Theoretical 3D electron diffraction electrostatic potential maps of proteins modeled with a multipolar pseudoatom data bank

X-rays, electrons, and neutrons as probes of atomic matter

X-rays, electrons, and neutrons probe different properties of matter. X-rays feel electron density (ED). Electrons sense the electrostatic potential (ESP) of electrons and nuclei. Neutrons are sensitive to nuclear coherent scattering length (NCSL). While NCSL maps are widely understood to be different, ED and ESP maps are tacitly assumed to be similar. Here, I show that the belief in ED and ESP map equivalence is mistaken, but contains a grain of truth. Using density functional theory (DFT), the Bethe-Mott (BM) relation, and the Thomas-Fermi (TF) and Cromer-Mann (CM) atomic models, I show that ED and ESP maps are indeed more similar to each other than to NCSL maps. Nevertheless, peak and integrated map values depend differently on the atomic order number and on the contributions from electrons in the inner and outer CM shells. ED and ESP maps also differ in the sign and relative magnitude of excess charge effects.

Exploiting the full potential of cryo-EM maps

The Coulomb potential maps generated by electron microscopy (EM) experiments contain not only information about the position but also about the charge state of the atom. This feature of EM maps allows the identification of specific ions and the protonation state of amino acid side chains in the sample. Here, we summarize qualitative observations of charges in EM maps, discuss the difficulties in interpreting the charge in Coulomb potential maps with respect to distinguishing it from radiation damage, and outline considerations to implement the correct charge in fitting algorithms.

Multipolar Atom Types from Theory and Statistical Clustering (MATTS) Data Bank: Restructurization and Extension of UBDB

A fast and accurate operational model of electron density is crucial in many scientific disciplines including crystallography, molecular biology, pharmaceutical, and structural chemistry. In quantum crystallography, the aspherical refinement of crystal structures is becoming increasingly popular because of its accurate description in terms of physically meaningful properties. The transferable aspherical atom model (TAAM) is quick and precise, though it requires a robust algorithm for atom typing and coverage of the most popular atom types present in small organic molecules. Thus, the University at Buffalo Databank (UBDB) has been renamed to the Multipolar Atom Types from Theory and Statistical clustering (MATTS) data bank, broadened, restructured, and implemented into the software DiSCaMB with 651 atom types obtained from 2316 small-molecule crystal structures containing C, H, N, O, P, S, F, Cl, and Br atoms. MATTS2021 data bank now covers most of the small molecules, peptides, RNA, DNA, and some frequently occurring cations and anions in biological, pharmaceutical, and organic materials, including the majority of known crystal structures composed of the above elements. The multipole model parameters (P_val, κ, κ′, P_lm) obtained for different atom types were greatly influenced by neighboring atom types, hybridization, geometrical strain in the ring system, and charges on the molecule. Contrary to previous findings, the atoms showing variable oxidation states and ions deviate from the linear dependence of monopole-derived charges on the expansion–contraction κ parameter.

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

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.