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The use of multi-attribute method (MAM) for identity and purity testing of biopharmaceuticals offers the ability to complement and replace multiple conventional analytical technologies with a single mass spectrometry (MS) method. Phase-appropriate method validation is one major consideration for the implementation of MAM in a current Good Manufacturing Practice (cGMP) environment. We developed a MAM workflow for therapeutic monoclonal antibodies (mAbs) with optimized sample preparation using lysyl endopeptidase (Lys-C) digestion. In this study, we evaluated the assay performances of this platform MAM workflow for identity, product quality attributes (PQAs) monitoring and new peak detection (NPD) for single and coformulated mAbs. An IgG4 mAb-1 and its coformulations were used as model molecules in this study. The assay performance evaluation demonstrated the full potential of the platform MAM approach for its intended use for characterization and quality control of single mAb-1 and mAb-1 in its coformulations. To the best of our knowledge, this is the first performance evaluation of MAM for mAb identity, PQA monitoring, and new peak detection (NPD) in a single assay, featuring 1) the first performance evaluation of MAM for PQA monitoring using Lys-C digestion with a high-resolution MS, 2) a new approach for mAb identity testing capable of distinguishing single mAb from coformulations using MAM, and 3) the performance evaluation of NPD for MAM with Lys-C digestion. The developed platform MAM workflow and the MAM performance evaluation paved the way for its GMP qualification and enabled clinical release of mAb-1 in GMP environment with MAM.

The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment.

The commercially available Polysorbate 80 (PS-80) is a highly heterogeneous product. It is a complex and structurally diverse mixture consisting of polymeric species containing polyoxyethylenes (POEs), fatty acid esters, with/or without a carbohydrate core. The core is primarily sorbitan, with some isosorbide and sorbitol. Depending on the sources of fatty acids and the degrees of esterification, multiple combinations of fatty acid esters are commonly observed. A number of POE intermediates, such as polyoxyethylene glycols, POE-sorbitans, POE-isosorbides, and an array of fatty acid esters from these intermediates remain in the raw material as well. The complex composition of PS-80 is difficult to control and poses a significant characterization challenge for its use in the pharmaceutical industry. Here, we present a novel solution for PS-80 characterization using ultra high-performance liquid chromatography coupled with charge-reduction high resolution mass spectrometry. Post column co-infusion of triethylamine focused the signal into mainly singly charged molecular ions and reduced the extent of in-source fragmentation, resulting in a simpler ion map and enhanced measurement of PS-80 species. The data processing workflow is designed to programmatically identify PS-80 component classes and reduce the burden of manually analyzing complex MS data. The 2-dimensional graphical representation of the data helps visualize these features. Together, these innovative methodologies enabled us to analyze components in PS-80 with unprecedented detail and shall be a useful tool to study formulation and stability of pharmaceutical preparations. The power of this approach was demonstrated by comparing the composition of PS-80 obtained from different vendors.

Supplementary Figure S1 - Study schema

Sipuleucel-T is an autologous cellular therapy for asymptomatic, or minimally symptomatic, metastatic castrate-resistant prostate cancer, designed to stimulate an immune response against prostate cancer. In a recent clinical trial (NCT00715104), we found that neoadjuvant sipuleucel-T increased the number of activated T cells within the tumor microenvironment. The current analysis examined whether sipuleucel-T altered adaptive T-cell responses by expanding pre-existing T cells or by recruiting new T cells to prostate tissue. Next-generation sequencing of the T-cell receptor (TCR) genes from blood or prostate tissue was used to quantitate and track T-cell clonotypes in these treated subjects with prostate cancer. At baseline, there was a significantly greater diversity of circulating TCR sequences in subjects with prostate cancer compared with healthy donors. Among healthy donors, circulating TCR sequence diversity remained unchanged over the same time interval. In contrast, sipuleucel-T treatment reduced circulating TCR sequence diversity versus baseline as measured by the Shannon index. Interestingly, sipuleucel-T treatment resulted in greater TCR sequence diversity in resected prostate tissue in sipuleucel-T–treated subjects versus tissue of nonsipuleucel-T–treated subjects with prostate cancer. Furthermore, sipuleucel-T increased TCR sequence commonality between blood and resected prostate tissue in treated versus untreated subjects with prostate cancer. The broadening of the TCR repertoire within the prostate tissue supports the hypothesis that sipuleucel-T treatment facilitates the recruitment of T cells into the prostate. Our results highlight the importance of assessing T-cell response to immunotherapy both in the periphery and in tumor tissue. Cancer Res; 76(13); 3711–8. ©2016 AACR.

