Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning

Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including autism, Alzheimer's disease, dementia, cardiovascular disease, dry eye disease, and diabetes, and thus are molecules worthy of biomarker exploration studies. Since triacylglycerols and wax esters make up the majority of skin-surface lipid secretions, a viable sampling method for these potential biomarkers would be that of groomed latent fingerprints. Currently, however, blood-based sampling protocols predominate in the field. The invasiveness of a blood draw limits its utility to protected populations, including children and the elderly. Herein we describe a noninvasive means for sample collection (from fingerprints) paired with fast MS data-acquisition (MassIVE data set MSV000092742) and efficient data analysis via machine learning. Using both supervised and unsupervised classification, we demonstrate the usefulness of this method in determining whether a variable of interest imparts measurable change within the lipidomic data set. As a proof-of-concept, we show that the method is capable of distinguishing between the fingerprints of different individuals as well as between anatomical sebum collection regions. This noninvasive, high-throughput approach enables future lipidomic biomarker researchers to more easily include underrepresented, protected populations, such as children and the elderly, thus moving the field closer to definitive disease diagnoses that apply to all.

Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning

To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent nature of normalization have led some researchers to develop open-source academic software for choosing the best approach. While these tools are certainly beneficial to the community, none of them meet all of the needs of all users, particularly users who want to test new strategies that are not available in these products. Herein, we present a simple and straightforward workflow that facilitates the identification of optimal normalization strategies using straightforward evaluation metrics, employing both supervised and unsupervised machine learning. The workflow offers a "DIY" aspect, where the performance of any normalization strategy can be evaluated for any type of MS data. As a demonstration of its utility, we apply this workflow on two distinct datasets, an ESI-MS dataset of extracted lipids from latent fingerprints and a cancer spheroid dataset of metabolites ionized by MALDI-MSI, for which we identified the best-performing normalization strategies.

Accurately detecting AI text when ChatGPT is told to write like a chemist

Large language models like ChatGPT can generate authentic-seeming text at lightning speed, but many journal publishers reject language models as authors on manuscripts. Thus, a means to accurately distinguish human-generated from artificial intelligence (AI)-generated text is immediately needed. We recently developed an accurate AI text detector for scientific journals and, herein, test its ability in a variety of challenging situations, including on human text from a wide variety of chemistry journals, on AI text from the most advanced publicly available language model (GPT-4), and, most important, on AI text generated using prompts designed to obfuscate AI use. In all cases, AI and human text was assigned with high accuracy. ChatGPT-generated text can be readily detected in chemistry journals; this advance is a fundamental prerequisite for understanding how automated text generation will impact scientific publishing from now into the future.

Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools

ChatGPT has enabled access to artificial intelligence (AI)-generated writing for the masses, initiating a culture shift in the way people work, learn, and write. The need to discriminate human writing from AI is now both critical and urgent. Addressing this need, we report a method for discriminating text generated by ChatGPT from (human) academic scientists, relying on prevalent and accessible supervised classification methods. The approach uses new features for discriminating (these) humans from AI; as examples, scientists write long paragraphs and have a penchant for equivocal language, frequently using words like "but," "however," and "although." With a set of 20 features, we built a model that assigns the author, as human or AI, at over 99% accuracy. This strategy could be further adapted and developed by others with basic skills in supervised classification, enabling access to many highly accurate and targeted models for detecting AI usage in academic writing and beyond.

Asymmetric conformations of cleaved HIV-1 envelope glycoprotein trimers in styrene-maleic acid lipid nanoparticles

During virus entry, the pretriggered human immunodeficiency virus (HIV-1) envelope glycoprotein (Env) trimer initially transits into a default intermediate state (DIS) that remains structurally uncharacterized. Here, we present cryo-EM structures at near-atomic resolution of two cleaved full-length HIV-1 Env trimers purified from cell membranes in styrene-maleic acid lipid nanoparticles without antibodies or receptors. The cleaved Env trimers exhibited tighter subunit packing than uncleaved trimers. Cleaved and uncleaved Env trimers assumed remarkably consistent yet distinct asymmetric conformations, with one smaller and two larger opening angles. Breaking conformational symmetry is allosterically coupled with dynamic helical transformations of the gp41 N-terminal heptad repeat (HR1N) regions in two protomers and with trimer tilting in the membrane. The broken symmetry of the DIS potentially assists Env binding to two CD4 receptors-while resisting antibody binding-and promotes extension of the gp41 HR1 helical coiled-coil, which relocates the fusion peptide closer to the target cell membrane.

Quantitative clinical glycomics strategies: A guide for selecting the best analysis approach

Glycans introduce complexity to the proteins to which they are attached. These modifications vary during the progression of many diseases; thus, they serve as potential biomarkers for disease diagnosis and prognosis. The immense structural diversity of glycans makes glycosylation analysis and quantitation difficult. Fortunately, recent advances in analytical techniques provide the opportunity to quantify even low-abundant glycopeptides and glycans derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Understanding the strengths and weaknesses of different quantitative glycomics analysis methods is important for selecting the best strategy to analyze glycosylation changes in any given set of clinical samples. To provide guidance towards selecting the proper approach, we discuss four widely used quantitative glycomics analysis platforms, including fluorescence-based analysis of released N-linked glycans and three different varieties of MS-based analysis: liquid chromatography (LC)-mass spectrometry (MS) analysis of glycopeptides, matrix-assisted laser desorption ionization-time of flight MS, and LC-ESI-MS analysis of released N-linked glycans. These methods' strengths and weaknesses are compared, particularly associated with the figures of merit that are important for clinical biomarker studies, including: the initial sample requirements, the methods' throughput, sample preparation time, the number of species identified, the methods' utility for isomer separation and structural characterization, method-related challenges associated with quantitation, repeatability, the expertise required, and the cost for each analysis. This review, therefore, provides unique guidance to researchers who endeavor to undertake a clinical glycomics analysis by offering insights on the available analysis technologies.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

How (Not) to Generate a Highly Predictive Biomarker Panel Using Machine Learning

This review "teaches" researchers how to make their lackluster proteomics data look really impressive, by applying an inappropriate but pervasive strategy that selects features in a biased manner. The strategy is demonstrated and used to build a classification model with an accuracy of 92% and AUC of 0.98, while relying completely on random numbers for the data set. This "lesson" in data processing is not to be practiced by anyone; on the contrary, it is meant to be a cautionary tale showing that very unreliable results are obtained when a biomarker panel is generated first, using all the available data, and then tested by cross-validation. Data scientists describe the error committed in this scenario as having test data leak into the feature selection step, and it is currently a common mistake in proteomics biomarker studies that rely on machine learning. After the demonstration, advice is provided about how machine learning methods can be applied to proteomics data sets without generating artificially inflated accuracies.

Exposing the Brain Proteomic Signatures of Alzheimer's Disease in Diverse Racial Groups: Leveraging Multiple Data Sets and Machine Learning

Recent studies have highlighted that the proteome can be used to identify potential biomarker candidates for Alzheimer's disease (AD) in diverse cohorts. Furthermore, the racial and ethnic background of participants is an important factor to consider to ensure the effectiveness of potential biomarkers for representative populations. A promising approach to survey potential biomarker candidates for diagnosing AD in diverse cohorts is the application of machine learning to proteomics data sets. Herein, we leveraged six existing bottom-up proteomics data sets, which included non-Hispanic White, African American/Black, and Hispanic participants, to study protein changes in AD and cognitively unimpaired participants. Machine learning models were applied to these data sets and resulted in the identification of amyloid-β precursor protein (APP) and heat shock protein β-1 (HSPB1) as two proteins that have high ability to distinguish AD; however, each protein's performance varied based upon the racial and ethnic background of the participants. HSPB1 particularly was helpful for generating high areas under the curve (AUCs) for African American/Black participants. Overall, HSPB1 improved the performance of the machine learning models when combined with APP and/or participant age and is a potential candidate that should be further explored in AD biomarker discovery efforts.

Asymmetric Structures and Conformational Plasticity of the Uncleaved Full-Length Human Immunodeficiency Virus Envelope Glycoprotein Trimer

The functional human immunodeficiency virus (HIV-1) envelope glycoprotein (Env) trimer [(gp120/gp41)3] is produced by cleavage of a conformationally flexible gp160 precursor. gp160 cleavage or the binding of BMS-806, an entry inhibitor, stabilizes the pretriggered, "closed" (state 1) conformation recognized by rarely elicited broadly neutralizing antibodies. Poorly neutralizing antibodies (pNAbs) elicited at high titers during natural infection recognize more "open" Env conformations (states 2 and 3) induced by binding the receptor, CD4. We found that BMS-806 treatment and cross-linking decreased the exposure of pNAb epitopes on cell surface gp160; however, after detergent solubilization, cross-linked and BMS-806-treated gp160 sampled non-state-1 conformations that could be recognized by pNAbs. Cryo-electron microscopy of the purified BMS-806-bound gp160 revealed two hitherto unknown asymmetric trimer conformations, providing insights into the allosteric coupling between trimer opening and structural variation in the gp41 HR1N region. The individual protomer structures in the asymmetric gp160 trimers resemble those of other genetically modified or antibody-bound cleaved HIV-1 Env trimers, which have been suggested to assume state-2-like conformations. Asymmetry of the uncleaved Env potentially exposes surfaces of the trimer to pNAbs. To evaluate the effect of stabilizing a state-1-like conformation of the membrane Env precursor, we treated cells expressing wild-type HIV-1 Env with BMS-806. BMS-806 treatment decreased both gp160 cleavage and the addition of complex glycans, implying that gp160 conformational flexibility contributes to the efficiency of these processes. Selective pressure to maintain flexibility in the precursor of functional Env allows the uncleaved Env to sample asymmetric conformations that potentially skew host antibody responses toward pNAbs. IMPORTANCE The envelope glycoprotein (Env) trimers on the surface of human immunodeficiency virus (HIV-1) mediate the entry of the virus into host cells and serve as targets for neutralizing antibodies. The functional Env trimer is produced by cleavage of the gp160 precursor in the infected cell. We found that the HIV-1 Env precursor is highly plastic, allowing it to assume different asymmetric shapes. This conformational plasticity is potentially important for Env cleavage and proper modification by sugars. Having a flexible, asymmetric Env precursor that can misdirect host antibody responses without compromising virus infectivity would be an advantage for a persistent virus like HIV-1.

The opportunity cost of automated glycopeptide analysis: case study profiling the SARS-CoV-2 S glycoprotein

Glycosylation analysis of viral glycoproteins contributes significantly to vaccine design and development. Among other benefits, glycosylation analysis allows vaccine developers to assess the impact of construct design or producer cell line choices for vaccine production, and it is a key measure by which glycoproteins that are produced for use in vaccination can be compared to their native viral forms. Because many viral glycoproteins are multiply glycosylated, glycopeptide analysis is a preferrable approach for mapping the glycans, yet the analysis of glycopeptide data can be cumbersome and requires the expertise of an experienced analyst. In recent years, a commercial software product, Byonic, has been implemented in several instances to facilitate glycopeptide analysis on viral glycoproteins and other glycoproteomics data sets, and the purpose of the study herein is to determine the strengths and limitations of using this software, particularly in cases relevant to vaccine development. The glycopeptides from a recombinantly expressed trimeric S glycoprotein of the SARS-CoV-2 virus were first analyzed using an expert-based analysis strategy; subsequently, analysis of the same data set was completed using Byonic. Careful assessment of instances where the two methods produced different results revealed that the glycopeptide assignments from Byonic contained more false positives than true positives, even when the data were assessed using a 1% false discovery rate. The work herein provides a roadmap for removing the spurious assignments that Byonic generates, and it provides an assessment of the opportunity cost for relying on automated assignments for glycopeptide data sets from viral glycoproteins.

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier

Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several orders of magnitude in abundance. Machine learning tools that effectively leverage these data to accurately identify disease states are in high demand. While mass spectrometry data sets are rich with potentially useful information, using the data effectively can be challenging because of missing entries in the data sets and because the number of samples is typically much smaller than the number of features, two challenges that make machine learning difficult. To address this problem, we have modified a new supervised classification tool, the Aristotle Classifier, so that omics data sets can be better leveraged for identifying disease states. The optimized classifier, AC.2021, is benchmarked on multiple data sets against its predecessor and two leading supervised classification tools, Support Vector Machine (SVM) and XGBoost. The new classifier, AC.2021, outperformed existing tools on multiple tests using proteomics data. The underlying code for the classifier, provided herein, would be useful for researchers who desire improved classification accuracy when using their omics data sets to identify disease states.

Dataset of why inclusion matters for Alzheimer's disease biomarker discovery in plasma

Here we present a plasma proteomics dataset that was generated to understand the importance of self-reported race for biomarker discovery in Alzheimer's disease. This dataset is related to the article “Why inclusion matters for Alzheimer's disease biomarker discovery in plasma” [1]. Plasma samples were obtained from clinically diagnosed Alzheimer's disease and cognitively normal adults of African American/Black and non-Hispanic White racial and ethnic backgrounds. Plasma was immunodepleted, digested, and isobarically tagged with commercial reagents. Tagged peptides were fractionated using high pH fractionation and resulting fractions analysed by liquid chromatography – mass spectrometry (LC-MS/MS & MS³) analysis on an Orbitrap Fusion Lumos mass spectrometer. The resulting data was processed using Proteome Discoverer to produce a list of identified proteins with corresponding tandem mass tag (TMT) intensity information.

Analysis of glycosylation and disulfide bonding of wild-type SARS-CoV-2 spike glycoprotein

The SARS-CoV-2 coronavirus, the etiologic agent of COVID-19, uses its spike (S) glycoprotein anchored in the viral membrane to enter host cells. The S glycoprotein is the major target for neutralizing antibodies elicited by natural infection and by vaccines. Approximately 35% of the SARS-CoV-2 S glycoprotein consists of carbohydrate, which can influence virus infectivity and susceptibility to antibody inhibition. We found that virus-like particles produced by coexpression of SARS-CoV-2 S, M, E and N proteins contained spike glycoproteins that were extensively modified by complex carbohydrates. We used a fucose-selective lectin to enrich the Golgi-resident fraction of a wild-type SARS-CoV-2 S glycoprotein trimer, and determined its glycosylation and disulfide bond profile. Compared with soluble or solubilized S glycoproteins modified to prevent proteolytic cleavage and to retain a prefusion conformation, more of the wild-type S glycoprotein N-linked glycans are processed to complex forms. Even Asn 234, a significant percentage of which is decorated by high-mannose glycans on soluble and virion S trimers, is predominantly modified in the Golgi by processed glycans. Three incompletely occupied sites of O-linked glycosylation were detected. Viruses pseudotyped with natural variants of the serine/threonine residues implicated in O-linked glycosylation were generally infectious and exhibited sensitivity to neutralization by soluble ACE2 and convalescent antisera comparable to that of the wild-type virus. Unlike other natural cysteine variants, a Cys15Phe (C15F) mutant retained partial, but unstable, infectivity. These findings enhance our understanding of the Golgi processing of the native SARS-CoV-2 S glycoprotein carbohydrates and could assist the design of interventions.

The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data

One unifying challenge when classifying biological samples with mass spectrometry data is overcoming the obstacle of sample-to-sample variability so that differences between groups, such as between a healthy set and a disease set, can be identified. Similarly, when the same sample is re-analyzed under identical conditions, instrument signals can fluctuate by more than 10%. This signal inconsistency imposes difficulties in identifying subtle differences across a set of samples, and it weakens the mass spectrometrist’s ability to effectively leverage data in domains as diverse as proteomics, metabolomics, glycomics, and imaging. We selected challenging data sets in the fields of glycomics, mass spectrometry imaging, and bacterial typing to study the problem of within-group signal variability and adapted a 30 year old statistical approach to address the problem. The solution, “local-balanced model,” relies on using balanced subsets of training data to classify test samples. This analysis strategy was assessed on ESI-MS data of IgG-based glycopeptides and MALDI-MS imaging data of endogenous lipids, and MALDI-MS data of bacterial proteins. Two preliminary examples on non-mass spectrometry data sets are also included to show the potential generality of the method outside the field of MS analysis. We demonstrate that this approach is superior to simple normalization methods, generalizable to multiple mass spectrometry domains, and potentially appropriate in fields as diverse as physics and satellite imaging. In some cases, improvements in classification can be dramatic, with accuracy escalating from 60% with normalization alone to over 90% with the additional development described herein.

Clinically Viable Assay for Monitoring Uromodulin Glycosylation

Uromodulin, also known as the Tamm-Horsfall protein or THP, is the most abundant protein excreted in human urine. It is associated with the progression of kidney diseases; therefore, changes in the glycosylation profile of this protein could serve as a potential biomarker for kidney health. The typical glycomics analysis approaches used to quantify uromodulin glycosylation involve time-consuming and tedious glycoprotein isolation and labeling steps, which limit their utility in clinical glycomics assays, where sample throughput is important. Herein, we introduce a radically simplified sample preparation workflow, with direct ESI-MS analysis, enabling the quantification of N-linked glycans that originate from uromodulin. The method omits any glycan labeling steps but includes steps to reduce the salt content of the samples, thereby minimizing ion suppression. The method is effective for quantifying subtle glycosylation differences of uromodulin samples derived from different biological states. As a proof of concept, glycosylation from samples that differ by pregnancy status were shown to be differentiable.

Why Inclusion Matters for Alzheimer’s Disease Biomarker Discovery in Plasma

Background: African American/Black adults have a disproportionate incidence of Alzheimer’s disease (AD) and are underrepresented in biomarker discovery efforts. Objective: This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults. Methods: We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates. Results: In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86%for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47%for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD. Conclusion: These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers.

Structural insight into the novel iron-coordination and domain interactions of transferrin-1 from a model insect, Manduca sexta

Transferrins function in iron sequestration and iron transport by binding iron tightly and reversibly. Vertebrate transferrins coordinate iron through interactions with two tyrosines, an aspartate, a histidine, and a carbonate anion, and conformational changes that occur upon iron binding and release have been described. Much less is known about the structure and functions of insect transferrin-1 (Tsf1), which is present in hemolymph and influences iron homeostasis mostly by unknown mechanisms. Amino acid sequence and biochemical analyses have suggested that iron coordination by Tsf1 differs from that of the vertebrate transferrins. Here we report the first crystal structure (2.05 Å resolution) of an insect transferrin. Manduca sexta (MsTsf1) in the holo form exhibits a bilobal fold similar to that of vertebrate transferrins, but its carboxyl-lobe adopts a novel orientation and contacts with the amino-lobe. The structure revealed coordination of a single Fe3+ ion in the amino-lobe through Tyr90, Tyr204, and two carbonate anions. One carbonate anion is buried near the ferric ion and is coordinated by four residues, whereas the other carbonate anion is solvent exposed and coordinated by Asn121. Notably, these residues are highly conserved in Tsf1 orthologs. Docking analysis suggested that the solvent exposed carbonate position is capable of binding alternative anions. These findings provide a structural basis for understanding Tsf1 function in iron sequestration and transport in insects as well as insight into the similarities and differences in iron homeostasis between insects and humans.

How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab

As the field of mass spectrometry imaging continues to grow, so too do its needs for optimal methods of data analysis. One general need in image analysis is the ability to classify the underlying regions within an image, as healthy or diseased, for example. Classification, as a general problem, is often best accomplished by supervised machine learning strategies; unfortunately, conducting supervised machine learning on MS imaging files is not typically done by mass spectrometrists because a high degree of specialized knowledge is needed. To address this problem, we developed a fully open-source approach that facilitates supervised machine learning on MS imaging files, and we demonstrated its implementation on sets of cancer spheroids that either had or had not undergone chemotherapy treatment. These supervised machine learning studies demonstrated that metabolic changes induced by the monoclonal antibody, Cetuximab, are detectable but modest at 24 h, and by 72 h, the drug induces a larger and more diverse metabolic response.

So You Discovered a Potential Glycan-Based Biomarker; Now What? We Developed a High-Throughput Method for Quantitative Clinical Glycan Biomarker Validation

Glycomic-based approaches to discover potential biomarkers have shown great promise in their ability to distinguish between healthy and diseased individuals; these methods can identify when aberrant glycosylation is significant, but they cannot practically be adapted into widely implemented diagnostic assays because they are too complex, expensive, and low-throughput. We have developed a new strategy that addresses challenges associated with sample preparation, sample throughput, instrumentation needs, and data analysis to transfer the valuable knowledge provided by protein glycosylation into a clinical environment. Notably, the detection limits of the assay are in the single-digit picomole range. Proof of principle is demonstrated by quantifying the changes in the sialic acid content in fetuin. As the sialic acid content in proteins varies in a number of disease states, this example demonstrates the utility of the method for biomarker analysis. Furthermore, the developed method can be adapted to other biologically important saccharides, affording a broad array of quantitative glycomic analyses that are accessible in a high-throughput, plate-reader format. These studies enable glycomic-based biomarker discovery efforts to transition through the difficult landscape of developing a potential biomarker into a clinical assay.

HIV vaccine delayed boosting increases Env variable region 2-specific antibody effector functions

In the RV144 HIV-1 phase III trial, vaccine efficacy directly correlated with the magnitude of the variable region 2-specific (V2-specific) IgG antibody response, and in the presence of low plasma IgA levels, with the magnitude of plasma antibody-dependent cellular cytotoxicity. Reenrollment of RV144 vaccinees in the RV305 trial offered the opportunity to define the function, maturation, and persistence of vaccine-induced V2-specific and other mAb responses after boosting. We show that the RV144 vaccine regimen induced persistent V2 and other HIV-1 envelope-specific memory B cell clonal lineages that could be identified throughout the approximately 11-year vaccination period. Subsequent boosts increased somatic hypermutation, a critical requirement for antibody affinity maturation. Characterization of 22 vaccine-induced V2-specific mAbs with epitope specificities distinct from previously characterized RV144 V2-specific mAbs CH58 and CH59 found increased in vitro antibody-mediated effector functions. Thus, when inducing non-neutralizing antibodies, one method by which to improve HIV-1 vaccine efficacy may be through late boosting to diversify the V2-specific response to increase the breadth of antibody-mediated anti-HIV-1 effector functions.

Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS

MALDI-TOF MS has shown great utility for rapidly identifying microbial species. It can be used to successfully type bacteria and fungi from a variety of sources more rapidly and cost-effectively than traditional methods. One area where improvements are necessary is in the typing of highly similar samples, such as those samples from the same genus but different species or samples from within a single species but from different strains. One promising way to address this current limitation is by using advanced machine learning techniques. In this work, we adapt a newly developed machine learning tool, the Aristotle Classifier, to bacterial classification of MALDI-TOF MS data. This tool was originally developed for classifying glycomics and glycoproteomics data, so we modified it to be well-suited for assigning mass spectral data from bacterial proteins. The classifier exceeds existing benchmarks in classifying bacteria, and it shows particularly strong performance when the samples to be identified are highly similar. The combination of mass spectrometry data and tools like the Aristotle Classifier could ameliorate the ambiguities associated with challenging bacterial classification problems.

The Aristotle Classifier: Using the Whole Glycomic Profile To Indicate a Disease State

"The totality is not, as it were, a mere heap, but the whole is something besides the parts."-Aristotle. We built a classifier that uses the totality of the glycomic profile, not restricted to a few glycoforms, to differentiate samples from two different sources. This approach, which relies on using thousands of features, is a radical departure from current strategies, where most of the glycomic profile is ignored in favor of selecting a few features, or even a single feature, meant to capture the differences in sample types. The classifier can be used to differentiate the source of the material; applicable sources may be different species of animals, different protein production methods, or, most importantly, different biological states (disease vs healthy). The classifier can be used on glycomic data in any form, including derivatized monosaccharides, intact glycans, or glycopeptides. It takes advantage of the fact that changing the source material can cause a change in the glycomic profile in many subtle ways: some glycoforms can be upregulated, some downregulated, some may appear unchanged, yet their proportion-with respect to other forms present-can be altered to a detectable degree. By classifying samples using the entirety of their glycan abundances, along with the glycans' relative proportions to each other, the "Aristotle Classifier" is more effective at capturing the underlying trends than standard classification procedures used in glycomics, including PCA (principal components analysis). It also outperforms workflows where a single, representative glycomic-based biomarker is used to classify samples. We describe the Aristotle Classifier and provide several examples of its utility for biomarker studies and other classification problems using glycomic data from several sources.

Determinants of Tenascin-C and HIV-1 envelope binding and neutralization

Interactions between innate antiviral factors at mucosal surfaces and HIV-1 virions contribute to the natural inefficiency of HIV-1 transmission and are a platform to inform the development of vaccine and nonvaccine strategies to block mucosal HIV-1 transmission. Tenascin-C (TNC) is a large, hexameric extracellular matrix glycoprotein identified in breast milk and genital fluids that broadly neutralizes HIV-1 via interaction with the HIV-1 Envelope (Env) variable 3 (V3) loop. In this report, we characterize the specific determinants of the interaction between TNC and the HIV-1 Env. We observed that TNC binding and neutralization of HIV-1 is dependent on the TNC fibrinogen-like globe (fbg) and fibronectin-type III (fn) domains, oligomerization, and its newly-mapped glycan structure. Moreover, we observed that TNC-mediated neutralization is also dependent on Env V3 residues 321/322 and 326/327, which surround the IGDIR motif of the V3 loop, as well the N332 glycan, which is critical to the broadly neutralizing activity of glycan-dependent V3-specific antibodies such as PGT128. Our results demonstrate a striking parallel between innate and adaptive immune mechanisms of broad HIV neutralization and provide further insight into the host protein-virus interactions responsible for the natural inefficiency of mucosal HIV-1 transmission.

DecoyDeveloper: An On-Demand, De Novo Decoy Glycopeptide Generator

Glycopeptide analysis is a growing field that is struggling to adopt effective, automated tools. Many creative workflows and software apps have emerged recently that offer promising capabilities for assigning glycopeptides to MS data in an automated fashion. The effectiveness of these tools is best measured and improved by determining how often they would select a glycopeptide decoy as a spectral match, instead of its correct assignment; yet generating the appropriate number and type of glycopeptide decoys can be challenging. To address this need, we have designed DecoyDeveloper, an on-demand decoy glycopeptide generator that can produce a high volume of decoys with low mass differences. DecoyDeveloper has a simple user interface and is capable of producing large sets of decoys containing complete, biologically relevant glycan and peptide sequences. We demonstrate the tool's efficiency by applying it to a set of 80 glycopeptide targets. This tool is freely available and can be found at http://glycopro.chem.ku.edu/J1.php .

Stabilization of the V2 loop improves the presentation of V2 loop-associated broadly neutralizing antibody epitopes on HIV-1 envelope trimers

A successful HIV-1 vaccine will likely need to elicit broadly neutralizing antibodies (bNAbs) that target the envelope glycoprotein (Env) spike on the virus. Native-like recombinant Env trimers of the SOSIP design now serve as a platform for achieving this challenging goal. However, SOSIP trimers usually do not bind efficiently to the inferred germline precursors of bNAbs (gl-bNAbs). We hypothesized that the inherent flexibilities of the V1 and V2 variable loops in the Env trimer contribute to the poor recognition of gl-bNAb epitopes at the trimer apex that extensively involve V2 residues. To reduce local V2 flexibility and improve the binding of V2-dependent bNAbs and gl-bNAbs, we designed BG505 SOSIP.664 trimer variants containing newly created disulfide bonds intended to stabilize the V2 loop in an optimally antigenic configuration. The first variant, I184C/E190C, contained a new disulfide bond within the V2 loop, whereas the second variant, E153C/R178C, had a new disulfide bond that cross-linked V2 and V1. The resulting engineered native-like trimer variants were both more reactive with and were neutralized by V2 bNAbs and gl-bNAbs, a finding that may be valuable in the design of germline targeting and boosting trimer immunogens to create an antigenic conformation optimal for HIV vaccine development.

Interdomain Stabilization Impairs CD4 Binding and Improves Immunogenicity of the HIV-1 Envelope Trimer

The HIV-1 envelope (Env) spike is a trimer of gp120/gp41 heterodimers that mediates viral entry. Binding to CD4 on the host cell membrane is the first essential step for infection but disrupts the native antigenic state of Env, posing a key obstacle to vaccine development. We locked the HIV-1 Env trimer in a pre-fusion configuration, resulting in impaired CD4 binding and enhanced binding to broadly neutralizing antibodies. This design was achieved via structure-guided introduction of neo-disulfide bonds bridging the gp120 inner and outer domains and was successfully applied to soluble trimers and native gp160 from different HIV-1 clades. Crystallization illustrated the structural basis for CD4-binding impairment. Immunization of rabbits with locked trimers from two different clades elicited neutralizing antibodies against tier-2 viruses with a repaired glycan shield regardless of treatment with a functional CD4 mimic. Thus, interdomain stabilization provides a widely applicable template for the design of Env-based HIV-1 vaccines.

Method for Quantifying Oxidized Methionines and Application to HIV-1 Env

Recombinantly expressed proteins are susceptible to oxidation during expression, purification, storage, and analysis; the residue most susceptible to oxidation is methionine. Methionine oxidation can be overestimated using current quantitative analysis methods because oxidation can occur during sample preparation, and researchers often do not use methods that account for this possibility. An experimental strategy had been developed previously to solve this problem through the use of an ¹⁸O-labeled hydrogen peroxide reagent. However, the method did not address the analysis of peptides that contained multiple methionine residues. Herein, we develop and validate a new analysis method that uses theoretical isotope distributions and experimental spectra to quantify methionine oxidation that is present prior to sample preparation. The newly described approach is more rapid than the previously described method, and it needs only half the amount of protein for analysis. This method was validated using model proteins; then, it was applied to the analysis of recombinant HIV-1 Env, the key protein in HIV vaccine candidates. While Met oxidation of this protein could not be analyzed using previous methods, the approach described herein was useful for determining the oxidation state of HIV-Env. Graphical Abstract ᅟ.

Improving the Immunogenicity of Native-like HIV-1 Envelope Trimers by Hyperstabilization

The production of native-like recombinant versions of the HIV-1 envelope glycoprotein (Env) trimer requires overcoming the natural flexibility and instability of the complex. The engineered BG505 SOSIP.664 trimer mimics the structure and antigenicity of native Env. Here, we describe how the introduction of new disulfide bonds between the glycoprotein (gp)120 and gp41 subunits of SOSIP trimers of the BG505 and other genotypes improves their stability and antigenicity, reduces their conformational flexibility, and helps maintain them in the unliganded conformation. The resulting next-generation SOSIP.v5 trimers induce strong autologous tier-2 neutralizing antibody (NAb) responses in rabbits. In addition, the BG505 SOSIP.v6 trimers induced weak heterologous NAb responses against a subset of tier-2 viruses that were not elicited by the prototype BG505 SOSIP.664. These stabilization methods can be applied to trimers from multiple genotypes as components of multivalent vaccines aimed at inducing broadly NAbs (bNAbs).

Recent mass spectrometry-based techniques and considerations for disulfide bond characterization in proteins

Disulfide bonds are important structural moieties of proteins: they ensure proper folding, provide stability, and ensure proper function. With the increasing use of proteins for biotherapeutics, particularly monoclonal antibodies, which are highly disulfide bonded, it is now important to confirm the correct disulfide bond connectivity and to verify the presence, or absence, of disulfide bond variants in the protein therapeutics. These studies help to ensure safety and efficacy. Hence, disulfide bonds are among the critical quality attributes of proteins that have to be monitored closely during the development of biotherapeutics. However, disulfide bond analysis is challenging because of the complexity of the biomolecules. Mass spectrometry (MS) has been the go-to analytical tool for the characterization of such complex biomolecules, and several methods have been reported to meet the challenging task of mapping disulfide bonds in proteins. In this review, we describe the relevant, recent MS-based techniques and provide important considerations needed for efficient disulfide bond analysis in proteins. The review focuses on methods for proper sample preparation, fragmentation techniques for disulfide bond analysis, recent disulfide bond mapping methods based on the fragmentation techniques, and automated algorithms designed for rapid analysis of disulfide bonds from liquid chromatography-MS/MS data. Researchers involved in method development for protein characterization can use the information herein to facilitate development of new MS-based methods for protein disulfide bond analysis. In addition, individuals characterizing biotherapeutics, especially by disulfide bond mapping in antibodies, can use this review to choose the best strategies for disulfide bond assignment of their biologic products. Graphical Abstract This review, describing characterization methods for disulfide bonds in proteins, focuses on three critical components: sample preparation, mass spectrometry data, and software tools.

cGMP production and analysis of BG505 SOSIP.664, an extensively glycosylated, trimeric HIV-1 envelope glycoprotein vaccine candidate

We describe the properties of BG505 SOSIP.664 HIV‐1 envelope glycoprotein trimers produced under current Good Manufacturing Practice (cGMP) conditions. These proteins are the first of a new generation of native‐like trimers that are the basis for many structure‐guided immunogen development programs aimed at devising how to induce broadly neutralizing antibodies (bNAbs) to HIV‐1 by vaccination. The successful translation of this prototype demonstrates the feasibility of producing similar immunogens on an appropriate scale and of an acceptable quality for Phase I experimental medicine clinical trials. BG505 SOSIP.664 trimers are extensively glycosylated, contain numerous disulfide bonds and require proteolytic cleavage, all properties that pose a substantial challenge to cGMP production. Our strategy involved creating a stable CHO cell line that was adapted to serum‐free culture conditions to produce envelope glycoproteins. The trimers were then purified by chromatographic methods using a 2G12 bNAb affinity column and size‐exclusion chromatography. The chosen procedures allowed any adventitious viruses to be cleared from the final product to the required extent of >12 log₁₀. The final cGMP production run yielded 3.52 g (peptidic mass) of fully purified trimers (Drug Substance) from a 200 L bioreactor, a notable yield for such a complex glycoprotein. The purified trimers were fully native‐like as judged by negative‐stain electron microscopy, and were stable over a multi‐month period at room temperature or below and for at least 1 week at 50°C. Their antigenicity, disulfide bond patterns, and glycan composition were consistent with trimers produced on a research laboratory scale. The methods reported here should pave the way for the cGMP production of other native‐like Env glycoprotein trimers of various designs and genotypes.

Dissecting the Dissociation Patterns of Fucosylated Glycopeptides Undergoing CID: A Case Study in Improving Automated Glycopeptide Analysis Scoring Algorithms

The need to investigate the fragmentation of fucosylated glycopeptides is driven by recent work showing that at least one, and perhaps many, glycopeptide analysis scoring algorithms are less effective at identifying fucosylated glycopeptides than non-fucosylated glycopeptides. Herein, we study the CID fragmentation characteristics of fucosylated glycopeptides and the scoring rules of the glycopeptide analysis software, GlycoPep Grader, in an effort to improve automated assignments of these important glycopeptides. We identified some prominent product ions from a common fragmentation pathway of fucosylated glycopeptides that were not accounted for in the scoring rules. Based on this finding, we propose new scoring rules for fucosylated glycopeptides that can be incorporated into GlycoPep Grader and other similar analysis software tools to more accurately identify these species. The approach used here, to improve one particular scoring algorithm, could henceforth be used to improve any other algorithm that assigns glycopeptides based on their MS/MS data.

Vaccine Elicitation of High Mannose-Dependent Neutralizing Antibodies against the V3-Glycan Broadly Neutralizing Epitope in Nonhuman Primates

Induction of broadly neutralizing antibodies (bnAbs) that target HIV-1 envelope (Env) is a goal of HIV-1 vaccine development. A bnAb target is the Env third variable loop (V3)-glycan site. To determine whether immunization could induce antibodies to the V3-glycan bnAb binding site, we repetitively immunized macaques over a 4-year period with an Env expressing V3-high mannose glycans. Env immunizations elicited plasma antibodies that neutralized HIV-1 expressing only high-mannose glycans-a characteristic shared by early bnAb B cell lineage members. A rhesus recombinant monoclonal antibody from a vaccinated macaque bound to the V3-glycan site at the same amino acids as broadly neutralizing antibodies. A structure of the antibody bound to glycan revealed that the three variable heavy-chain complementarity-determining regions formed a cavity into which glycan could insert and neutralized multiple HIV-1 isolates with high-mannose glycans. Thus, HIV-1 Env vaccination induced mannose-dependent antibodies with characteristics of V3-glycan bnAb precursors.

