Multipolar Atom Types from Theory and Statistical Clustering (MATTS) Data Bank: Impact of Surrounding Atoms on Electron Density from Cluster Analysis

• Published in: Journal of chemical information and modeling Vol. 62; no. 16; pp. 3766 - 3783
• Main Authors: Rybicka, Paulina Maria, Kulik, Marta, Chodkiewicz, Michał Leszek, Dominiak, Paulina Maria
• Format: Journal Article
• Language: English
• Published: Washington American Chemical Society 22.08.2022
• Subjects: Cluster analysis; Clustering; Computational Chemistry; Coordinates; Data banks; Electron density; Electrons; Multipoles; Nucleic acids; Organic chemistry; Parameterization; Parameters; Spatial distribution; Topology
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.2c00145

The multipole model (MM) uses an aspherical approach to describe electron density and can be used to interpret data from X-ray diffraction in a more accurate manner than using the spherical approximation. The MATTS (multipolar atom types from theory and statistical clustering) data bank gathers MM parameters specific for atom types in proteins, nucleic acids, and organic molecules. However, it was not fully understood how the electron density of particular atoms responds to their surroundings and which factors describe the electron density in molecules within the MM. In this work, by applying clustering using descriptors available in the MATTS data bank, that is, topology and multipole parameters, we found the topology features with the biggest impact on the multipole parameters: the element of the central atom, the number of first neighbors, and planarity of the group. The similarities in the spatial distribution of electron density between and within atom type classes revealed distinct and unique atom types. The quality of existing types can be improved by adding better parametrization, definitions, and local coordinate systems. Future development of the MATTS data bank should lead to a wider range of atom types necessary to construct the electron density of any molecule.