Visualizing Amino Acid Substitutions in a Physicochemical Vector Space

A three-dimensional representation of the twenty proteinogenic amino acids in a physicochemical space is presented. Vectors corresponding to amino acid substitutions are classified based on whether they are accessible via a single-nucleotide mutation. It is shown that the standard genetic code establishes a “choice architecture” that permits nearly independent tuning of the properties related with size and those related with hydrophobicity. This work sheds light on the non-arbitrary benefits of evolvability that may have shaped the development standard genetic code to increase the probability that adaptive point mutations will be generated. Illustrations of the usefulness of visualizing amino acid substitutions in a 3D physicochemical space are shown using recent datasets collected regarding the SARS-CoV-2 receptor binding domain. First, the substitutions most responsible for antibody escape are almost always inaccessible via single nucleotide mutation, and change multiple properties concurrently. Second, it is shown that assays of ACE2 binding by sarbecovirus variants, including the viruses responsible for SARS and COVID-19, are more easily understood when plotted with this method. The results of this research can extend our understanding of certain hereditary disorders caused by point mutations, as well as guide the development of rational protein and vaccine design.