Two New Tools for Glycopeptide Analysis Researchers: A Glycopeptide Decoy Generator and a Large Data Set of Assigned CID Spectra of Glycopeptides

The glycopeptide analysis field is tightly constrained by a lack of effective tools that translate mass spectrometry data into meaningful chemical information, and perhaps the most challenging aspect of building effective glycopeptide analysis software is designing an accurate scoring algorithm for MS/MS data. We provide the glycoproteomics community with two tools to address this challenge. The first tool, a curated set of 100 expert-assigned CID spectra of glycopeptides, contains a diverse set of spectra from a variety of glycan types; the second tool, Glycopeptide Decoy Generator, is a new software application that generates glycopeptide decoys de novo. We developed these tools so that emerging methods of assigning glycopeptides' CID spectra could be rigorously tested. Software developers or those interested in developing skills in expert (manual) analysis can use these tools to facilitate their work. We demonstrate the tools' utility in assessing the quality of one particular glycopeptide software package, GlycoPep Grader, which assigns glycopeptides to CID spectra. We first acquired the set of 100 expert assigned CID spectra; then, we used the Decoy Generator (described herein) to generate 20 decoys per target glycopeptide. The assigned spectra and decoys were used to test the accuracy of GlycoPep Grader's scoring algorithm; new strengths and weaknesses were identified in the algorithm using this approach. Both newly developed tools are freely available. The software can be downloaded at http://glycopro.chem.ku.edu/GPJ.jar.

HAMS: High-Affinity Mass Spectrometry Screening. A High-Throughput Screening Method for Identifying the Tightest-Binding Lead Compounds for Target Proteins with No False Positive Identifications

A major challenge in drug discovery is the identification of high affinity lead compounds that bind a particular target protein; these leads are typically identified by high throughput screens. Mass spectrometry has become a detection method of choice in drug screening assays because the target and the ligand need not be modified. Label-free assays are advantageous because they can be developed more rapidly than assays requiring labels, and they eliminate the risk of the label interfering with the binding event. However, in commonly used MS-based screening methods, detection of false positives is a major challenge. Here, we describe a detection strategy designed to eliminate false positives. In this approach, the protein and the ligands are incubated together, and the non-binders are separated for detection. Hits (protein binders) are not detectable by MS after incubation with the protein, but readily identifiable by MS when the target protein is not present in the incubation media. The assay was demonstrated using three different proteins and hundreds of non-inhibitors; no false positive hits were identified in any experiment. The assay can be tuned to select for ligands of a particular binding affinity by varying the quantity of protein used and the immobilization method. As examples, the method selectively detected inhibitors that have K_i values of 0.2 μM, 50 pM, and 700 pM. These findings demonstrate that the approach described here compares favorably with traditional MS-based screening methods. Graphical Abstract ᅟ.

GlycoPep MassList: software to generate massive inclusion lists for glycopeptide analyses

Protein glycosylation drives many biological processes and serves as markers for disease; therefore, the development of tools to study glycosylation is an essential and growing area of research. Mass spectrometry can be used to identify both the glycans of interest and the glycosylation sites to which those glycans are attached, when proteins are proteolytically digested and their glycopeptides are analyzed by a combination of high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) methods. One major challenge in these experiments is collecting the requisite MS/MS data. The digested glycopeptides are often present in complex mixtures and in low abundance, and the most commonly used approach to collect MS/MS data on these species is data-dependent acquisition (DDA), where only the most intense precursor ions trigger MS/MS. DDA results in limited glycopeptide coverage. Semi-targeted data acquisition is an alternative experimental approach that can alleviate this difficulty. However, due to the massive heterogeneity of glycopeptides, it is not obvious how to expediently generate inclusion lists for these types of analyses. To solve this problem, we developed the software tool GlycoPep MassList, which can be used to generate inclusion lists for liquid chromatography tandem-mass spectrometry (LC-MS/MS) experiments. The utility of the software was tested by conducting comparisons between semi-targeted and untargeted data-dependent analysis experiments on a variety of proteins, including IgG, a protein whose glycosylation must be characterized during its production as a biotherapeutic. When the GlycoPep MassList software was used to generate inclusion lists for LC-MS/MS experiments, more unique glycopeptides were selected for fragmentation. Generally, ∼30 % more unique glycopeptides can be analyzed per protein, in the simplest cases, with low background. In cases where background ions from proteins or other interferents are high, usage of an inclusion list is even more advantageous. The software is freely publically accessible. Graphical abstract Software increases the number of glycopeptides that get selected for MS/MS analysis.

Disulfide Bond Characterization of Endogenous IgG3 Monoclonal Antibodies Using LC-MS: An Investigation of IgG3 Disulfide-mediated Isoforms

The use of monoclonal antibodies (mAbs) for the manufacture of innovator and biosimilar biotherapeutics has increased tremendously in recent years. From a structural perspective, mAbs have high disulfide bond content, and the correct disulfide connectivity is required for proper folding and to maintain their biological activity. Therefore, disulfide linkage mapping is an important component of mAB characterization for ensuring drug safety and efficacy. The native disulfide linkage patterns of all four subclasses of IgG antibodies have been well established since the late 1960s. Among these IgG subtypes, disulfide mediated isoforms have been identified for IgG2 and IgG4, and to a lesser extent in IgG1, which is the most studied IgG subclass. However, no studies have been carried out so far to investigate whether different IgG3 isoforms exist due to alternative disulfide connectivity. In an effort to investigate the presence of disulfide-mediated isoforms in IgG3, we employed a bottom-up mass spectrometry approach to accurately determine the disulfide bond linkages in endogenous human IgG3 monoclonal antibody and our results show that no such alternative disulfide bonds exist. While many antibody-based drugs are developed around IgG1, IgG3 represents a new, and in some cases, more desirable drug candidate. Our data represent the first demonstration that alternative disulfide bond arrangements are not present in endogenous IgG3; and therefore, they should not be present in recombinant forms used as antibody-based therapeutics.

Generation and Characterization of a Bivalent HIV-1 Subtype C gp120 Protein Boost for Proof-of-Concept HIV Vaccine Efficacy Trials in Southern Africa

The viral envelope glycoprotein (Env) is the major target for antibody (Ab)-mediated vaccine development against the Human Immunodeficiency Virus type 1 (HIV-1). Although several recombinant Env antigens have been evaluated in clinical trials, only the surface glycoprotein, gp120, (from HIV-1 subtype B, MN, and subtype CRF_01AE, A244) used in the ALVAC prime-AIDSVAX gp120 boost RV144 Phase III HIV vaccine trial was shown to contribute to protective efficacy, although modest and short-lived. Hence, for clinical trials in southern Africa, a bivalent protein boost of HIV-1 subtype C gp120 antigens composed of two complementary gp120s, from the TV1.C (chronic) and 1086.C (transmitted founder) HIV-1 strains, was selected. Stable Chinese Hamster Cell (CHO) cell lines expressing these gp120s were generated, scalable purification methods were developed, and a detailed analytical analysis of the purified proteins was conducted that showed differences and complementarity in the antigenicity, glycan occupancy, and glycan content of the two gp120 molecules. Moreover, mass spectrometry revealed some disulfide heterogeneity in the expressed proteins, particularly in V1V2-C1 region and most prominently in the TV1 gp120 dimers. These dimers not only lacked binding to certain key CD4 binding site (CD4bs) and V1V2 epitope-directed ligands but also elicited reduced Ab responses directed to those epitopes, in contrast to monomeric gp120, following immunization of rabbits. Both monomeric and dimeric gp120s elicited similarly high titer Tier 1 neutralizing Abs as measured in standard virus neutralization assays. These results provide support for clinical evaluations of bivalent preparations of purified monomeric TV1.C and 1086.C gp120 proteins.

