concluded that these proteins can be used in combination with small molecular drugs to give synergistic benefits in the future.

Unraveling the Microscopic Mechanism of Molecular Ion Interaction with Monoclonal Antibodies: Impact on Protein Aggregation

Understanding and predicting protein aggregation represents one of the major challenges in accelerating the pharmaceutical development of protein therapeutics. In addition to maintaining the solution pH, buffers influence both monoclonal antibody (mAb) aggregation in solution and the aggregation mechanisms since the latter depend on the protein charge. Molecular-level insight is necessary to understand the relationship between the buffer-mAb interaction and mAb aggregation. Here, we use all-atom molecular dynamics simulations to investigate the interaction of phosphate (Phos) and citrate (Cit) buffer ions with the Fab and Fc domains of mAb COE3. We demonstrate that Phos and Cit ions feature binding mechanisms, with the protein that are very different from those reported previously for histidine (His). These differences are reflected in distinctive ion-protein binding modes and adsorption/desorption kinetics of the buffer molecules from the mAb surface and result in dissimilar effects of these buffer species on mAb aggregation. While His shows significant affinity toward hydrophobic amino acids on the protein surface, Phos and Cit ions preferentially bind to charged amino acids. We also show that Phos and Cit anions provide bridging contacts between basic amino acids in neighboring proteins. The implications of such contacts and their connection to mAb aggregation in therapeutic formulations are discussed.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

Mechanistic Insights into the Adsorption of Monoclonal Antibodies at the Water/Vapor Interface

Monoclonal antibodies (mAbs) are active components of therapeutic formulations that interact with the water-vapor interface during manufacturing, storage, and administration. Surface adsorption has been demonstrated to mediate antibody aggregation, which leads to a loss of therapeutic efficacy. Controlling mAb adsorption at interfaces requires a deep understanding of the microscopic processes that lead to adsorption and identification of the protein regions that drive mAb surface activity. Here, we report all-atom molecular dynamics (MD) simulations of the adsorption behavior of a full IgG1-type antibody at the water/vapor interface. We demonstrate that small local changes in the protein structure play a crucial role in promoting adsorption. Also, interfacial adsorption triggers structural changes in the antibody, potentially contributing to the further enhancement of surface activity. Moreover, we identify key amino acid sequences that determine the adsorption of antibodies at the water-air interface and outline strategies to control the surface activity of these important therapeutic proteins.

QTY code designed antibodies for aggregation prevention: A structural bioinformatic and computational study

Therapeutic monoclonal antibodies are the most rapidly growing class of molecular medicine, and they are beneficial to the treatment of a broad spectrum of human diseases. However, the aggregation of antibodies during the process of manufacture, distribution, and storage poses significant challenges, potentially compromising efficacy and inducing adverse immune responses. We previously conceived a QTY (glutamine, threonine, tyrosine) code, a simple tool for enhancing protein water-solubility by systematically pairwise replacing hydrophobic residues L (leucine), V (valine)/I (isoleucine), and F (phenylalanine). The QTY code offers a promising alternative to traditional methods of controlling aggregation in integral transmembrane proteins. In this study, we designed variants of four antibodies applying the QTY code, changing only the β-sheets. Through the structure-based aggregation analysis, we found that these QTY antibody variants demonstrated significantly decreased aggregation propensity compared to their wild-type counter parts. Our results of molecular dynamics simulations showed that the design by QTY code is capable of maintaining the antigen-binding affinity and structural stability. Our structural informatic and computational study suggests that the QTY code offers a significant potential in mitigating antibody aggregation.

Supercharged Phosphotriesterase for improved Paraoxon activity

Phosphotriesterases (PTEs) represent a class of enzymes capable of efficient neutralization of organophosphates (OPs), a dangerous class of neurotoxic chemicals. PTEs suffer from low catalytic activity, particularly at higher temperatures, due to low thermostability and low solubility. Supercharging, a protein engineering approach via selective mutation of surface residues to charged residues, has been successfully employed to generate proteins with increased solubility and thermostability by promoting charge-charge repulsion between proteins. We set out to overcome the challenges in improving PTE activity against OPs by employing a computational protein supercharging algorithm in Rosetta. Here, we discover two supercharged PTE variants, one negatively supercharged (with -14 net charge) and one positively supercharged (with +12 net charge) and characterize them for their thermodynamic stability and catalytic activity. We find that positively supercharged PTE possesses slight but significant losses in thermostability, which correlates to losses in catalytic efficiency at all temperatures, whereas negatively supercharged PTE possesses increased catalytic activity across 25°C-55°C while offering similar thermostability characteristic to the parent PTE. The impact of supercharging on catalytic efficiency will inform the design of shelf-stable PTE and criteria for enzyme engineering.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Residue-resolved insights into the stabilization of therapeutic proteins by excipients: A case study of two monoclonal antibodies with arginine and glutamate

