Abstract

Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers—the numbers of connected components, holes and voids—in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis.Abstract: Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Targeting pathogenic T cells using vaccines consisting of synthetic peptides representing T cell epitopes is one such strategy that is currently being evaluated with encouraging results. Future challenges in the development of therapeutic vaccines include selection of appropriate antigens and peptides, optimization of peptide dose and route of administration and identifying strategies to induce bystander suppression. Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given peptide based on their structural complementarity. Compared to other peptide and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Peptidomimetics, deriving from structure-based, combinatorial or protein dissection approaches, can play a key role as hit compounds. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets. Here, in Biogenea we have for the first time generated Quantum representation algorithms for topological and geometric analysis of a surface designed IHMVYSK peptide-mimo based chemo-ligand comprising therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications.

Keywords

Quantum algorithms, topological, geometric, analysis, surface, representation, peptide-mimo, chemo-ligand, therapeutic, vaccine-like, agonistic, potential, druggable, synthetic, regulator, allergic, autoimmune, treatment, applications; Quantum representation algorithms; topological; geometric analysis; surface designed; IHMVYSK peptide-mimo based; chemo-ligand; therapeutic vaccine-like; agonistic properties; novel druggable; synthetic regulator ; allergic and autoimmune treatment applications.