Carbohydrates on Proteins: Site-Specific Glycosylation Analysis by Mass Spectrometry

Glycosylation on proteins adds complexity and versatility to these biologically vital macromolecules. To unveil the structure-function relationship of glycoproteins, glycopeptide-centric analysis using mass spectrometry (MS) has become a method of choice because the glycan is preserved on the glycosylation site and site-specific glycosylation profiles of proteins can be readily determined. However, glycopeptide analysis is still challenging given that glycopeptides are usually low in abundance and relatively difficult to detect and the resulting data require expertise to analyze. Viewing the urgent need to address these challenges, emerging methods and techniques are being developed with the goal of analyzing glycopeptides in a sensitive, comprehensive, and high-throughput manner. In this review, we discuss recent advances in glycoprotein and glycopeptide analysis, with topics covering sample preparation, analytical separation, MS and tandem MS techniques, as well as data interpretation and automation.

New glycoproteomics software, GlycoPep Evaluator, generates decoy glycopeptides de novo and enables accurate false discovery rate analysis for small data sets

Glycoproteins are biologically significant large molecules that participate in numerous cellular activities. In order to obtain site-specific protein glycosylation information, intact glycopeptides, with the glycan attached to the peptide sequence, are characterized by tandem mass spectrometry (MS/MS) methods such as collision-induced dissociation (CID) and electron transfer dissociation (ETD). While several emerging automated tools are developed, no consensus is present in the field about the best way to determine the reliability of the tools and/or provide the false discovery rate (FDR). A common approach to calculate FDRs for glycopeptide analysis, adopted from the target-decoy strategy in proteomics, employs a decoy database that is created based on the target protein sequence database. Nonetheless, this approach is not optimal in measuring the confidence of N-linked glycopeptide matches, because the glycopeptide data set is considerably smaller compared to that of peptides, and the requirement of a consensus sequence for N-glycosylation further limits the number of possible decoy glycopeptides tested in a database search. To address the need to accurately determine FDRs for automated glycopeptide assignments, we developed GlycoPep Evaluator (GPE), a tool that helps to measure FDRs in identifying glycopeptides without using a decoy database. GPE generates decoy glycopeptides de novo for every target glycopeptide, in a 1:20 target-to-decoy ratio. The decoys, along with target glycopeptides, are scored against the ETD data, from which FDRs can be calculated accurately based on the number of decoy matches and the ratio of the number of targets to decoys, for small data sets. GPE is freely accessible for download and can work with any search engine that interprets ETD data of N-linked glycopeptides. The software is provided at https://desairegroup.ku.edu/research.

Glycosylation and disulfide bond analysis of transiently and stably expressed clade C HIV-1 gp140 trimers in 293T cells identifies disulfide heterogeneity present in both proteins and differences in O-linked glycosylation

The HIV-1 envelope protein (Env) mediates viral entry into host cells to initiate infection and is the sole target of antibody-based vaccine development. Significant efforts have been made toward the design, engineering, and expression of various soluble forms of HIV Env immunogen, yet a highly effective immunogen remains elusive. One of the key challenges in the development of an effective HIV vaccine is the presence of the complex set of post-translational modifications (PTMs) on Env, namely, glycosylation and disulfide bonds, that affect protein folding, epitope accessibility, and immunogenecity. Although these PTMs vary with expression systems, variations in Env's PTMs due to changes in the expression method are not yet well established. In this study, we compared the disulfide bond network and glycosylation profiles of clade C recombinant HIV-1 Env trimers, C97ZA012 gp140, expressed by stable and transient transfections using an integrated mass mapping workflow that combines collision induced dissociation (CID) and electron transfer dissociation (ETD). Site-specific analysis of the N- and O-glycosylation profiles revealed that C97ZA012 gp140 produced by both transfection methods displayed a high degree of similarity in N-glycosylation profiles and site occupancy except for one site. By contrast, different O-glycosylation profiles were detected. Analysis of the disulfide bond networks of the Env revealed that both transfection methods yielded C97ZA012 gp140 adopting the expected disulfide bond pattern identified for the monomeric gp120 and gp41 as well as alternative disulfide bond patterns in the C1, V1/V2, and C2 regions. The finding that disulfide bonding is consistently heterogeneous in these proteins is perhaps the most significant outcome of these studies; this disulfide heterogeneity has been reported for multiple other recombinant gp140s, and it is likely present in most recombinantly expressed Env immunogens.

Absolute quantitation of glycosylation site occupancy using isotopically labeled standards and LC-MS

N-linked glycans are required to maintain appropriate biological functions on proteins. Underglycosylation leads to many diseases in plants and animals; therefore, characterizing the extent of glycosylation on proteins is an important step in understanding, diagnosing, and treating diseases. To determine the glycosylation site occupancy, protein N-glycosidase F (PNGase F) is typically used to detach the glycan from the protein, during which the formerly glycosylated asparagine undergoes deamidation to become an aspartic acid. By comparing the abundance of the resulting peptide containing aspartic acid against the one containing non-glycosylated asparagine, the glycosylation site occupancy can be evaluated. However, this approach can give inaccurate results when spontaneous chemical deamidation of the non-glycosylated asparagine occurs. To overcome this limitation, we developed a new method to measure the glycosylation site occupancy that does not rely on converting glycosylated peptides to their deglycosylated forms. Specifically, the overall protein concentration and the non-glycosylated portion of the protein are quantified simultaneously by using heavy isotope-labeled internal standards coupled with LC-MS analysis, and the extent of site occupancy is accurately determined. The efficacy of the method was demonstrated by quantifying the occupancy of a glycosylation site on bovine fetuin. The developed method is the first work that measures the glycosylation site occupancy without using PNGase F, and it can be done in parallel with glycopeptide analysis because the glycan remains intact throughout the workflow.

Software for automated interpretation of mass spectrometry data from glycans and glycopeptides

The purpose of this review is to provide those interested in glycosylation analysis with the most updated information on the availability of automated tools for MS characterization of N-linked and O-linked glycosylation types. Specifically, this review describes software tools that facilitate elucidation of glycosylation from MS data on the basis of mass alone, as well as software designed to speed the interpretation of glycan and glycopeptide fragmentation from MS/MS data. This review focuses equally on software designed to interpret the composition of released glycans and on tools to characterize N-linked and O-linked glycopeptides. Several websites have been compiled and described that will be helpful to the reader who is interested in further exploring the described tools.

