2024/10/10 by Olga Anosova, Anosova, Olga, Alexey Gorelov +7 · 1 citation
Biochemistry, Genetics and Molecular Biology · Materials Science · #51-08 #51F20 #51M15 #51N20 #Computational Geometry (cs.CG) #Enzyme Structure and Function #FOS: Computer and information sciences #Microtubule and mitosis dynamics #Protein Structure and Dynamics
paper · pdf · doi:10.48550/arxiv.2410.08203
openalex publication_date 2024/10/10 · openalex created_date 2024/10/13 · openalex updated_date 2026/08/01
Proteins are large biomolecules that regulate all living organisms and consist of one or several chains. The primary structure of a protein chain is a sequence of amino acid residues whose three main atoms (alpha-carbon, nitrogen, and carbonyl carbon) form a protein backbone. The tertiary structure is the rigid shape of a protein chain represented by atomic positions in 3-dimensional space. Because different geometric structures often have distinct functional properties, it is important to continuously quantify differences in rigid shapes of protein backbones. Unfortunately, many widely used similarities of proteins fail axioms of a distance metric and discontinuously change under tiny perturbations of atoms. This paper develops a complete invariant that identifies any protein backbone in 3-dimensional space, uniquely under rigid motion. This invariant is Lipschitz bi-continuous in the sense that it changes up to a constant multiple of a maximum perturbation of atoms, and vice versa. The new invariant has been used to detect thousands of (near-)duplicates in the Protein Data Bank, whose presence inevitably skews machine learning predictions. The resulting invariant space allows low-dimensional maps with analytically defined coordinates that reveal substantial variability in the protein universe.