Abstract: In a first aspect of the present disclosure, there is provided a computer-implemented method of predicting a binding affinity of a query binder molecule to a query target molecule, the query binder molecule having a first amino acid sequence and the query target molecule having a second amino acid sequence, the method comprising: computing, with the at least one processor, the binding affinity for the query binder molecule to the query target molecule as a weighted combination of reference binding values of reference binder-target subsequence pairs, wherein weights of the weighted combination are based on similarity scores.

Abstract: The present invention relates to a method for characterising the HLA status of a genetic sample obtained from a subject, comprising the steps of: i. carrying out DNA or RNA sequencing on said genetic sample obtained from said subject; ii. aligning the obtained sequence with one or more reference HLA allele sequences; iii. Applying a variant calling technique to identify the presence of or type of variant(s) in the HLA sequence of said genetic sample thereby to determine the HLA status.

Abstract: The present invention relates to a coronavirus vaccine composition, comprising one or more epitopes suitable for stimulating a broad adaptive immune response across a plurality of human leukocyte antigen (HLA) populations, for either MHC Class I and/or MHC Class II immunogenicity. The selection of such epitopes is made possible by the generation of predictive data by an artificial intelligence (AI)-driven platform, through the analysis of large scale epitope mapping of the SARS-CoV-2 proteome and epitope scoring based upon predicted immunogenicity, followed by robust statistical analysis and Monte Carlo-based simulation. The vaccine compositions of the present invention are suitable for use in the therapeutic or prophylactic treatment of SARS-CoV-2 infections. The invention also describes methods for using said compositions.

Abstract: A computer-implemented method of identifying one or more candidate regions of one or more source proteins that are predicted to instigate an adaptive immunogenic response across a plurality of human leukocyte antigen, HLA, types, wherein the one or more source proteins has an amino acid sequence is disclosed.

Abstract: According to a first aspect of the present invention there is provided a computer-implemented method of predicting a binding affinity value of a query binder molecule to a query target molecule, the query binder molecule having a first amino acid sequence and the query target molecule having a second amino acid sequence, the method comprising: encoding the first and second amino acid sequences together as a plurality of data elements to generate an encoded pair of amino acids, each data element of the encoded pair representing which amino acids from the first and second amino acid sequences are paired at a respective contact point between the first amino acid sequence and the second amino acid sequence to form a contact point pair, wherein a contact point pair is a pairing of amino acids from a binder molecule and a target molecule which are proximal to one another to influence binding; and, applying a machine learning or statistical model to the encoded pair of amino acids to predict a binding affinity value

Abstract: In a first aspect of the present disclosure, there is provided a computer-implemented method of predicting a binding affinity of a query binder molecule to a query target molecule, the query binder molecule having a first amino acid sequence and the query target molecule having a second amino acid sequence, the method comprising: computing, with the at least one processor, the binding affinity for the query binder molecule to the query target molecule as a weighted combination of reference binding values of reference binder-target subsequence pairs, wherein weights of the weighted combination are based on similarity scores.

Abstract: The present invention provides a method for identifying peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation. In particular, the invention/method controls for the influence of protein abundance, stability and HLA/MHC binding on processing and presentation, enabling a machine-learning algorithm or statistical inference model trained using the method to be applied to any test peptide regardless of its HLA/MHC restriction i.e. the algorithm operates in a HLA/MHC-agnostic manner. This is attained through the building of positive and negative data sets of peptide sequences (peptides identified or inferred from surface bound or secreted MHC/peptide complexes in the literature, and those which are not).