Protein formulation development relies on the selection of excipients that inhibit protein-protein interactions preventing aggregation. Empirical strategies involve screening many excipient and buffer combinations by physicochemical characterization using forced degradation or temperature-induced stress, mostly under accelerated conditions. Such methods do not readily provide information on the inter- and intramolecular interactions responsible for the effects of excipients. Here, we describe a combined experimental and computational approach for investigating the effect of protein-excipient interactions on formulation stability, which allows the identification of preferential interaction sites and thus can aid in the selection of excipients to be experimentally screened. Model systems composed of two marketed therapeutic IgG1 monoclonal antibodies with identical Fc domain sequences, trastuzumab and omalizumab, were investigated with commonly used excipients arginine, glutamate, and equimolar arginine/glutamate mixtures. Protein-excipient interactions were studied using all-atom molecular dynamics (MD) simulations, which show accumulation of the excipients at specific antibody regions. Preferential excipient-interaction sites were particularly found for charged and aromatic residues and in the complementary-determining regions, with more pronounced arginine contacts for omalizumab than trastuzumab. These computational findings are in line with the more pronounced stabilizing effects of arginine observed in the long-term storage stability study. Furthermore, the aggregation and solubility propensity predicted by commonly used in silico tools do not align with the preferential excipient-interaction sites identified by the MD simulations, suggesting that different physicochemical mechanisms are at play.

Modulation of the high concentration viscosity of IgG1 antibodies using clinically validated Fc mutations

The self-association of therapeutic antibodies can result in elevated viscosity and create problems in manufacturing and formulation, as well as limit delivery by subcutaneous injection. The high concentration viscosity of some antibodies has been reduced by variable domain mutations or by the addition of formulation excipients. In contrast, the impact of Fc mutations on antibody viscosity has been minimally explored. Here, we studied the effect of a panel of common and clinically validated Fc mutations on the viscosity of two closely related humanized IgG1, κ antibodies, omalizumab (anti-IgE) and trastuzumab (anti-HER2). Data presented here suggest that both Fab-Fab and Fab-Fc interactions contribute to the high viscosity of omalizumab, in a four-contact model of self-association. Most strikingly, the high viscosity of omalizumab (176 cP) was reduced 10.7- and 2.2-fold by Fc modifications for half-life extension (M252Y:S254T:T256E) and aglycosylation (N297G), respectively. Related single mutations (S254T and T256E) each reduced the viscosity of omalizumab by ~6-fold. An alternative half-life extension Fc mutant (M428L:N434S) had the opposite effect in increasing the viscosity of omalizumab by 1.5-fold. The low viscosity of trastuzumab (8.6 cP) was unchanged or increased by ≤ 2-fold by the different Fc variants. Molecular dynamics simulations provided mechanistic insight into the impact of Fc mutations in modulating electrostatic and hydrophobic surface properties as well as conformational stability of the Fc. This study demonstrates that high viscosity of some IgG1 antibodies can be mitigated by Fc mutations, and thereby offers an additional tool to help design future antibody therapeutics potentially suitable for subcutaneous delivery.

Developability considerations for bispecific and multispecific antibodies

Bispecific antibodies (bsAb) and multispecific antibodies (msAb) encompass a diverse variety of formats that can concurrently bind multiple epitopes, unlocking mechanisms to address previously difficult-to-treat or incurable diseases. Early assessment of candidate developability enables demotion of antibodies with low potential and promotion of the most promising candidates for further development. Protein-based therapies have a stringent set of developability requirements in order to be competitive (e.g. high-concentration formulation, and long half-life) and their assessment requires a robust toolkit of methods, few of which are validated for interrogating bsAbs/msAbs. Important considerations when assessing the developability of bsAbs/msAbs include their molecular format, likelihood for immunogenicity, specificity, stability, and potential for high-volume production. Here, we summarize the critical aspects of developability assessment, and provide guidance on how to develop a comprehensive plan tailored to a given bsAb/msAb.

Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG1 antibody

Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤ 2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2-10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.

