The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data, and in particular mass spectrometry data obtained by data-independent acquisition. The systems and methods achieve improvements in sequencing accuracy over existing systems and methods and enables complete assembly of novel protein sequences without assisting databases. To sequence peptides from mass spectrometry data obtained by data-independent acquisition, precursor profiles representing intensities of one or more precursor ion signals associated with a precursor retention time and fragment ion spectra representing signals from fragment ions and fragment retention times are fed into a neural network.

An in vitro or ex vivo method for determining the ability of an individual to respond to a stimulus, based on the measurement of the expression of at least two different biomarkers, selected from different lists among three lists of biomarkers, from a blood sample of the individual, incubated with the stimulus, as well as tools allowing the implementation of this method and the use of these tools.

The present invention provides a method for controlling a speed of a polypeptide passing through a nanopore and use thereof in determining an amino acid sequence of a polypeptide. Specifically, the method comprises: conjugating a polynucleotide to the polypeptide to give a polynucleotide-polypeptide conjugate, and applying a voltage across the nanopore in the presence of a polynucleotide binding enzyme to move the conjugate through the nanopore. The polynucleotide binding enzyme controls the movement of the polynucleotide and thereby controls the movement of the conjugated polypeptide in the nanopore, thus controlling the speed of the polypeptide passing through the nanopore. While controlling the speed of the polypeptide, the present invention reads a nanopore current signal during the process of the polypeptide passing through the nanopore to give an electrical signal of the polypeptide. The electrical signal can be further used to acquire an amino acid sequence of the polypeptide, to identify the polypeptide or a part thereof, or to establish a library of polypeptide electrical signals.

Methods and devices obtain cellular-component, e.g., proteins, RNA, DNA, metabolites, and combinations of these, from a solid tissue in-vivo using electroporation, and subsequently profile such tissue either inside or outside the subject's body. A method for determining if a solid tissue of a subject includes a benign or malignant tumor, or if a space occupying lesion (SOL) within the solid tissue is malignant or benign, includes placing at least one electroporation-electrode within the solid tissue, or within the SOL or in proximity thereto; applying pulsed electric field (PEF) via the at least one electroporation-electrode to thereby induce permeabilization of cells of the solid tissue or the SOL, and consequently release of at least one cellular-component therefrom to an extracellular matrix between and surrounding the cells; extracting the at least one cellular-component from the extracellular matrix.

The present invention refers to p53 sequence and post translational modifications (PTMs) and to their use as biomarkers in the diagnosis of neurodegenerative disease and cognitive decline and/or in the prognosis of Alzheimer's disease at different stages and/or of neurodegenerative disease in a biological sample. The invention also provides for a 1) diagnostic method based on a highly accurate mass spectrometry analysis for the diagnosis of neurodegenerative disease, including Mild Cognitive Impairment (MCI), Alzheimer's disease (AD), fronto-temporal dementia (FTD), Lewi's Body (LB), and vascular dementia (VD) in a subject, by evaluating the PTMs to the said p53 linear sequence protein and possible cut of its full sequence specifically in human plasma of patients; and 2) prognosis of AD in CU and MCI patients.

A modified peptide or a modified polypeptide has the amino acid sequence of Thr-Val-Asp-Ser-Cys-Leu-Thr (SEQ ID NO: 1) and adhesiveness to a norbornene-based polymer. A ratio of the total number of amino acids constituting the modified peptide or the modified polypeptide to the number of oligopeptides consisting of the amino acid sequence of SEQ ID NO: 1 contained in the modified peptide or the modified polypeptide is 7 or more and 80 or less. The number of oligopeptides consisting of the amino acid sequence of SEQ ID NO: 1 is 1.

The present description provides methods, assays and reagents useful for sequencing proteins. Sequencing proteins in a broad sense involves observing the plausible identity and order of amino acids, which is useful for sequencing single polypeptide molecules or multiple molecules of a single polypeptide. In one aspect, the methods are useful for sequencing multiple polypeptides. The methods and reagents described herein can be useful for high resolution interrogation of the proteome and enabling ultrasensitive diagnostics critical for early detection of diseases.

The present disclosure provides methods of identifying and quantifying the peptides displayed by the major histocompatibility complex (MHC). Such methods may comprise the ability to determine the type, identity, and quantity of each peptide displayed by the MHC. In some embodiments, these methods may be used to develop an anti-cancer therapy or type the HLA of a patient. Also provided herein are compositions comprising peptides from the MHC which have been prepared for sequencing.

The present invention relates to the field of biochemistry, more particularly to proteomics, more particularly to protein sequencing, even more particularly to single molecule peptide sequencing. The invention discloses methods for single molecule protein sequencing and/or amino acid identification using cleavage inducing agents which are not specific for one particular amino acid, cleave polypeptides step by step from the N-terminus onwards and provide information on the identity of the cleaved amino acids based on the reaction kinetics.

The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data. The systems and methods achieve improvements in sequencing accuracy over existing systems and methods and enables complete assembly of novel protein sequences without assisting databases. The present systems and methods are re-trainable to adapt to new sources of data and provides a complete end-to-end training and prediction solution, which is advantageous given the growing massive amount of data. The systems and methods combine deep learning and dynamic programming to solve optimization problems.