Structural basis for SHOC2 modulation of RAS signalling.

The RAS-RAF pathway is one of the most commonly dysregulated in human cancers1-3. Despite decades of study, understanding of the molecular mechanisms underlying dimerization and activation4 of the kinase RAF remains limited. Recent structures of inactive RAF monomer5 and active RAF dimer5-8 bound to 14-3-39,10 have revealed the mechanisms by which 14-3-3 stabilizes both RAF conformations via specific phosphoserine residues. Prior to RAF dimerization, the protein phosphatase 1 catalytic subunit (PP1C) must dephosphorylate the N-terminal phosphoserine (NTpS) of RAF11 to relieve inhibition by 14-3-3, although PP1C in isolation lacks intrinsic substrate selectivity. SHOC2 is as an essential scaffolding protein that engages both PP1C and RAS to dephosphorylate RAF NTpS11-13, but the structure of SHOC2 and the architecture of the presumptive SHOC2-PP1C-RAS complex remain unknown. Here we present a cryo-electron microscopy structure of the SHOC2-PP1C-MRAS complex to an overall resolution of 3 Å, revealing a tripartite molecular architecture in which a crescent-shaped SHOC2 acts as a cradle and brings together PP1C and MRAS. Our work demonstrates the GTP dependence of multiple RAS isoforms for complex formation, delineates the RAS-isoform preference for complex assembly, and uncovers how the SHOC2 scaffold and RAS collectively drive specificity of PP1C for RAF NTpS. Our data indicate that disease-relevant mutations affect complex assembly, reveal the simultaneous requirement of two RAS molecules for RAF activation, and establish rational avenues for discovery of new classes of inhibitors to target this pathway.

Probing the Protein-Excipient Interaction in the Orally Delivered Protein by Solid-State Hydrogen-Deuterium Exchange Mass Spectrometry and Molecular Dynamics.

The oral administration of protein therapeutics in solid dosage form is gaining popularity due to its benefits, such as improved medication adherence, convenience, and ease of use for patients compared to traditional parental delivery. However, formulating oral biologics presents challenges related to pH barriers, enzymatic breakdown, and poor bioavailability. Therefore, understanding the interaction between excipients and protein therapeutics in the solid state is crucial for formulation development. In this Letter, we present a case study focused on investigating the role of excipients in protein aggregation during the production of a solid dosage form of a single variable domain on a heavy chain (VHH) protein. We employed solid-state hydrogen-deuterium exchange coupled with mass spectrometry (ssHDX-MS) at both intact protein and peptide levels to assess differences in protein-excipient interactions between two formulations. ssHDX-MS analysis revealed that one formulation effectively prevents protein aggregation during compaction by blocking ß-sheets across the VHH protein, thereby preventing ß-sheet-ß-sheet interactions. Spatial aggregation propensity (SAP) mapping and cosolvent simulation from molecular dynamics (MD) simulation further validated the protein-excipient interaction sites identified through ssHDX-MS. Additionally, the MD simulation demonstrated that the interaction between the VHH protein and excipients involves hydrophilic interactions and/or hydrogen bonding. This novel approach holds significant potential for understanding protein-excipient interactions in the solid state and can guide the formulation and process development of orally delivered protein dosage forms, ultimately enhancing their efficacy and stability.

Adapting Ferritin, a Naturally Occurring Protein Cage, to Modulate Intrinsic Agonism of OX40.

Ferritin is a multivalent, self-assembling protein scaffold found in most human cell types, in addition to being present in invertebrates, higher plants, fungi, and bacteria, that offers an attractive alternative to polymer-based drug delivery systems (DDS). In this study, the utility of the ferritin cage as a DDS was demonstrated within the context of T cell agonism for tumor killing. Members of the tumor necrosis factor receptor superfamily (TNFRSF) are attractive targets for the development of anticancer therapeutics. These receptors are endogenously activated by trimeric ligands that occur in transmembrane or soluble forms, and oligomerization and cell-surface anchoring have been shown to be essential aspects of the targeted agonism of this receptor class. Here, we demonstrated that the ferritin cage could be easily tailored for multivalent display of anti-OX40 antibody fragments on its surface and determined that these arrays are capable of pathway activation through cell-surface clustering. Together, these results confirm the utility, versatility, and developability of ferritin as a DDS.

AmberTools.

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Antibody-receptor interactions mediate antibody-dependent cellular cytotoxicity.

