Enzymes catalyze a number of reactions with high efficiency and stereoselectivity. It is thought that strong, direct, and permanent electric fields within the active site of the enzyme contribute to the superb catalytic efficiency of enzymes. This effect is called electrostatic preorganization. Most often, electrostatic preorganization is analyzed by evaluating the local electric field at discrete points, such as a bond center, using, for example, vibrational Stark spectroscopy. However, the protein macromolecule creates a significantly more complicated heterogeneous electric field that affects the entire active site, whose total change density thus gets perturbed, with the implications for the catalytic mechanism. We present a global distribution of streamlines method to analyze the topology of the heterogeneous electric fields in within an enzyme active site. We focus on ketosteroid isomerase (KSI), an enzyme known to produce a field on the order of 100 MV/cm along the critical carbonyl bond in the steroid substrate. We investigate how mutations known to cause activity changes, as well as applied small external electric fields perturb the electric fields in the KSI active site. Where classical single-point analysis failed, using our method allowed us to properly correlate global changes in the electric field to changes in the reaction barrier. We were able to show that topologically similar local electric fields had similar reaction barriers.

The developments of the open-source chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes can address, while showing that is an attractive platform for state-of-the-art atomistic computer simulations.

In this paper we establish a monolayer of Mn on W͑110͒ as a model system for two-dimensional itinerant antiferromagnetism. Combining scanning tunneling microscopy ͑STM͒, low-energy electron diffraction, and ab initio calculations performed with the full-potential linearized augmented plane wave method we have studied the structural, electronic, and magnetic properties of a Mn monolayer on W͑110͒. Our experimental results indicate that in spite of the huge tensile strain Mn grows pseudomorphically on W͑110͒ up to a thickness of three monolayers. Intermixing between the Mn overlayer and the W substrate can be excluded. Using these structural data as a starting point for the ab initio calculations of one monolayer Mn on W͑110͒ we conclude that ͑i͒ Mn is magnetic and exhibits a large magnetic moment of 3.32 B , ͑ii͒ the magnetic moments are arranged in a c(2ϫ2) antiferromagnetic order, ͑iii͒ the easy axis of the magnetization is in plane and points along the ͓11 0͔ direction, i.e., the direction along the long side of the ͑110͒ surface unit cell with a magnetocrystalline anisotropy energy of 1.3-1.5 meV, and ͑iv͒ the Mn-W interlayer distance is 2.14 Å. The calculated electronic structure of a Mn monolayer on W͑110͒ is compared with experimental scanning tunneling spectroscopy results. Several aspects are in nice agreement, but one cannot unambiguously deduce the magnetic structure from such a comparison. The proposed two-dimensional antiferromagnetic ground state of a Mn monolayer on W͑110͒ is directly verified by the use of spin-polarized STM ͑SP-STM͒ in the constant-current mode, and an in-plane easy magnetization axis could be confirmed using tips with different magnetization directions. We compare the measurements with theoretically determined SP-STM images calculated combining the Tersoff-Hamann model extended to SP-STM with the ab initio calculation, resulting in good agreement.

Utilizing electric fields to catalyze chemical reactions is not a new idea, but in enzymology it undergoes a renaissance, inspired by Warhsel's concept of electrostatic preorganization. According to this concept, the source of the immense catalytic efficiency of enzymes is the intramolecular electric field that permanently favors the reaction transition state over the reactants. Within enzyme design, computational efforts have fallen short in designing enzymes with natural-like efficacy. The outcome could improve if long-range electrostatics (often omitted in current protocols) would be optimized. Here, we highlight the major developments in methods for analyzing and designing electric fields generated by the protein scaffolds, in order to both better understand how natural enzymes function, and aid artificial enzyme design.

Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry, that countless variants, with or without catalysts, have been studied and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous, and can be used effectively for prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels-Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels-Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels-Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes in 𝑘 !"# . We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments.