Structural remodeling of SARS-CoV-2 spike protein glycans reveals the regulatory roles in receptor-binding affinity.

Glycans of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein are speculated to play functional roles in the infection processes as they extensively cover the protein surface and are highly conserved across the variants. The spike protein has been the principal target for vaccine and therapeutic development while the exact effects of its glycosylation remain elusive. Analytical reports have described the glycan heterogeneity of the spike protein. Subsequent molecular simulation studies provided a knowledge basis of the glycan functions. However, experimental data on the role of discrete glycoforms on the spike protein pathobiology remains scarce. Building an understanding of their roles in SARS-CoV-2 is important as we continue to develop effective medicines and vaccines to combat the disease. Herein, we used designed combinations of glycoengineering enzymes to simplify and control the glycosylation profile of the spike protein receptor-binding domain (RBD). Measurements of the receptor-binding affinity revealed opposite regulatory effects of the RBD glycans with and without sialylation, which presents a potential strategy for modulating the spike protein behaviors through glycoengineering. Moreover, we found that the reported anti-SARS-CoV-(2) antibody, S309, neutralizes the impact of different RBD glycoforms on the receptor-binding affinity. In combination with molecular dynamics simulation, this work reports the regulatory roles that glycosylation plays in the interaction between the viral spike protein and host receptor, providing new insights into the nature of SARS-CoV-2. Beyond this study, enzymatic glycan remodeling offers the opportunity to understand the fundamental role of specific glycoforms on glycoconjugates across molecular biology.

In Silico Method Development of Achiral and Chiral Tandem Column Reversed-phase Liquid Chromatography for Multicomponent Pharmaceutical Mixtures.

Tandem column liquid chromatography (LC) is a convenient, cost-effective approach to resolve multicomponent mixtures by serially coupling columns on readily available one-dimensional separation systems without specialized user training. Yet, adoption of this technique remains limited, mainly due to the difficulty in identifying optimal selectivity out of many possible tandem column combinations. At this point, method development and optimization require laborious "hit-or-miss" experimentation and "blind" screening when investigating different column selectivity without standard analytes. As a result, many chromatography practitioners end up combining two columns of similar selectivity, limiting the scope and potential of tandem column LC as a mainstay for industrial applications. To circumvent this challenge, we herein introduce a straightforward in silico multifactorial approach as a framework to expediently map the separation landscape across multiple tandem columns (achiral and chiral) and eluent combinations (isocratic and gradient elution) under reversed-phase LC conditions. Retention models were built using commercially available LC simulator software showcasing less than 2% difference between experimental and simulated retention times for analytes of interest in multicomponent pharmaceutical mixtures (e.g., metabolites and cyclic peptides).

Automated Hydrophobic Interaction Chromatography Screening Combined with In Silico Optimization as a Framework for Nondenaturing Analysis and Purification of Biopharmaceuticals.

The mounting complexity of new modalities in the biopharmaceutical industry entails a commensurate level of analytical innovations to enable the rapid discovery and development of novel therapeutics and vaccines. Hydrophobic interaction chromatography (HIC) has become one of the widely preferred separation techniques for the analysis and purification of biopharmaceuticals under nondenaturing conditions. Inarguably, HIC method development remains very challenging and labor-intensive owing to the numerous factors that are typically optimized by a "hit-or-miss" strategy (e.g., the nature of the salt, stationary phase chemistry, temperature, mobile phase additive, and ionic strength). Herein, we introduce a new HIC method development framework composed of a fully automated multicolumn and multieluent platform coupled with in silico multifactorial simulation and integrated fraction collection for streamlined method screening, optimization, and analytical-scale purification of biopharmaceutical targets. The power and versatility of this workflow are showcased by a wide range of applications including trivial proteins, monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), oxidation variants, and denatured proteins. We also illustrate convenient and rapid HIC method development outcomes from the effective combination of this screening setup with computer-assisted simulations. HIC retention models were built using readily available LC simulator software outlining less than a 5% difference between experimental and simulated retention times with a correlation coefficient of >0.99 for pharmaceutically relevant multicomponent mixtures. In addition, we demonstrate how this approach paves the path for a straightforward identification of first-dimension HIC conditions that are combined with mass spectrometry (MS)-friendly reversed-phase liquid chromatography (RPLC) detection in the second dimension (heart-cutting two-dimensional (2D)-HIC-RPLC-diode array detector (DAD)-MS), enabling the analysis and purification of biopharmaceutical targets.

Rapid antibody conformational screening by matrix-assisted laser desorption/ionization hydrogen-deuterium exchange mass spectrometry.

Recent advances in the field of cancer biology have accelerated the discovery and development of novel biopharmaceuticals. At the forefront of these drug development efforts are high-throughput screening, compressed timelines, and limited sample quantities, all characteristic of the discovery space. To meet program targets, large numbers of protein variants must be produced, screened, and characterized, presenting a daunting analytical challenge. Additionally, the higher-order structure is paramount for protein function and must be monitored as a critical quality attribute. Matrix-assisted laser desorption/ionization mass spectrometry has been utilized as an ultra-fast, automatable, sample-sparing analytical tool for biomolecules. Our group has published applications integrating hydrogen-deuterium exchange mass spectrometry with matrix-assisted laser desorption/ionization mass spectrometry for the rapid conformational characterization of small proteins, the current work expands this application to monoclonal and bi-specific antibodies. This study demonstrates the ability of the methodology, matrix-assisted laser desorption/ionization hydrogen-deuterium exchange mass spectrometry, to detect conformational differences between bi-specific antibodies from different expression hosts. These conformational differences were validated by orthogonal techniques including circular dichroism, nuclear magnetic resonance, and size-exclusion chromatography hydrogen-deuterium exchange mass spectrometry. This work demonstrates the utility of applying the developed methodology as a rapid conformational screening tool to triage samples for further analytical characterization.

Trapping-Enrichment Multi-dimensional Liquid Chromatography with On-Line Deuterated Solvent Exchange for Streamlined Structure Elucidation at the Microgram Scale.

At the forefront of chemistry and biology research, development timelines are fast-paced and large quantities of pure targets are rarely available. Herein, we introduce a new framework, which is built upon an automated, online trapping-enrichment multi-dimensional liquid chromatography platform (TE-Dt-mDLC) that enables: 1) highly efficient separation of complex mixtures in a first dimension (1 D-UV); 2) automated peak trapping-enrichment and buffer removal achieved through a sequence of H2 O and D2 O washes using an independent pump setup; and 3) a second dimension separation (2 D-UV-MS) with fully deuterated mobile phases and fraction collection to minimize protic residues for immediate NMR analysis while bypassing tedious drying processes and minimizing analyte degradation. Diverse examples of target isolation and characterization from organic synthesis and natural product chemistry laboratories are illustrated, demonstrating recoveries above 90 % using as little as a few micrograms of material.

In Silico Multifactorial Modeling for Streamlined Development and Optimization of Two-Dimensional Liquid Chromatography.

Continued adoption of two-dimensional liquid chromatography (2D-LC) in industrial laboratories will depend on the development of approaches to make method development for 2D-LC more systematic, less tedious, and less reliant on user expertise. In this paper, we build on previous efforts in these directions by describing the use of multifactorial modeling software that can help streamline and simplify the method development process for 2D-LC. Specifically, we have focused on building retention models for second dimension (2D) separations involving variables including gradient time, temperature, organic modifier blending, and buffer concentration using LC simulator (ACD/Labs) software. Multifactorial retention modeling outcomes are illustrated as resolution map planes or cubes that enable straightforward location of 2D conditions that maximize resolution while minimizing analysis time. We also illustrate the practicality of this approach by identifying conditions that yield baseline separation of all compounds co-eluting from a first dimension (1D) separation using a single combination of 2D stationary phase and elution conditions. The multifactorial retention models were found to be very accurate for both the 1D and 2D separations, with differences between experimental and simulated retention times of less than 0.5%. Pharmaceutical applications of this approach for multiple heartcutting 2D-LC were demonstrated using IEC-IEC or achiral RPLC-chiral RPLC for 2D separations of multicomponent mixtures. The framework outlined here should help make 2D-LC method development more systematic and streamline development and optimization for a variety of 2D-LC applications in both industry and academia.

Mapping the Separation Landscape in Two-Dimensional Liquid Chromatography: Blueprints for Efficient Analysis and Purification of Pharmaceuticals Enabled by Computer-Assisted Modeling.

Recent developments in two-dimensional liquid chromatography (2D-LC) now make separation and analysis of very complex mixtures achievable. Despite being such a powerful chromatographic tool, current 2D-LC technology requires a series of arduous method development activities poorly suited for a fast-paced industrial environment. Recent introductions of new technologies including active solvent modulation and a support for multicolumn 2D-LC are helping to overcome this stigma. However, many chromatography practitioners believe that the lack of a systematic way to effectively optimize 2D-LC separations is a missing link in securing the viability of 2D-LC as a mainstay for industrial applications. In this work, a computer-assisted modeling approach that dramatically simplifies both offline and online 2D-LC method developments is introduced. Our methodology is based on mapping the separation landscape of pharmaceutically relevant mixtures across both first (1D) and second (2D) dimensions using LC Simulator (ACD/Labs) software. Retention models for 1D and 2D conditions were built using a minimal number of multifactorial modeling experiments (2 × 2 or 3 × 3 parameters: gradient slope, column temperature, and different column and mobile phase combinations). The approach was first applied to online 2D-LC analysis involving achiral and chiral separations of complex mixtures of enantiomeric species. In these experiments, the retention models proved to be quite accurate for both the 1D and 2D separations, with retention time differences between experiments and simulations of less than 3.5%. This software-based concept was also demonstrated for offline 2D-LC purification of drug substances.

Introducing Multifactorial Peak Crossover in Analytical and Preparative Chromatography via Computer-Assisted Modeling.

Modern pharmaceutical processes can often lead to multicomponent mixtures of closely related species that are difficult to resolve under chromatographic conditions, and even worse in preparative scale settings. Despite recent improvements in column technology and instrumentation, there remains an urgent need for creating innovative approaches that address challenging coelutions of critical pair and poor chromatographic productivity of purification methods. Herein, we overcome these challenges by introducing a simple and practical technique named multifactorial peak crossover (MPC) via computer-assisted chromatographic modeling. The approach outlined here focuses on mapping the separation landscape of pharmaceutical mixtures to quickly identify spaces of peak coelution crossings which enables one to conveniently switch the elution order of target analytes. Diverse examples of MPC diagrams as a function of column temperature, mobile phase gradient or a multifactorial combination in reversed phase and ion exchange chromatography (RPLC and IEC) modes are generated using ACD Laboratories/LC Simulator software and corroborated with experimental data match (overall retention time differences of less than 1%). This powerful MPC technique allows us to gain massive productivity increases (shorter cycle time and higher sample loading) for purification of pharmaceuticals by selectively switching the elution order of target components away from undesired tailing peaks and coelution spaces. MPC chromatography dramatically reduces the time spent developing productive analytical and preparative scale separations. In addition, we illustrate how this new MPC concept can be used to gain substantial improvements of the signal-to-noise ratio, enabling straightforward ppb detection of low-level target components with direct impact in the quantitation of metabolites and potential genotoxic impurities (PGIs). These innovations are of paramount importance in order to facilitate efficient isolation, characterization, and quantitation of drug substances in the development of new medicines.

Ultra-high-throughput SPE-MALDI workflow: Blueprint for efficient purification and screening of peptide libraries.

At the forefront of synthetic endeavors in the pharmaceutical industry, including drug discovery and high-throughput screening, timelines are tight and large quantities of pure chemical targets are rarely available. In this regard, the development of novel and increasingly challenging chemistries requires a commensurate level of innovation to develop reliable analytical assays and purification workflows with rapid turnaround that enables accelerated pharmacological evaluation. A small-scale automation platform enabling high-throughput analysis and purification to streamline the selection of candidate leads would be a transformative advance. Herein, we introduce an automation-friendly solid-phase extraction-matrix-assisted laser desorption/ionization (SPE-MALDI) platform applied to the high-throughput purification and analysis of peptide libraries. This advance enabled us to purify peptides from microgram levels in less than a day with results comparable to traditional high-performance liquid chromatography-diode array detection-mass spectrometry (HPLC-DAD-MS).

In silico method development for the reversed-phase liquid chromatography separation of proteins using chaotropic mobile phase modifiers.

Recent advances in biomedical and pharmaceutical processes has enabled a notable increase of protein- and peptide-based drug therapies and vaccines that often contain a higher-order structure critical to their efficacy. Hyphenation of chromatographic and spectrometric techniques is at the center of all facets of biopharmaceutical analysis, purification and chemical characterization. Although computer-assisted chromatographic modeling of small molecules has reached a mature stage across the pharmaceutical industry, software-based method optimization approaches for large molecules has yet to see the same revitalization. Conformational changes of biomolecules under chromatographic conditions have been identified as the major culprit in terms of sub-optimal modeling outcomes. In order to circumvent these challenges, we herein investigate the outcomes generated via computer-assisted modeling from using different chaotropic and denaturing mobile phases (trifluoroacetic acid, sodium perchlorate and guanidine hydrochloride in acetonitrile/water-based eluents). Linear and polynomial regression retention models using ACD/Labs software were built as a function of gradient slope, column temperature and mobile phase buffer for eight different model proteins ranging from 12 to 670 kDa (holo-transferrin, cytochrome C, apomyoglobin, ribonuclease A, ribonuclease A type I-A, albumin, y-globulin and thyroglobulin bovine). Correlation between experimental and modeled outputs was substantially improved by using strong chaotropic and denaturing modifiers in the mobile phase, even when using linear regression modeling as typically observed for small molecules. On the contrary, the use of conventional TFA buffer concentrations at low column temperatures required the used of polynomial regression modeling indicating potential conformational structure changes of proteins upon chromatographic conditions. In addition, we illustrate the power of modern computer-assisted chromatography modeling combined with chaotropic agents in the developing of new RPLC assays for protein-based therapeutics and vaccines.

Liposome Artificial Membrane Permeability Assay by MALDI-hydrogen-deuterium exchange mass spectrometry for peptides and small proteins.

The pharmaceutical industry's focus has expanded to include peptide and protein-based therapeutics; however, some analytical challenges have arisen along the way, including the urgent need for fast and robust measurement of the membrane permeability of peptides and small proteins. In this study, a simple and efficient approach that utilizes MALDI-TOF-MS to study peptide and protein permeability through an artificial liposome membrane in conjunction with a differential hydrogen-deuterium exchange (HDX) methodology is described. A non-aqueous (aprotic) matrix was evaluated for use with MALDI sample preparation in order to eliminate undesirable hydrogen-deuterium back-exchange. Peptides and proteins were incubated with liposomes and their penetration into the liposome membrane over time was measured by MALDI-MS. A differential HDX approach was used to distinguish the peptides outside of the liposome from those inside. In this regard, the peptides on the outside of the liposomes were labeled using short exposure to deuterium oxide, while the peptides inside of the liposomes were protected from labeling. Subsequently, the unlabeled versus labeled peak area ratios for peptide and protein samples were compared using MALDI-TOF-MS. In this proof-of-concept study, we developed the Liposome Artificial Membrane Permeability Assay (LAMPA) workflow to study three well-known membrane-active model peptides (melittin, alamethicin, and gramicidin) and two model proteins (aprotinin and ubiquitin). The permeability results obtained from this were corroborated by previously reported data for studied peptides and proteins. The proposed LAMPA by MALDI-HDX-MS can be applied in an ultra-high-throughput manner for studying and rank-ordering membrane permeability of peptides and small proteins.