Binding of antibodies to their receptors is a core component of the innate immune system. Understanding the precise interactions between antibodies and their Fc receptors has led to the engineering of novel mAb biotherapeutics with tailored biological activities. One of the most significant findings is that afucosylated monoclonal antibodies demonstrate increased affinity toward the receptor FcγRIIIa, with a commensurate increase in antibody-dependent cellular cytotoxicity. Crystal structure analysis has led to the hypothesis that afucosylation in the Fc region results in reduced steric hindrance between antibody-receptor intermolecular glycan interactions, enhancing receptor affinity; however, solution-phase data have yet to corroborate this hypothesis. In addition, recent work has shown that the fragment antigen-binding (Fab) region may directly interact with Fc receptors; however, the biological consequences of these interactions remain unclear. By probing differences in solvent accessibility between native and afucosylated immunoglobulin G1 (IgG1) using hydroxyl radical footprinting-MS, we provide the first solution-phase evidence that an IgG1 bearing an afucosylated Fc region appears to require fewer conformational changes for FcγRIIIa binding. In addition, we performed extensive molecular dynamics (MD) simulations to understand the molecular mechanism behind the effects of afucosylation. The combination of these techniques provides molecular insight into the steric hindrance from the core Fc fucose in IgG1 and corroborates previously proposed Fab-receptor interactions. Furthermore, MD-guided rational mutagenesis enabled us to demonstrate that Fab-receptor interactions directly contribute to the modulation of antibody-dependent cellular cytotoxicity activity. This work demonstrates that in addition to Fc-polypeptide and glycan-mediated interactions, the Fab provides a third component that influences IgG-Fc receptor biology.

Multiscale Coarse-Grained Approach to Investigate Self-Association of Antibodies.

Self-association of therapeutic monoclonal antibodies (mabs) are thought to modulate the undesirably high viscosity observed in their concentrated solutions. Computational prediction of such a self-association behavior is advantageous early during mab drug candidate selection when material availability is limited. Here, we present a coarse-grained (CG) simulation method that enables microsecond molecular dynamics simulations of full-length antibodies at high concentrations. The proposed approach differs from others in two ways: first, charges are assigned to CG beads in an effort to reproduce molecular multipole moments and charge asymmetry of full-length antibodies instead of only localized charges. This leads to great improvements in the agreement between CG and all-atom electrostatic fields. Second, the distinctive hydrophobic character of each antibody is incorporated through empirical adjustments to the short-range van der Waals terms dictated by cosolvent all-atom molecular dynamics simulations of antibody variable regions. CG simulations performed on a set of 15 different mabs reveal that diffusion coefficients in crowded environments are markedly impacted by intermolecular interactions. Diffusion coefficients computed from the simulations are in correlation with experimentally measured observables, including viscosities at a high concentration. Further, we show that the evaluation of electrostatic and hydrophobic characters of the mabs is useful in predicting the nonuniform effect of salt on the viscosity of mab solutions. This CG modeling approach is particularly applicable as a material-free screening tool for selecting antibody candidates with desirable viscosity properties.

Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences.

Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first "Highland Games" competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development-related properties of six antibodies from their amino acid sequences alone. Predictions included purification-influencing properties such as isoelectric point and protein A elution pH, and biophysical properties such as stability and viscosity at very high concentrations. Essential contributions were made by a large variety of individuals, including companies which consented to provide antibody amino acid sequences and test materials, volunteers who undertook the preparation and experimental characterization of these materials, and prediction teams who attempted to predict antibody properties from sequence alone. Best practices were identified and shared, and areas in which the community excels at making predictions were identified, as well as areas presenting opportunities for considerable improvement. Predictions of isoelectric point and protein A elution pH were especially good with all-prediction average errors of 0.2 and 1.6 pH unit, respectively, while predictions of some other properties were notably less good. This manuscript presents the events, methods, and results of the competition, and can serve as a tutorial and as a reference for in-house benchmarking by others. Organizations vary in their policies concerning disclosure of methods, but most managements were very cooperative with the Highland Games exercise, and considerable insight into common and best practices is available from the contributed methods. The accumulated data set will serve as a benchmarking tool for further development of in silico prediction tools.

Trehalose Limits Fragment Antibody Aggregation and Influences Charge Variant Formation in Spray-Dried Formulations at Elevated Temperatures.