Interlaboratory study on differential analysis of protein glycosylation by mass spectrometry: the ABRF glycoprotein research multi-institutional study 2012

One of the principal goals of glycoprotein research is to correlate glycan structure and function. Such correlation is necessary in order for one to understand the mechanisms whereby glycoprotein structure elaborates the functions of myriad proteins. The accurate comparison of glycoforms and quantification of glycosites are essential steps in this direction. Mass spectrometry has emerged as a powerful analytical technique in the field of glycoprotein characterization. Its sensitivity, high dynamic range, and mass accuracy provide both quantitative and sequence/structural information. As part of the 2012 ABRF Glycoprotein Research Group study, we explored the use of mass spectrometry and ancillary methodologies to characterize the glycoforms of two sources of human prostate specific antigen (PSA). PSA is used as a tumor marker for prostate cancer, with increasing blood levels used to distinguish between normal and cancer states. The glycans on PSA are believed to be biantennary N-linked, and it has been observed that prostate cancer tissues and cell lines contain more antennae than their benign counterparts. Thus, the ability to quantify differences in glycosylation associated with cancer has the potential to positively impact the use of PSA as a biomarker. We studied standard peptide-based proteomics/glycomics methodologies, including LC-MS/MS for peptide/glycopeptide sequencing and label-free approaches for differential quantification. We performed an interlaboratory study to determine the ability of different laboratories to correctly characterize the differences between glycoforms from two different sources using mass spectrometry methods. We used clustering analysis and ancillary statistical data treatment on the data sets submitted by participating laboratories to obtain a consensus of the glycoforms and abundances. The results demonstrate the relative strengths and weaknesses of top-down glycoproteomics, bottom-up glycoproteomics, and glycomics methods.

Characterizing O-linked glycopeptides by electron transfer dissociation: fragmentation rules and applications in data analysis

Studying protein O-glycosylation remains an analytical challenge. Different from N-linked glycans, the O-glycosylation site is not within a known consensus sequence. Additionally, O-glycans are heterogeneous with numerous potential modification sites. Electron transfer dissociation (ETD) is the method of choice in analyzing these glycopeptides since the glycan side chain remains intact in ETD, and the glycosylation site can be localized on the basis of the c and z fragment ions. Nonetheless, new software is necessary for interpreting O-glycopeptide ETD spectra in order to expedite the analysis workflow. To address the urgent need, we studied the fragmentation of O-glycopeptides in ETD and found useful rules that facilitate their identification. By implementing the rules into an algorithm to score potential assignments against ETD-MS/MS data, we applied the method to glycopeptides generated from various O-glycosylated proteins including mucin, erythropoietin, fetuin, and an HIV envelope protein, 1086.C gp120. The site-specific O-glycopeptide composition was correctly assigned in every case, proving the merits of our method in analyzing glycopeptide ETD data. The algorithm described herein can be easily incorporated into other automated glycomics tools.

In-solution digestion of glycoproteins for glycopeptide-based mass analysis

Glycopeptides are generated from the enzymatic digestion of glycoproteins with a specific or nonspecific protease. Whether this enzymatic conversion of glycoproteins into glycopeptides and peptides is done in-solution or in-gel, an efficient digestion protocol is one of the key components of a successful outcome in a mass spectrometry-based experimental workflow. This chapter outlines an optimized in-solution digestion protocol to prepare samples for glycopeptide-based mass analysis.

GlycoPep Detector: a tool for assigning mass spectrometry data of N-linked glycopeptides on the basis of their electron transfer dissociation spectra

Electron transfer dissociation (ETD) is commonly used in fragmenting N-linked glycopeptides in their mass spectral analyses to complement collision-induced dissociation (CID) experiments. The glycan remains intact through ETD, while the peptide backbone is cleaved, providing the sequence of amino acids for a glycopeptide. Nonetheless, data analysis is a major bottleneck to high-throughput glycopeptide identification based on ETD data, due to the complexity and diversity of ETD mass spectra compared to CID counterparts. GlycoPep Detector (GPD) is a web-based tool to address this challenge. It filters out noise peaks that interfere with glycopeptide sequencing, correlates input glycopeptide compositions with the ETD spectra, and assigns a score for each candidate. By considering multiple ion series (c-, z-, and y-ions) and scoring them separately, the software gives more weighting to the ion series that matches peaks of high intensity in the spectra. This feature enables the correct glycopeptide to receive a high score while keeping scores of incorrect compositions low. GPD has been utilized to interpret data collected on six model glycoproteins (RNase B, avidin, fetuin, asialofetuin, transferrin, and AGP) as well as a clade C HIV envelope glycoprotein, C.97ZA012 gp140ΔCFI. In every assignment made by GPD, the correct glycopeptide composition earns a score that is about 2-fold higher than other incorrect glycopeptide candidates (decoys). The software can be accessed at http://glycopro.chem.ku.edu/ZZKHome.php .

Characterization of host-cell line specific glycosylation profiles of early transmitted/founder HIV-1 gp120 envelope proteins

Glycosylation plays an essential role in regulating protein function by modulating biological, structural, and therapeutic properties. However, due to its inherent heterogeneity and diversity, the comprehensive analysis of protein glycosylation remains a challenge. As part of our continuing effort in the analysis of glycosylation profiles of recombinant HIV-1 envelope-based immunogens, we evaluated and compared the host-cell specific glycosylation pattern of recombinant HIV-1 surface glycoprotein, gp120, derived from clade C transmitted/founder virus 1086.C expressed in Chinese hamster ovary (CHO) and human embryonic kidney containing T antigen (293T) cell lines. We used an integrated glycopeptide-based mass mapping workflow that includes a partial deglycosylation step described in our previous study with the inclusion of a fragmentation technique, electron transfer dissociation (ETD), to complement collision-induced dissociation. The inclusion of ETD facilitated the analysis by providing additional validation for glycopeptide identification and expanding the identified glycopeptides to include coverage of O-linked glycosylation. The site-specific glycosylation analysis shows that the transmitted/founder 1086.C gp120 expressed in CHO and 293T displayed distinct similarities and differences. For N-linked glycosylation, two sites (N386 and N392) in the V4 region were populated with high mannose glycans in the CHO cell-derived 1086.C gp120, while these sites had a mixture of high mannose and processed glycans in the 293T cell-derived 1086.C gp120. Compositional analysis of O-linked glycans revealed that 293T cell-derived 1086.C gp120 consisted of core 1, 2, and 4 type O-linked glycans, while CHO cell-derived 1086.C exclusively consisted of core 1 type O-linked glycans. Overall, glycosylation site occupancy of the CHO and 293T cell-derived 1086.C gp120 showed a high degree of similarity except for one site at N88 in the C1 region. This site was partially occupied in 293T-gp120 but fully occupied in CHO-gp120. Site-specific glycopeptide analysis of transmitted/founder 1086.C gp120 expressed in CHO cells revealed the presence of phosphorylated glycans, while 293T cell-produced 1086.C gp120 glycans were not phosphorylated. While the influence of phosphorylated glycans on immunogenicity is unclear, distinguishing host-cell specific variations in glycosylation profiles provide insights into the similarity (or difference) in recombinant vaccine products. While these differences had minimal effect on envelope antigenicity, they may be important in considering immunogenicity and functional capacities of recombinant envelope proteins produced in different expression systems.

Glycopeptide analysis, recent developments and applications

Glycopeptide-based analysis is used to inform researchers about the glycans on one or more proteins. The method's key attractive feature is its ability to link glycosylation information to exact locations (glycosylation sites) on proteins. Numerous applications for glycopeptide analysis are known, and several examples are described herein. The techniques used to characterize glycopeptides are still emerging, and recently, research focused on facilitating aspects of glycopeptide analysis has advanced significantly in the areas of sample preparation, MS fragmentation, and automation of data analysis. These recent developments, described herein, provide the foundation for the growth of glycopeptide analysis as a blossoming field.