Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome.

The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.

Laboratory information management software for engineered mini-protein therapeutic workflow.

BACKGROUND: Protein based therapeutics are one of the fastest growing classes of novel medical interventions in areas such as cancer, infectious disease, and inflammation. Protein engineering plays an important role in the optimization of desired therapeutic properties such as reducing immunogenicity, increasing stability for storage, increasing target specificity, etc. One category of protein therapeutics is nature-inspired bioengineered cystine-dense peptides (CDPs) for various biological targets. These engineered proteins are often further modified by synthetic chemistry. For example, candidate mini-proteins can be conjugated into active small molecule drugs. We refer to modified mini-proteins as "Optides" (Optimized peptides). To efficiently serve the multidisciplinary lab scientists with varied therapeutic portfolio research goals in a non-commercial setting, a cost effective extendable laboratory information management system (LIMS) is/was needed. RESULTS: We have developed a LIMS named Optide-Hunter for a generalized engineered protein compounds workflow that tracks entities and assays from creation to preclinical experiments. The implementation and custom modules are built using LabKey server, which is an Open Source platform for scientific data integration and analysis. Optide-Hunter contains a compound registry, in-silico assays, high throughput production, large-scale production, in vivo assays and data extraction from a specimen-tracking database. It is used to store, extract, and view data for various therapeutics projects. Optide-Hunter also includes external processing stand-alone software (HPLCPeakClassifierApp) for automated chromatogram classification. The HPLCPeakClassifierApp is used for pre-processing of HPLC data prior to loading to Optide-Hunter. The custom implementation is done using data transformation modules in R, SQL, javascript, and java and is Open Source to assist new users in customizing it for their unique workflows. Instructions for exploring a deployed version of Optide-Hunter can be found at https://www.labkey.com/case%20study/optide-hunter CONCLUSION: The Optide-Hunter LIMS system is designed and built to track the process of engineering, producing and prioritizing protein therapeutic candidates. It can be easily adapted and extended for use in small or large research laboratories where multidisciplinary scientists are collaborating to engineer compounds for potential therapeutic or protein science applications. Open Source exploration of Optide-Hunter can help any bioinformatics scientist adapt, extend, and deploy an equivalent system tailored to each laboratory's workflow.

An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples.

BACKGROUND: Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing. RESULTS: We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low µg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples. CONCLUSIONS: We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.

mProphet: automated data processing and statistical validation for large-scale SRM experiments.

Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.

TraML--a standard format for exchange of selected reaction monitoring transition lists.

Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring.

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles).

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the United States National Cancer Institute convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: 1) an evolving list of comprehensive quality metrics and 2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology.

Selected reaction monitoring (SRM) is an accurate quantitative technique, typically used for small-molecule mass spectrometry (MS). SRM has emerged as an important technique for targeted and hypothesis-driven proteomic research, and is becoming the reference method for protein quantification in complex biological samples. SRM offers high selectivity, a lower limit of detection and improved reproducibility, compared to conventional shot-gun-based tandem MS (LC-MS/MS) methods. Unlike LC-MS/MS, which requires computationally intensive informatic postanalysis, SRM requires preacquisition bioinformatic analysis to determine proteotypic peptides and optimal transitions to uniquely identify and to accurately quantitate proteins of interest. Extensive arrays of bioinformatics software tools, both web-based and stand-alone, have been published to assist researchers to determine optimal peptides and transition sets. The transitions are oftentimes selected based on preferred precursor charge state, peptide molecular weight, hydrophobicity, fragmentation pattern at a given collision energy (CE), and instrumentation chosen. Validation of the selected transitions for each peptide is critical since peptide performance varies depending on the mass spectrometer used. In this review, we provide an overview of open source and commercial bioinformatic tools for analyzing LC-MS data acquired by SRM.

PASSEL: the PeptideAtlas SRMexperiment library.

