Maximum n-times Coverage for Vaccine DesignDownload PDF

29 Sept 2021, 00:35 (edited 21 Feb 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: computational biology, vaccine design, COVID-19, maximum n-times coverage, combinatorial optimization, integer linear programming
  • Abstract: We introduce the maximum $n$-times coverage problem that selects $k$ overlays to maximize the summed coverage of weighted elements, where each element must be covered at least $n$ times. We also define the min-cost $n$-times coverage problem where the objective is to select the minimum set of overlays such that the sum of the weights of elements that are covered at least $n$ times is at least $\tau$. Maximum $n$-times coverage is a generalization of the multi-set multi-cover problem, is NP-complete, and is not submodular. We introduce two new practical solutions for $n$-times coverage based on integer linear programming and sequential greedy optimization. We show that maximum $n$-times coverage is a natural way to frame peptide vaccine design, and find that it produces a pan-strain COVID-19 vaccine design that is superior to 29 other published designs in predicted population coverage and the expected number of peptides displayed by each individual's HLA molecules.
  • One-sentence Summary: We introduce the maximum $n$-times coverage problem that selects $k$ overlays to maximize the summed coverage of weighted elements, where each element must be covered at least $n$ times, and show its importance for vaccine design.
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