The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

Disclosed are methods related to identifying an essential gene which serves as an antibiotic target in a bacterium.

A method of analysis using mass spectrometry and/or ion mobility spectrometry is disclosed comprising: (a) using a first device to generate smoke, aerosol or vapour from a target in vitro or ex vivo cell population; (b) mass analysing and/or ion mobility analysing said smoke, aerosol or vapour, or ions derived therefrom, in order to obtain spectrometric data; and (c) analysing said spectrometric data in order to identify and/or characterise said target cell population or one or more cells and/or compounds present in said target cell population.

A method of analysis using mass spectrometry and/or ion mobility spectrometry is disclosed comprising: (a) using a first device to generate smoke, aerosol or vapour from a target in vitro or ex vivo cell population; (b) mass analysing and/or ion mobility analysing said smoke, aerosol or vapour, or ions derived therefrom, in order to obtain spectrometric data; and (c) analysing said spectrometric data in order to identify and/or characterise said target cell population or one or more cells and/or compounds present in said target cell population.

Systems and techniques are described to generate amino acid sequences of target proteins based on amino acid sequences of template proteins using machine learning techniques. The amino acid sequences of the target proteins can be generated based on data that constrains the modifications that can be made to the amino acid sequences of the template proteins. In illustrative examples, the template proteins can include antibodies produced by a non-human mammal that bind to an antigen and the target proteins can correspond to human antibodies with a region having at least a threshold amount of identity with the binding region of the template antibody. Generative adversarial networks can be used to produce the amino acid sequences of the target proteins.

The present invention provides compositions and methods that can be used to identify an antigen or epitope region of an antigen specific for a disease or other condition. Such methods incorporate k-mer binding statistics to serum antibody from condition and control cohort samples to predict the suitability of antigen sequences identified as relevant to the disease or condition as antigen markers. Also disclosed herein are systems for performing the same.

Aspects of the present disclosure relate to computing systems and computational methods for docking a library of compounds against a massive amount of conformations of a protein of interest.

Aspects of the present disclosure relate to computing systems and computational methods for docking a library of compounds against a massive amount of conformations of a protein of interest.

The present disclosure relates to a method for generating variants of a protein based on a native protein regulated by allosteric pathway, the method comprising: i) providing 3D structures of the native protein; ii) identifying at least one pair of coupled allosteric sites within the amino acid sequence of the native protein named microswitch; iii) generating in silico mutations of said identified microswitch to generate a pool of variants; iv) computing at least one score reflecting the variation in allosteric coupling; and/or the variation in the relative stability v) predicting the activity of each variant compared to the native protein based on the computed score. The disclosure also concern a computer implemented program to carry out said method, and a variant of a protein or an active fragment thereof, a polynucleotide.