A method for characterizing proteins, including steps of (a) detecting a plurality of proteins, wherein individual proteins of the plurality are associated with unique identifiers, wherein the detecting distinguishes the identities of the individual proteins and the unique identifiers associated with the individual proteins; (b) digesting the proteins to form peptides, wherein the peptides from each protein are associated with the unique identifiers for the respective individual protein; (c) detecting the peptides and associated unique identifiers, wherein the detecting distinguishes characteristics of individual peptides, and wherein the detecting distinguishes unique identifiers associated with the individual peptides; and (d) correlating characteristics detected in step (c) with individual proteins detected in step (a) based on the unique identifiers associated with the individual proteins and the peptides.

Disclosures herein are directed to methods and compositions for predicting high- and low-risk liver disease in patients. Based on the results achieved from the methods and compositions disclosed herein, liver disease patients can be classified into a prognostic risk group, which enables early diagnosis and prevention of HCC and other lethal complications. Methods and compositions disclosed herein substantially improve the poor prognosis of subjects having or at risk for one or more liver diseases.

Systems and methods to identify new protein targets of a chemical compound or its derivatives were described. The methods can be used for detection of new binding partners as long as the chemical compound can covalently bind to the protein targets. Once protein targets are resolved, information related to new protein targets can then be used to couple with real-world patient data such as adverse events, efficacy data, and disease correlation data to deduce real-world evidence. Systems collectively with all this information can aide clinical development and use of pharmaceutical drug. Methods are provided for detection of covalently bound phenyl vinyl sulfone (PVS) or its derivatives, and afatinib or its derivatives. Furthermore, generation of antiserum recognizing carrier bound PVS or carrier bound afatinib is described. PRMT1 is described as a new target of PVS and RRM1, RRM2, and NFKB are described as new targets of afatinib.

Methods disclosed herein concern cancer proteogenomics, which integrates genomics, transcriptomics and mass spectrometry (MS)-based proteomics to gain insights into cancer biology and treatment efficacy. To promote clinical utility of embodiments herein, proteogenomics approaches were developed for frozen core needle biopsies using tissue-sparing specimen processing with or without a microscaled proteomics workflow.

The present invention provides compositions comprising chimeric polypeptides that bind to free ubiquitin proteins or free ubiquitin-like proteins with high affinity, as well as chimeric polypeptides that bind to both free and conjugated ubiquitin proteins or free and conjugated ubiquitin-like proteins, and methods of using the chimeric polypeptides to determine the amount of free or total ubiquitin or free or total ubiquitin-like proteins in various types of samples.

Systems and methods for automated sample preparation and processing of protein corona are described herein, as well as its application in the discovery of advanced diagnostic tools as well as therapeutic agents.

The present invention provides a method for determining the resistance of a microorganism to a drug by detecting at least a transcript of a drug resistance gene from a microorganism in a biological sample, the method comprising the steps of: (i) lysing the cells by means of a chemical or a mechanical method, thereby obtaining a lysate and cell debris; (ii) obtaining a ribosome-antibody complex from the lysate using an antibody or a fragment thereof which binds specifically to a microorganism-ribosomal protein; (iii) purifying the mRNA associated to the ribosome-antibody complex by means of a nucleic acid extraction method; and (iv) submitting the resulting mRNA to a specific gene detection method, thereby identifying the at least one drug resistance gene transcript of the biological sample. Provided methods and kits allow determining the resistance to antibiotics of a biological sample in a rapid and reliable manner, thereby minimizing the risk of AMR and allowing the definition of the therapeutic potential of a selected antibiotic

There is disclosed a method of identifying colon cancer-specific vesicle-associated proteins from a tissue sample of a subject. The method comprises isolating tissue-resident extracellular vesicles from the tissue sample; identifying vesicle-associated proteins associated with the isolated tissue-resident extracellular vesicles; quantifying the identified vesicle-associated proteins to identify one or more major vesicle-associated proteins; creating vesicle-associated protein profiles for the identified vesicle-associated proteins; and comparing the vesicle-associated protein profiles for the identified vesicle-associated proteins with pre-determined vesicle-associated protein profiles of the tumour tissue samples and/or non-tumour tissue samples.

The present invention relates to a method for monitoring kinase activity or activation in a sample, the method comprises the steps of a) providing a sample comprising a kinase, b) incubating the sample with a protease to cleave the kinase provided in step a) into protease specific proteolytic peptides, c) applying phosphopeptide enrichment to the sample, d) analysing the sample obtained in step c) via liquid chromatography-mass spectrometry, and e) detecting phosphorylations of the kinase provided in step a), wherein the detection of step e) is performed only in case a proteolytic peptide associated with the activation region of the kinase is identified.

Provided herein are methods for identifying new biomarkers for various diseases using proteomics, peptidomics, metabolics, proteoglycomics, glycomics, mass spectrometry and machine learning. The present disclosure also provides glycopeptides as biomarkers for various diseases such as cancer and autoimmune diseases.