In-situ biophysical characterization of high-concentration protein formulations using wNMR

High-concentration protein formulation is of paramount importance in patient-centric drug product development, but it also presents challenges due to the potential for enhanced aggregation and increased viscosity. The analysis of critical quality attributes often necessitates the transfer of samples from their primary containers together with sample dilution. Therefore, there is a demand for noninvasive, in situ biophysical methods to assess protein drug products directly in primary sterile containers, such as prefilled syringes, without dilution. In this study, we introduce a novel application of water proton nuclear magnetic resonance (wNMR) to evaluate the aggregation propensity of a high-concentration drug product, Dupixent® (dupilumab), under stress conditions. wNMR results demonstrate a concentration-dependent, reversible association of dupilumab in the commercial formulation, as well as irreversible aggregation when exposed to accelerated thermal stress, but gradually reversible aggregation when exposed to freeze and thaw cycles. Importantly, these results show a strong correlation with data obtained from established biophysical analytical tools widely used in the pharmaceutical industry. The application of wNMR represents a promising approach for in situ noninvasive analysis of high-concentration protein formulations directly in their primary containers, providing valuable insights for drug development and quality assessment.

Interaction between preservatives and a monoclonal antibody in support of multidose formulation development

Multidose formulations have patient-centric advantages over single-dose formats. A major challenge in developing multidose formulations is the prevention of microbial growth that can potentially be introduced during multiple drawings. The incorporation of antimicrobial preservatives (APs) is a common approach to inhibit this microbial growth. Selection of the right preservative while maintaining drug product stability is often challenging. We explored the effects of three APs, 1.1 % (w/v) benzyl alcohol, 0.62 % (w/v) phenol, and 0.42 % (w/v) m-cresol, on a model immunoglobulin G1 monoclonal antibody, termed the "NIST mAb." As measured by hydrogen exchange-mass spectrometry (HX-MS) and differential scanning calorimetry, conformational stability was decreased in the presence of APs. Specifically, flexibility (faster HX) was significantly increased in the CH2 domain (HC 238-255) across all APs. The addition of phenol caused the greatest conformational destabilization, followed by m-cresol and benzyl alcohol. Storage stability studies conducted by subvisible particle (SVP) analysis at 40 °C over 4 weeks further revealed an increase in SVPs in the presence of phenol and m-cresol but not in the presence of benzyl alcohol. However, as monitored by size exclusion chromatography, there was neither a significant change in the monomeric content nor an accumulation of soluble aggregate in the presence of APs.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

Comparative Analysis of Protein Surface Hydrophobicity Maps Determined by Sparse Sampling INDUS and Spatial Aggregation Propensity

Protein surface hydrophobicity plays a central role in various biological processes such as protein folding and aggregation, as well as in the design and manufacturing of biotherapeutics. While the hydrophobicity of protein surface patches has been linked to their constituent residue hydropathies, recent research has shown that protein surface hydrophobicity is more complex and characterized by the response of water to these surfaces. In this work, we employ water density perturbations to map the surface hydrophobicity of a set of model proteins using sparse indirect umbrella sampling simulations (SSI). This technique is used to identify hydrophobic surface patches for the set of model proteins, and the results are compared to those obtained from the widely adopted spatial aggregation propensity (SAP) technique. While SAP-based calculations show agreement with SSI in some cases, there are several examples of disagreement. We identify four general classes of difference in behavior and study factors that contribute to these differences. We find that the SAP method can sometimes mask the effect of weakly nonpolar or isolated nonpolar residues that can lead to strong hydrophobic patches on the protein surface. In addition, hydrophobic patches identified by SAP can exhibit shifts in both position and strength on the SSI map. Our results demonstrate that the combination of topography and chemical context controls the hydrophobicity of a given patch above and beyond the intrinsic polarity of the residues present on the patch surface. The availability of more accurate protein hydrophobicity maps in concert with new classes of hydrophobic molecular descriptors may create significant opportunities for in silico prediction of protein behavior for a range of applications, such as protein design, biomanufacturability, and downstream bioprocessing.