The preparation of PLGA rods for sustained release applications via a hot-melt extrusion process employs heat and mechanical shear. Understanding protein stability and degradation mechanisms at high temperature in the solid state is therefore important for the preparation of protein-loaded PLGA rods. The stability of a model protein, labeled Fab2, has been investigated in solid-state formulations containing trehalose at elevated temperatures. Spray-dried formulations containing varying levels of trehalose were exposed to temperatures ranging from 90 to 120 °C. Measurement of aggregation and chemical degradation rates suggests that trehalose limits Fab2 degradation in a concentration-dependent manner, but the effect tends to saturate when the mass ratio of trehalose to protein is around 1 in the solid formulation. The Fab2 secondary structure and spray-dried particle morphology were studied using circular dichroism and scanning electron microscopy techniques, respectively. On the basis of temperature and trehalose-dependent aggregation kinetics as well as changes in spray-dried particle morphology, a mechanism is proposed for the trehalose stabilization of proteins in solid state at elevated temperatures. The results reported here suggest that when fragment antibodies in the solid state are formulated with trehalose as excipient, a high temperature process such as hot-melt extrusion can be successfully accomplished with minimal degradation.

Grid-Based Surface Generalized Born Model for Calculation of Electrostatic Binding Free Energies.

Fast and accurate calculation of solvation free energies is central to many applications, such as rational drug design. In this study, we present a grid-based molecular surface implementation of "R6" flavor of the generalized Born (GB) implicit solvent model, named GBNSR6. The speed, accuracy relative to numerical Poisson-Boltzmann treatment, and sensitivity to grid surface parameters are tested on a set of 15 small protein-ligand complexes and a set of biomolecules in the range of 268 to 25099 atoms. Our results demonstrate that the proposed model provides a relatively successful compromise between the speed and accuracy of computing polar components of the solvation free energies (ΔGpol) and binding free energies (ΔΔGpol). The model tolerates a relatively coarse grid size h = 0.5 Å, where the grid artifact error in computing ΔΔGpol remains in the range of kBT ∼ 0.6 kcal/mol. The estimated ΔΔGpols are well correlated (r2 = 0.97) with the numerical Poisson-Boltzmann reference, while showing virtually no systematic bias and RMSE = 1.43 kcal/mol. The grid-based GBNSR6 model is available in Amber (AmberTools) package of molecular simulation programs.

Accuracy limit of rigid 3-point water models.

Classical 3-point rigid water models are most widely used due to their computational efficiency. Recently, we introduced a new approach to constructing classical rigid water models [S. Izadi et al., J. Phys. Chem. Lett. 5, 3863 (2014)], which permits a virtually exhaustive search for globally optimal model parameters in the sub-space that is most relevant to the electrostatic properties of the water molecule in liquid phase. Here we apply the approach to develop a 3-point Optimal Point Charge (OPC3) water model. OPC3 is significantly more accurate than the commonly used water models of same class (TIP3P and SPCE) in reproducing a comprehensive set of liquid bulk properties, over a wide range of temperatures. Beyond bulk properties, we show that OPC3 predicts the intrinsic charge hydration asymmetry (CHA) of water - a characteristic dependence of hydration free energy on the sign of the solute charge - in very close agreement with experiment. Two other recent 3-point rigid water models, TIP3PFB and H2ODC, each developed by its own, completely different optimization method, approach the global accuracy optimum represented by OPC3 in both the parameter space and accuracy of bulk properties. Thus, we argue that an accuracy limit of practical 3-point rigid non-polarizable models has effectively been reached; remaining accuracy issues are discussed.

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.

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.

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.

Predicting Formulation Conditions During Ultrafiltration and Dilution to Drug Substance Using a Donnan Model with Homology-Model Based Protein Charge.

In the manufacturing of therapeutic monoclonal antibodies (mAbs), the final steps of the purification process are typically ultrafiltration/diafiltration (UF/DF), dilution, and conditioning. These steps are developed such that the final drug substance (DS) is formulated to the desired mAb, buffer, and excipient concentrations. To develop these processes, process and formulation development scientists often perform experiments to account for the Gibbs-Donnan and volume-exclusion effects during UF/DF, which affect the output pH and buffer concentration of the UF/DF process. This work describes the development of an in silico model for predicting the DS pH and buffer concentration after accounting for the Gibbs-Donnan and volume-exclusion effects during the UF/DF operation and the subsequent dilution and conditioning steps. The model was validated using statistical analysis to compare model predictions against experimental results for nine molecules of varying protein concentrations and formulations. In addition, our results showed that the structure-based in silico approach used to calculate the protein charge was more accurate than a sequence-based approach. Finally, we used the model to gain fundamental insights about the Gibbs-Donnan effect by highlighting the role of the protein charge concentration (the protein concentration multiplied with protein charge at the formulation pH) on the Gibbs-Donnan effect. Overall, this work demonstrates that the Gibbs-Donnan and volume-exclusions effects can be predicted using an in silico model, potentially alleviating the need for experiments.