Public repositories for proteomics data have accelerated proteomics research by enabling more efficient cross-analyses of datasets, supporting the creation of protein and peptide compendia of experimental results, supporting the development and testing of new software tools, and facilitating the manuscript review process. The repositories available to date have been designed to accommodate either shotgun experiments or generic proteomic data files. Here, we describe a new kind of proteomic data repository for the collection and representation of data from selected reaction monitoring (SRM) measurements. The PeptideAtlas SRM Experiment Library (PASSEL) allows researchers to easily submit proteomic data sets generated by SRM. The raw data are automatically processed in a uniform manner and the results are stored in a database, where they may be downloaded or browsed via a web interface that includes a chromatogram viewer. PASSELenables cross-analysis of SRMdata, supports optimization of SRMdata collection, and facilitates the review process of SRMdata. Further, PASSELwill help in the assessment of proteotypic peptide performance in a wide array of samples containing the same peptide, as well as across multiple experimental protocols.

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles).

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed upon two primary needs for the wide use of quality metrics: (i) an evolving list of comprehensive quality metrics and (ii) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in Proteomics, Proteomics Clinical Applications, Journal of Proteome Research, and Molecular and Cellular Proteomics, as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles).

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (1) an evolving list of comprehensive quality metrics and (2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

Ex silico engineering of cystine-dense peptides yielding a potent bispecific T cell engager.

Cystine-dense peptides (CDPs) are a miniprotein class that can drug difficult targets with high affinity and low immunogenicity. Tools for their design, however, are not as developed as those for small-molecule and antibody drugs. CDPs have diverse taxonomic origins, but structural characterization is lacking. Here, we adapted Iterative Threading ASSEmbly Refinement (I-TASSER) and Rosetta protein modeling software for structural prediction of 4298 CDP scaffolds and performed in silico prescreening for CDP binders to targets of interest. Mammalian display screening of a library of docking-enriched, methionine and tyrosine scanned (DEMYS) CDPs against PD-L1 yielded binders from four distinct CDP scaffolds. One was affinity-matured, and cocrystallography yielded a high-affinity (KD = 202 pM) PD-L1-binding CDP that competes with PD-1 for PD-L1 binding. Its subsequent incorporation into a CD3-binding bispecific T cell engager produced a molecule with pM-range in vitro T cell killing potency and which substantially extends survival in two different xenograft tumor-bearing mouse models. Both in vitro and in vivo, the CDP-incorporating bispecific molecule outperformed a comparator antibody-based molecule. This CDP modeling and DEMYS technique can accelerate CDP therapeutic development.

The Protein Information and Property Explorer 2: gaggle-like exploration of biological proteomic data within one webpage.

The Protein Information and Property Explorer 2 (PIPE2) is an enhanced software program and updated web application that aims at providing the proteomic researcher a simple, intuitive user interface through which to begin inquiry into the biological significance of a list of proteins typically produced by MS/MS proteomic processing software. PIPE2 includes an improved interface, new data visualization options, and new data analysis methods for combining disparate, but related, data sets. In particular, PIPE2 has been enhanced to handle multi-dimensional data such as protein abundance, gene expression, and/or interaction data. The current architecture of PIPE2, modeled after that of Gaggle (a programming infrastructure for interoperability between separately developed software tools), contains independent functional units that can be instantiated and pieced together at the user's discretion to form a pipelined analysis workflow. Among these functional units is the Network Viewer component, which adds rich network analysis capabilities to the suite of existing proteomic web resources. Additionally, PIPE2 implements a framework within which new analysis procedures can be easily deployed and distributed over the World Wide Web. PIPE2 is available as a web service at http://pipe2.systemsbiology.net/.

ATAQS: A computational software tool for high throughput transition optimization and validation for selected reaction monitoring mass spectrometry.

