The present invention provides a method for identifying differential activation of a bisubstrate protein modifying enzyme between samples, comprising: (i) incubating a first sample with x different concentrations of the non-protein substrate of said enzyme, wherein x is 2 or greater than 2; (ii) quantifying modification of a polypeptide in said sample at each of the x different concentrations of the non-protein substrate; (iii) determining the affinity of said enzyme for said non-protein substrate; (iv) repeating steps (i) to (iii) for a second or subsequent sample; and (v) comparing the affinity of said enzyme for said non-protein substrate between said samples;
wherein a difference in affinity of said enzyme for said non-protein substrate between samples is indicative of differential activation of said enzyme between samples. The present invention also provides a method for identifying an in vivo substrate of a bisubstrate protein modifying enzyme.

Microfluidic devices and methods for using the same are provided. Aspects of the invention include microfluidic devices that include a separation medium and a pan-capture binding medium. The microfluidic devices are configured to subject a sample to two or more directionally distinct electric fields. Also provided are methods of using the devices as well as systems and kits that include the devices. The devices, systems and methods find use in a variety of different applications, including diagnostic and validation assays.

Methods and apparatus for the in vitro modelling of changes that occur on administration of a drug formulation are described, in particular for studying the changes that take place on administration of protein or peptide drug formulations by subcutaneous injection.

Methods for identifying a peptide by analyzing a linear readout representative of at least a portion of at least two amino acids along the peptide using a machine learning model, wherein the machine learning model is trained on linear readouts representative of a set of peptides of known sequence are provided. Methods of training a machine learning model on linear readouts representative of a set of known peptides, and systems for performing the methods of the invention are also provided.

This disclosure comprises devices and methods for determining the identity of individual protein molecules in a complex mixture.

Methods of analyzing glycosylated biomolecules include the steps of producing a deglycosylation mixture of biomolecules deglycosylated by natural or synthetic enzymatic or chemical techniques; providing a reagent solution having a labeling reagent in a polar aprotic, non-nucleophilic organic solvent; and mixing the deglycosylation mixture with the reagent solution in an excess of labeling reagent to produce derivatized glycosylamines. The method steps can be carried out purposefully without depletion of protein matter. A quenching solution can be added to the reaction mixture so that the pH of the reaction mixture is shifted to above 10. The yield of derivatized glycosylamines can be in an amount of about 80 to about 100 mole percent of the reaction mixture with minimal overlabeling, less than 0.2 mole percent. The derivizated glycosylamines can be separated from the reaction mixture and detected by chromatographic detection, fluorescence detection, mass spectrometry (“MS”), or Ultra Violet (“UV”) detection and/or a combination thereof.

The present invention provides methods, immunoassays, kits and devices pertaining to the detection of multiple biomolecules from single cells or other biological entities. It also enables the highly parallel detection of interacting biomolecules from such entities.

The present invention is based on the discovery that functional stratification and/or signaling profiles can be used for diagnosing disease status, determining drug resistance or sensitivity of cancer cells, monitoring a disease or responsiveness to a therapeutic agent, and/or predicting a therapeutic outcome for a subject. Provided herein are assays for diagnosis and/or prognosis of diseases in patients. Also provided are compositions and methods that evaluate the resistance or sensitivity of diseases to targeted therapeutic agents prior to initiation of the therapeutic regimen and to monitor the therapeutic effects of the therapeutic regimen. Also provided are methods for determining the difference between a basal level or state of a molecule in a sample and the level or state of the molecule after stimulation of a portion of the live sample with a modulator ex vivo, wherein the difference is expressed as a value which is indicative of the presence, absence or risk of having a disease. The methods of the invention may also be used for predicting the effect of an agent on the disease and monitoring the course of a subject's therapy.

A phosphoprotein extraction method and a mass spectrometric method using a microbore hollow fiber enzymatic reactor (mHFER) based antigen-antibody reaction and, specifically, to an extraction method and a mass spectrometric method, wherein phosphoproteins or phosphopeptides present in the body are extracted using phosphoserine-, phosphothreonine-, and phosphotyrosine-antibodies, and measured by a mass spectrometer, and thus biomarker phosphoproteins for diagnosis of diseases are found, contributing to early diagnosis of diseases. The mass spectrometric method using the antigen-antibody reaction based extraction method can: minimize temporal and economic burdens resulting from a low extraction rate and a complicated sample pre-treatment; increase the extraction efficiency by using a considerable number of phosphopeptides (or phosphoproteins) and antibodies with strong affinity; and allow the extraction of low-concentration phosphopeptides or phosphoproteins, and thus is expected to have high applicability in discovering disease diagnosis protein markers and identifying and studying mechanisms thereof.

Disclosed is a method that combines high throughput and flexible nature of a cell-free protein microarray with the quantitative capability of surface plasmon resonance to detect >400 different protein interactions in <1 hour. A method of detecting interactions between a targeting agent and one or more proteins of interest is disclosed. The method includes producing a set of proteins of interest using a cell-free protein expression system; providing the set of proteins of interest on a protein microarray wherein each spot in the array comprises a protein of interest; contacting the protein microarray with a targeting agent that binds to one or more of the set of proteins of interest; and detecting the binding of the targeting agent to the set of proteins of interest using surface plasmon resonance imaging (SPRi), thereby detecting the targeting agent and one or more proteins of interest in the micro array.