Function-structure approach reveals novel insights on the interplay of Immunoglobulin G 1 proteoforms and Fc gamma receptor IIa allotypes.

Human Fc gamma receptor IIa (FcγRIIa) or CD32a has two major allotypes with a single amino acid difference at position 131 (histidine or arginine). Differences in FcγRIIa allotypes are known to impact immunological responses such as the clinical outcome of therapeutic monoclonal antibodies (mAbs). FcγRIIa is involved in antibody-dependent cellular phagocytosis (ADCP), which is an important contributor to the mechanism-of-action of mAbs by driving phagocytic clearance of cancer cells. Hence, understanding the impact of individual mAb proteoforms on the binding to FcγRIIa, and its different allotypes, is crucial for defining meaningful critical quality attributes (CQAs). Here, we report a function-structure based approach guided by novel FcγRIIa affinity chromatography-mass spectrometry (AC-MS) assays to assess individual IgG1 proteoforms. This allowed to unravel allotype-specific differences of IgG1 proteoforms on FcγRIIa binding. FcγRIIa AC-MS confirmed and refined structure-function relationships of IgG1 glycoform interactions. For example, the positive impact of afucosylation was higher than galactosylation for FcγRIIa Arg compared to FcγRIIa His. Moreover, we observed FcγRIIa allotype-opposing and IgG1 proteoform integrity-dependent differences in the binding response of stress-induced IgG1 proteoforms comprising asparagine 325 deamidation. The FcγRIIa-allotype dependent binding differences resolved by AC-MS were in line with functional ADCP-surrogate bioassay models. The molecular basis of the observed allotype specificity and proteoform selectivity upon asparagine 325 deamidation was elucidated using molecular dynamics. The observed differences were attributed to the contributions of an inter-molecular salt bridge between IgG1 and FcγRIIa Arg and the contribution of an intra-molecular hydrophobic pocket in IgG1. Our work highlights the unprecedented structural and functional resolution of AC-MS approaches along with predictive biological significance of observed affinity differences within relevant cell-based methods. This makes FcγRIIa AC-MS an invaluable tool to streamline the CQA assessment of therapeutic mAbs.

Deciphering deamidation and isomerization in therapeutic proteins: Effect of neighboring residue.

Deamidation of asparagine (Asn) and isomerization of aspartic acid (Asp) residues are among the most commonly observed spontaneous post-translational modifications (PTMs) in proteins. Understanding and predicting a protein sequence's propensity for such PTMs can help expedite protein therapeutic discovery and development. In this study, we used proton-affinity calculations with semi-empirical quantum mechanics and microsecond long equilibrium molecular dynamics simulations to investigate mechanistic roles of structural conformation and chemical environment in dictating spontaneous degradation of Asn and Asp residues in 131 clinical-stage therapeutic antibodies. Backbone secondary structure, side-chain rotamer conformation and solvent accessibility were found to be key molecular indicators of Asp isomerization and Asn deamidation. Comparative analysis of backbone dihedral angles along with N-H proton affinity calculations provides a mechanistic explanation for the strong influence of the identity of the n + 1 residue on the rate of Asn/Asp degradation. With these findings, we propose a minimalistic physics-based classification model that can be leveraged to predict deamidation and isomerization propensity of proteins.

Fast Polarizable Water Model for Atomistic Simulations.