The Multi-Attribute Method (MAM) Consortium was initially formed as a venue to harmonize best practices, share experiences, and generate innovative methodologies to facilitate widespread integration of the MAM platform, which is an emerging ultra-high-performance liquid chromatography-mass spectrometry application. Successful implementation of MAM as a purity-indicating assay requires new peak detection (NPD) of potential process- and/or product-related impurities. The NPD interlaboratory study described herein was carried out by the MAM Consortium to report on the industry-wide performance of NPD using predigested samples of the NISTmAb Reference Material 8671. Results from 28 participating laboratories show that the NPD parameters being utilized across the industry are representative of high-resolution MS performance capabilities. Certain elements of NPD, including common sources of variability in the number of new peaks detected, that are critical to the performance of the purity function of MAM were identified in this study and are reported here as a means to further refine the methodology and accelerate adoption into manufacturer-specific protein therapeutic product life cycles.

The use of Multi-attribute method (MAM) for identity and purity testing of biopharmaceuticals offers the ability to complement and replace multiple conventional analytical technologies with a single mass spectrometry (MS) method. Method qualification and phase-appropriate validation is one major consideration for the implementation of MAM in a current Good Manufacturing Practice (cGMP) environment. We developed an improved MAM workflow with optimized sample preparation using Lys-C digestion for therapeutic monoclonal antibodies. In this study, we qualified the enhanced MAM workflow for mAb-1 identity, product quality attributes (PQAs) monitoring and new peak detection (NPD). The qualification results demonstrated the full potential of the MAM for its intended use in mAb-1 characterization and quality control in regulated labs. To the best of our knowledge, this is the first report of MAM qualification for mAb identity, PQA monitoring, and new peak detection (NPD) in a single assay, featuring 1) the first full qualification of MAM using Lys-C digestion without desalting using a high-resolution MS, 2) a new approach for mAb identity testing using MAM, and 3) the first qualification of NPD for MAM. The developed MAM workflow and the approaches for MAM qualification may serve as a reference for other labs in the industry.

The mass spectrometry based multi-attribute method (MAM) has gained popularity in the field of biopharmaceutical analysis as it promises a single method for comprehensive monitoring of multiple product quality attributes (PQAs) and product purity. Sample preparation for protein digestion and peptide separation are critical considerations for a reduced peptide mapping-based MAM. To avoid desalting steps required in tryptic protein digestion and in order to improve peptide separation for hydrophilic peptides, we developed an improved robust sample preparation using Lys-C protease for high-throughput MAM testing. Additionally, this method optimizes the peptide retention and separation of a stability-indicating VSNK peptide using a HSS T3 column for comprehensive PQA monitoring. A fully automated sample preparation had similar assay variations for PQAs monitoring compared to manual sample preparation. To the best of our knowledge, this is the first report of a high-resolution MS-based MAM using Lys-C digestion with enhanced PQA monitoring for hydrophilic peptides. The improved, robust MAM workflow for protein digestion and peptide separation will pave the way for broader MAM qualification and its applications for the characterization and quality control of therapeutic monoclonal antibodies.

Host cell proteins (HCPs) are process‐related impurities derived from host organisms, which need to be controlled to ensure adequate product quality and safety. In this study, product quality attributes were tracked for several monoclonal antibodies (mAbs) under the intended storage and accelerated stability conditions. One product quality attribute not expected to be stability indicating is the N‐glycan heterogeneity profile. However, significant N‐glycan degradation was observed for one mAb under accelerated and stressed stability conditions. The root cause for this instability was attributed to hexosaminidase B (HEXB), an enzyme known to remove terminal N‐acetylglucosamine (GlcNAc). HEXB was identified by liquid chromatography–mass spectrometry (LC–MS)‐based proteomics approach to be enriched in the impacted stability batches from mAb‐1. Subsequently, enzymatic and targeted multiple reaction monitoring (MRM) MS assays were developed to support process and product characterization. A potential interaction between HEXB and mAb‐1 was initially observed from the analysis of process intermediates by proteomics among several mAbs and later supported by computational modeling. An improved bioprocess was developed to significantly reduce HEXB levels in the final drug substance. A risk assessment was conducted by evaluating the in silico immunogenicity risk and the impact on product quality. To the best of our knowledge, HEXB is the first residual HCP reported to have impact on the glycan profile of a formulated drug product. The combination of different analytical tools, mass spectrometry, and computational modeling provides a general strategy on how to study residual HCP for biotherapeutics development.