BACKGROUND: Since its inception, proteomics has essentially operated in a discovery mode with the goal of identifying and quantifying the maximal number of proteins in a sample. Increasingly, proteomic measurements are also supporting hypothesis-driven studies, in which a predetermined set of proteins is consistently detected and quantified in multiple samples. Selected reaction monitoring (SRM) is a targeted mass spectrometric technique that supports the detection and quantification of specific proteins in complex samples at high sensitivity and reproducibility. Here, we describe ATAQS, an integrated software platform that supports all stages of targeted, SRM-based proteomics experiments including target selection, transition optimization and post acquisition data analysis. This software will significantly facilitate the use of targeted proteomic techniques and contribute to the generation of highly sensitive, reproducible and complete datasets that are particularly critical for the discovery and validation of targets in hypothesis-driven studies in systems biology. RESULT: We introduce a new open source software pipeline, ATAQS (Automated and Targeted Analysis with Quantitative SRM), which consists of a number of modules that collectively support the SRM assay development workflow for targeted proteomic experiments (project management and generation of protein, peptide and transitions and the validation of peptide detection by SRM). ATAQS provides a flexible pipeline for end-users by allowing the workflow to start or end at any point of the pipeline, and for computational biologists, by enabling the easy extension of java algorithm classes for their own algorithm plug-in or connection via an external web site.This integrated system supports all steps in a SRM-based experiment and provides a user-friendly GUI that can be run by any operating system that allows the installation of the Mozilla Firefox web browser. CONCLUSIONS: Targeted proteomics via SRM is a powerful new technique that enables the reproducible and accurate identification and quantification of sets of proteins of interest. ATAQS is the first open-source software that supports all steps of the targeted proteomics workflow. ATAQS also provides software API (Application Program Interface) documentation that enables the addition of new algorithms to each of the workflow steps. The software, installation guide and sample dataset can be found in http://tools.proteomecenter.org/ATAQS/ATAQS.html.

jTraML: an open source Java API for TraML, the PSI standard for sharing SRM transitions.

We here present jTraML, a Java API for the Proteomics Standards Initiative TraML data standard. The library provides fully functional classes for all elements specified in the TraML XSD document, as well as convenient methods to construct controlled vocabulary-based instances required to define SRM transitions. The use of jTraML is demonstrated via a two-way conversion tool between TraML documents and vendor specific files, facilitating the adoption process of this new community standard. The library is released as open source under the permissive Apache2 license and can be downloaded from http://jtraml.googlecode.com . TraML files can also be converted online at http://iomics.ugent.be/jtraml .

Halogenated peptides as internal standards (H-PINS): introduction of an MS-based internal standard set for liquid chromatography-mass spectrometry.

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.

ARTEMIS: A Novel Mass-Spec Platform for HLA-Restricted Self and Disease-Associated Peptide Discovery.

Conventional immunoprecipitation/mass spectroscopy identification of HLA-restricted peptides remains the purview of specializing laboratories, due to the complexity of the methodology, and requires computational post-analysis to assign peptides to individual alleles when using pan-HLA antibodies. We have addressed these limitations with ARTEMIS: a simple, robust, and flexible platform for peptide discovery across ligandomes, optionally including specific proteins-of-interest, that combines novel, secreted HLA-I discovery reagents spanning multiple alleles, optimized lentiviral transduction, and streamlined affinity-tag purification to improve upon conventional methods. This platform fills a middle ground between existing techniques: sensitive and adaptable, but easy and affordable enough to be widely employed by general laboratories. We used ARTEMIS to catalog allele-specific ligandomes from HEK293 cells for seven classical HLA alleles and compared results across replicates, against computational predictions, and against high-quality conventional datasets. We also applied ARTEMIS to identify potentially useful, novel HLA-restricted peptide targets from oncovirus oncoproteins and tumor-associated antigens.

Optimal therapeutic targeting by HDAC inhibition in biopsy-derived treatment-naïve diffuse midline glioma models.

BACKGROUND: Diffuse midline gliomas (DMGs), including diffuse intrinsic pontine gliomas (DIPGs), have a dismal prognosis, with less than 2% surviving 5 years postdiagnosis. The majority of DIPGs and all DMGs harbor mutations altering the epigenetic regulatory histone tail (H3 K27M). Investigations addressing DMG epigenetics have identified a few promising drugs, including the HDAC inhibitor (HDACi) panobinostat. Here, we use clinically relevant DMG models to identify and validate other effective HDACi and their biomarkers of response. METHODS: HDAC inhibitors were tested across biopsy-derived treatment-naïve in vitro and in vivo DMG models with biologically relevant radiation resistance. RNA sequencing was performed to define and compare drug efficacy and to map predictive biomarkers of response. RESULTS: Quisinostat and romidepsin showed efficacy with low nanomolar half-maximal inhibitory concentration (IC50) values (~50 and ~5 nM, respectively). Comparative transcriptome analyses across quisinostat, romidepsin, and panobinostat showed a greater degree of shared biological effects between quisinostat and panobinostat, and less overlap with romidepsin. However, some transcriptional changes were consistent across all 3 drugs at similar biologically effective doses, such as overexpression of troponin T1 slow skeletal type (TNNT1) and downregulation of collagen type 20 alpha 1 chain (COL20A1), identifying these as potential vulnerabilities or on-target biomarkers in DMG. Quisinostat and romidepsin significantly (P < 0.0001) inhibited in vivo tumor growth. CONCLUSIONS: Our data highlight the utility of treatment-naïve biopsy-derived models; establishes quisinostat and romidepsin as effective in vivo; illuminates potential mechanisms and/or biomarkers of DMG cell lethality due to HDAC inhibition; and emphasizes the need for brain tumor-penetrant versions of potentially efficacious agents.

Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring.

In many studies, particularly in the field of systems biology, it is essential that identical protein sets are precisely quantified in multiple samples such as those representing differentially perturbed cell states. The high degree of reproducibility required for such experiments has not been achieved by classical mass spectrometry-based proteomics methods. In this study we describe the implementation of a targeted quantitative approach by which predetermined protein sets are first identified and subsequently quantified at high sensitivity reliably in multiple samples. This approach consists of three steps. First, the proteome is extensively mapped out by multidimensional fractionation and tandem mass spectrometry, and the data generated are assembled in the PeptideAtlas database. Second, based on this proteome map, peptides uniquely identifying the proteins of interest, proteotypic peptides, are selected, and multiple reaction monitoring (MRM) transitions are established and validated by MS2 spectrum acquisition. This process of peptide selection, transition selection, and validation is supported by a suite of software tools, TIQAM (Targeted Identification for Quantitative Analysis by MRM), described in this study. Third, the selected target protein set is quantified in multiple samples by MRM. Applying this approach we were able to reliably quantify low abundance virulence factors from cultures of the human pathogen Streptococcus pyogenes exposed to increasing amounts of plasma. The resulting quantitative protein patterns enabled us to clearly define the subset of virulence proteins that is regulated upon plasma exposure.

A potent peptide-steroid conjugate accumulates in cartilage and reverses arthritis without evidence of systemic corticosteroid exposure.

On-target, off-tissue toxicity limits the systemic use of drugs that would otherwise reduce symptoms or reverse the damage of arthritic diseases, leaving millions of patients in pain and with limited physical mobility. We identified cystine-dense peptides (CDPs) that rapidly accumulate in cartilage of the knees, ankles, hips, shoulders, and intervertebral discs after systemic administration. These CDPs could be used to concentrate arthritis drugs in joints. A cartilage-accumulating peptide, CDP-11R, reached peak concentration in cartilage within 30 min after administration and remained detectable for more than 4 days. Structural analysis of the peptides by crystallography revealed that the distribution of positive charge may be a distinguishing feature of joint-accumulating CDPs. In addition, quantitative whole-body autoradiography showed that the disulfide-bonded tertiary structure is critical for cartilage accumulation and retention. CDP-11R distributed to joints while carrying a fluorophore imaging agent or one of two different steroid payloads, dexamethasone (dex) and triamcinolone acetonide (TAA). Of the two payloads, the dex conjugate did not advance because the free drug released into circulation was sufficient to cause on-target toxicity. In contrast, the CDP-11R-TAA conjugate alleviated joint inflammation in the rat collagen-induced model of rheumatoid arthritis while avoiding toxicities that occurred with nontargeted steroid treatment at the same molar dose. This conjugate shows promise for clinical development and establishes proof of concept for multijoint targeting of disease-modifying therapeutic payloads.

Simultaneous extraction and analysis of multiple cystine-dense peptides by µSPE and microflow-MS/MS from plasma.

Aim: Develop a universal extraction and liquid chromatography-mass spectrometer method to simultaneously analyze cystine-dense peptide (CDP) miniproteins from rat and human plasma. The results of the analysis will be used to assist selection of therapeutic drug candidates from the vast CDP library. Methods & results: A micro-elution solid-phase extraction method was developed for the sample preparation of the CDP peptides in rat and human plasma followed by analysis by microflow liquid chromatography MS/MS. The methods developed for drug discovery were found to be accurate (±≤15.2% from nominal concentrations) and precise (≤13.4% CV), with a dynamic range of 1.00-500 ng/ml and extraction recoveries of 47.2-99.0%. Conclusion: This bioanalytical method can be utilized to screen CDP proteins and other miniproteins for drug discovery, candidate selection and further drug development.