Biophysical evolution of the receptor-binding domains of SARS-CoVs

With hundreds of coronaviruses (CoVs) identified in bats that can infect humans, it is essential to understand how CoVs that affected the human population have evolved. Seven known CoVs have infected humans, of which three CoVs caused severe disease with high mortalities: severe acute respiratory syndrome (SARS)-CoV emerged in 2002, Middle East respiratory syndrome-CoV in 2012, and SARS-CoV-2 in 2019. SARS-CoV and SARS-CoV-2 belong to the same family, follow the same receptor pathway, and use their receptor-binding domain (RBD) of spike protein to bind to the angiotensin-converting enzyme 2 (ACE2) receptor on the human epithelial cell surface. The sequence of the two RBDs is divergent, especially in the receptor-binding motif that directly interacts with ACE2. We probed the biophysical differences between the two RBDs in terms of their structure, stability, aggregation, and function. Since RBD is being explored as an antigen in protein subunit vaccines against CoVs, determining these biophysical properties will also aid in developing stable protein subunit vaccines. Our results show that, despite RBDs having a similar three-dimensional structure, they differ in their thermodynamic stability. RBD of SARS-CoV-2 is significantly less stable than that of SARS-CoV. Correspondingly, SARS-CoV-2 RBD shows a higher aggregation propensity. Regarding binding to ACE2, less stable SARS-CoV-2 RBD binds with a higher affinity than more stable SARS-CoV RBD. In addition, SARS-CoV-2 RBD is more homogenous in terms of its binding stoichiometry toward ACE2 compared to SARS-CoV RBD. These results indicate that SARS-CoV-2 RBD differs from SARS-CoV RBD in terms of its stability, aggregation, and function, possibly originating from the diverse receptor-binding motifs. Higher aggregation propensity and decreased stability of SARS-CoV-2 RBD warrant further optimization of protein subunit vaccines that use RBD as an antigen by inserting stabilizing mutations or formulation screening.

Interfacial Pressure and Viscoelasticity of Antibodies and Their Correlation to Long-Term Stability in Formulation

Monoclonal antibodies (mAbs) form viscoelastic gel-like layers at the air-water interface due to their amphiphilic nature, and this same protein characteristic can lead to undesired aggregation of proteins in therapeutic formulations. We hypothesize that the interfacial viscoelasticity and surface pressure of mAbs at the air-water interface will correlate with their long-term stability. To test this hypothesis, the interfacial viscoelastic rheology and surface pressure of five different antibodies with varying visible particle counts from a three-year stability study were measured. We find that both the surface pressures and interfacial elastic moduli correlate well with the long-time mAb solution stability within a class of mAbs with the interfacial elastic moduli being particularly sensitive to discriminate between stable and unstable mAbs across a range of formulations. Furthermore, X-ray reflectivity was used to gain insight into the interfacial structure of mAbs at the air-water interface, providing a possible molecular mechanism to explain the relationship between interfacial elastic moduli and the long-term stability.

De novo design of knotted tandem repeat proteins

De novo protein design methods can create proteins with folds not yet seen in nature. These methods largely focus on optimizing the compatibility between the designed sequence and the intended conformation, without explicit consideration of protein folding pathways. Deeply knotted proteins, whose topologies may introduce substantial barriers to folding, thus represent an interesting test case for protein design. Here we report our attempts to design proteins with trefoil (31) and pentafoil (51) knotted topologies. We extended previously described algorithms for tandem repeat protein design in order to construct deeply knotted backbones and matching designed repeat sequences (N = 3 repeats for the trefoil and N = 5 for the pentafoil). We confirmed the intended conformation for the trefoil design by X ray crystallography, and we report here on this protein's structure, stability, and folding behaviour. The pentafoil design misfolded into an asymmetric structure (despite a 5-fold symmetric sequence); two of the four repeat-repeat units matched the designed backbone while the other two diverged to form local contacts, leading to a trefoil rather than pentafoil knotted topology. Our results also provide insights into the folding of knotted proteins.

Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations

The computational prediction of the viscosity of dense protein solutions is highly desirable, for example, in the early development phase of high-concentration biopharmaceutical formulations where the material needed for experimental determination is typically limited. Here, we use large-scale atomistic molecular dynamics (MD) simulations with explicit solvation to de novo predict the dynamic viscosities of solutions of a monoclonal IgG1 antibody (mAb) from the pressure fluctuations using a Green-Kubo approach. The viscosities at simulated mAb concentrations of 200 and 250 mg/mL are compared to the experimental values, which we measured with rotational rheometry. The computational viscosity of 24 mPa·s at the mAb concentration of 250 mg/mL matches the experimental value of 23 mPa·s obtained at a concentration of 213 mg/mL, indicating slightly different effective concentrations (or activities) in the MD simulations and in the experiments. This difference is assigned to a slight underestimation of the effective mAb-mAb interactions in the simulations, leading to a too loose dynamic mAb network that governs the viscosity. Taken together, this study demonstrates the feasibility of all-atom MD simulations for predicting the properties of dense mAb solutions and provides detailed microscopic insights into the underlying molecular interactions. At the same time, it also shows that there is room for further improvements and highlights challenges, such as the massive sampling required for computing collective properties of dense biomolecular solutions in the high-viscosity regime with reasonable statistical precision.

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

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