The mass spectrometry based multi-attribute method (MAM) has gained popularity in the field of biopharmaceutical analysis as it promises a single method for comprehensive monitoring of multiple product quality attributes (PQAs) and product purity. Sample preparation for protein digestion and peptide separation are critical considerations for a reduced peptide mapping-based MAM. To avoid desalting steps required in most tryptic protein digestion and in order to improve peptide separation for hydrophilic peptides, we developed an improved robust sample preparation using lysyl endopeptidase (Lys-C) for high-throughput MAM testing. Additionally, this method optimizes the peptide retention and separation of a stability-indicating VSNK peptide using a HSS T3 column for comprehensive PQA monitoring. A fully automated sample preparation had similar assay variations for PQAs monitoring compared to manual sample preparation. To the best of our knowledge, this is the first report of a high-resolution MS-based MAM using a streamlined Lys-C digestion without desalting with enhanced PQA monitoring for hydrophilic peptides. The improved, robust MAM workflow for protein digestion and peptide separation will pave the way for broader MAM qualification and its applications for the characterization and quality control of therapeutic monoclonal antibodies.

Polysorbate is widely used to maintain stability of biotherapeutic proteins in formulation development. Degradation of polysorbate can lead to particle formation in drug products, which is a major quality concern and potential patient risk factor. Enzymatic activity from residual enzymes such as lipases and esterases can cause polysorbate degradation. Their high activity, even at low concentration, constitutes a major analytical challenge. In this study, we evaluated and optimized the activity-based protein profiling (ABPP) approach to identify active enzymes responsible for polysorbate degradation. Using chemical probes to enrich active serine hydrolases, more than 80 proteins were identified in harvested cell culture fluid (HCCF) from monoclonal antibodies (mAb) production. A total of 8 known lipases were identified by ABPP, while only 5 lipases were identified by a traditional abundance-based proteomics (TABP) approach. Interestingly, phospholipase B-like 2 (PLBL2), a well-known problematic HCP was not found to be active in process-intermediates from two different mAbs. In a proof-of-concept study, phospholipase A2 group VII (PLA2G7) and sialic acid acetylesterase (SIAE) were identified by ABPP as possible root causes of polysorbate-80 degradation. The established ABBP approach can fill the gap between lipase abundance and activity, which enables more meaningful polysorbate degradation investigations for biotherapeutic development.

Host cell proteins (HCPs) are process-related impurities derived from host organisms, which need to be controlled to ensure adequate product quality and safety. In this study, product quality attributes were tracked for several mAbs under the intended storage and accelerated stability conditions. One product quality attribute not expected to be stability indicating is the N-glycan heterogeneity profile. However, significant N-glycan degradation was observed for one mAbs under accelerated and stressed stability conditions. The root cause for this instability was attributed to Hexosaminidase B (HEXB), an enzyme known to remove terminal N-acetylglucosamine (GlcNAc). HEXB was identified by liquid chromatography–mass spectrometry (LC-MS) based proteomics approach to be enriched in the impacted stability batches from mAb-1. Subsequently, enzymatic and targeted multiple reaction monitoring (MRM) MS assays were developed to support process and product characterization. A potential interaction between HEXB and mAb-1 was initially observed from the analysis of process intermediates by proteomics among several mAbs and later supported by computational modeling. An improved bioprocess was developed to significantly reduce HEXB levels in the final drug substance. A risk assessment was conducted by evaluating the in silico immunogenicity risk and the impact on product quality. To the best of our knowledge, HEXB is the first residual HCP reported to have impact on the glycan profile of a formulated drug product. The combination of different analytical tools, mass spectrometry and computational modeling provide a general strategy on how to study residual HCP for biotherapeutics development.

[Image: see text] The robustness of good laboratory practice and clinical data is reliant upon a clear understanding of the bioanalytical assays. One of the most important components of ligand-binding based assays is critical reagents used to directly or indirectly measure biologic markers or signals. High quality, reproducible, sustainable critical reagents through the development lifecycle could avoid unnecessary rework, multiple validations, cross-validations, and ensure consistency of the data. Numerous analytical methods (UPLC-size exclusion chromatography, cation exchange chromatography, biacore/octet, and high-resolution mass spectrometry) have been evaluated by using current critical reagents. A comprehensive analytical toolbox of biochemical and biophysical methods has been employed to evaluate the quality of critical reagents and explore potential issues if there are any. Moving forward, this “tiered approach” of critical reagents characterization will be used not only to establish critical quality attributes for new reagents but also to evaluate stability in support of reagents recertification.