Simulating water accurately has been a major challenge in atomistic simulations for decades. Inclusion of electronic polarizability effects holds considerable promise, yet existing approaches suffer from significant computational overheads compared to the widely used nonpolarizable water models. We have developed a globally optimal polarizable water model, OPC3-pol, that explicitly accounts for electronic polarizability with minimal impact on the computational efficiency. OPC3-pol reproduces five key bulk water properties at room temperature with an average relative error of 0.6%. In atomistic simulations, OPC3-pol's computational efficiency is in between that of 3- and 4-point nonpolarizable models; the model supports increased (4 fs) integration time step. OPC3-pol is tested in simulations of globular protein ubiquitin and a B-DNA dodecamer with several AMBER force fields, ff99SB, ff14SB, ff19SB, and OL15, demonstrating structure stability close to reference on multi-microsecond time scale. Simulation of an intrinsically disordered amyloid ß-peptide yields an ensemble with the radius of gyration of a random coil. The proposed water model can be trivially adopted by any package that supports standard nonpolarizable force fields and water models; its intended use is in long classical atomistic simulations where water polarization effects are expected to be important.

Vitamin D3 administration prevents memory deficit and alteration of biochemical parameters induced by unpredictable chronic mild stress in rats.

The present study aimed to investigate the effects of vitamin D3 (Vit D) administration on memory function, hippocampal level of amyloid-beta (Aß), brain-derived neurotrophic factor (BDNF) and oxidative stress status in a rat model of unpredictable chronic mild stress (UCMS). Vit D was intraperitoneally administered at doses of 100, 1000, and 10,000 IU/kg. Animals were subjected to UCMS for a total period of 4 weeks. Memory function was assessed using morris water maze (MWM) and passive avoidance (PA) tests. Biochemical markers were measured to reveal the status of oxidative stress and antioxidant defense system. In addition, the levels of Aß and BDNF were measured in hippocampal region. In the UCMS group, latency to find the platform was greater and the time spent in target quadrant (MWM test) as well as the latency to enter the dark compartment (PA test), were less than the vehicle group. Hippocampal malondialdehyde (MDA) and Aß concentrations in the UCMS group were higher than the vehicle group. Hippocampal level of thiol and BDNF plus the activities of catalase and superoxide dismutase (SOD) were reduced in UCMS group compared to the control subjects (i.e. vehicle group). Interestingly, Vit D treatment supplementation reversed the mentioned effects of UCMS. Our findings indicated that Vit D administration improves UCMS-induced impairment of learning and memory through prevention of adverse effects on Aß, BDNF and oxidative stress parameters.

Beneficial effects of vitamin D on anxiety and depression-like behaviors induced by unpredictable chronic mild stress by suppression of brain oxidative stress and neuroinflammation in rats.

The objective of this study was to investigate the protective effects of vitamin D (Vit D) on anxiety and depression-like behaviors induced by unpredictable chronic mild stress and brain tissue oxidative damage criteria and neuroinflammation in rats. The rats were treated as follows: (1) control, (2) UCMS, (3-5) Vit D 100, 1000, and 10,000 iu + UCMS. Rats were subjected to UCMS for a total of 4 weeks. During week 4, they received seven training trials. The brains were then collected to examine inflammation and oxidative stress criteria. Pretreatment with Vit D enhanced performances of the rats in the elevated plus maze (EPM) and open field (OF) and forced swimming test (FST). UCMS also increased MDA and interleukin-6 (IL-6) levels while decreased CAT, SOD, and thiol. Vit D reversed the effects of UCMS. The results of the current research revealed that Vit D improved UCMS-induced anxiety and depression via decreasing brain oxidative stress and inhibiting neuroinflammation.

Fc galactosylation follows consecutive reaction kinetics and enhances immunoglobulin G hexamerization for complement activation.

Fc galactosylation is a critical quality attribute for anti-tumor recombinant immunoglobulin G (IgG)-based monoclonal antibody (mAb) therapeutics with complement-dependent cytotoxicity (CDC) as the mechanism of action. Although the correlation between galactosylation and CDC has been known, the underlying structure-function relationship is unclear. Heterogeneity of the Fc N-glycosylation produced by Chinese hamster ovary (CHO) cell culture biomanufacturing process leads to variable CDC potency. Here, we derived a kinetic model of galactose transfer reaction in the Golgi apparatus and used this model to determine the correlation between differently galactosylated species from CHO cell culture process. The model was validated by a retrospective data analysis of more than 800 historical samples from small-scale and large-scale CHO cell cultures. Furthermore, using various analytical technologies, we discovered the molecular basis for Fc glycan terminal galactosylation changing the three-dimensional conformation of the Fc, which facilitates the IgG1 hexamerization, thus enhancing C1q avidity and subsequent complement activation. Our study offers insight into the formation of galactosylated species, as well as a novel three-dimensional understanding of the structure-function relationship of terminal galactose to complement activation in mAb therapeutics.