Immunotherapies are coming to the forefront as a treatment paradigm in cancer with multiple US FDA approvals in recent years and a better understanding of their therapeutic mode of action. The control of tumor growth by the immune system is orchestrated by a complex array of cellular interactions and molecular pathways, both in the immune cells as well as the tumor. Although research over the past three decades has elucidated many aspects of tumor immunosurveillance, given the inherent complexity of the immune cell phenotypes and function, high-throughput molecular profiling ('omics') approaches have now become essential to support the discovery and development of new therapies. Technologies, such as DNA and protein microarrays, deep sequencing, mass spectrometry, as well as the computational methods for their analyses, are advancing the contributions of systems biology towards the development and mechanistic understanding of cancer immunotherapies. In this review, the authors illustrate this through some recently reported studies.

C.1

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300–1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

IntroductionA proof-of-concept demonstration of the use of label-free quantitative glycoproteomics for biomarker discovery workflow is presented in this paper, using a mouse model for skin cancer as an example.Materials and MethodsBlood plasma was collected from ten control mice and ten mice having a mutation in the p19ARFgene, conferring them high propensity to develop skin cancer after carcinogen exposure. We enriched for N-glycosylated plasma proteins, ultimately generating deglycosylated forms of the tryptic peptides for liquid chromatography mass spectrometry (LC-MS) analyses. LC-MS runs for each sample were then performed with a view to identifying proteins that were differentially abundant between the two mouse populations. We then used a recently developed computational framework, Corra, to perform peak picking and alignment, and to compute the statistical significance of any observed changes in individual peptide abundances. Once determined, the most discriminating peptide features were then fragmented and identified by tandem mass spectrometry with the use of inclusion lists.Results and DiscussionsWe assessed the identified proteins to see if there were sets of proteins indicative of specific biological processes that correlate with the presence of disease, and specifically cancer, according to their functional annotations. As expected for such sick animals, many of the proteins identified were related to host immune response. However, a significant number of proteins are also directly associated with processes linked to cancer development, including proteins related to the cell cycle, localization, transport, and cell death. Additional analysis of the same samples in profiling mode, and in triplicate, confirmed that replicate MS analysis of the same plasma sample generated less variation than that observed between plasma samples from different individuals, demonstrating that the reproducibility of the LC-MS platform was sufficient for this application.ConclusionThese results thus show that an LC-MS-based workflow can be a useful tool for the generation of candidate proteins of interest as part of a disease biomarker discovery effort.

Rapid detection and identification of bacteria and other microorganisms has become a field where new analytical methods are needed. An approach, based on the use of pyrolysis coupled to mass spectrometry (Py-MS), for the detection and identification of bacteria in urine and other aqueous samples is described. In this procedure, the sample is placed on a direct insertion probe and pyrolysis is conducted directly into the ion source of the mass spectrometer. Ionization is achieved with a metastable atom bombardment source (MAB). In contrast to electron ionization, this novel ionization source, by providing discrete ionization energies, allows selective ionization and control over fragmentation. The MAB source is coupled to a time-of-flight (Tof) mass analyzer that provides high sensitivity and fast spectral acquisition. Experiments conducted with a number of uropathogenic bacteria, such as Escherichia coli, Citrobacter freundii, Proteus mirabilis and Pseudomonas aeruginosa, reveal that they provide discernable fingerprints when analyzed in urine samples by Py-MAB-Tof. Results can be obtained in minutes and the sensitivity is such that 104 bacteria on the pyrolysis probe are required for analysis.

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300–1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.

e16008 Background: Sipuleucel-T (sip-T) is an autologous cellular immunotherapy approved by the FDA and EMA for treatment of certain patients with asymptomatic or minimally symptomatic metastatic c...

This work shows how fingerprints of mass spectral patterns from microbial isolates are affected by variations in instrumental condition, by sample environment, and by sample handling factors. It describes a novel method by which pattern distortions can be mathematically corrected for variations in factors not amenable to experimental control. One uncontrollable variable is “between-batch” differences in culture media. Another, relevant for determination of noncultured extracts, is differences between the cells’ environmental experience (e.g., starved environmental extracts versus cultured standards). The method suggests that, after a single growth cycle on a solid medium (perhaps, a selective one), pyrolysis MS spectra of microbial isolates can be algorithmically compensated and an unknown isolate identified using a spectral database defined by culture on a different (perhaps, nonselective) medium. This reduces identification time to as few as 24 h from sample collection. The concept also proposes a possible way to compensate certain noncultured, nonisolated samples (e.g., cells concentrated from urine or impacted from aerosol or semi-selectively extracted by immunoaffinity methods from heavily contaminated matrices) for identification within half an hour. Using the method, microbial mass spectra from different labs can be assembled into coherent databases similar to those routinely used to identify pure compounds. This type of data treatment is applicable for rapid detection in biowarfare and bioterror events as well as in forensic, research, and clinical laboratory contexts.

Background Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. Results We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. Conclusion The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.