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Abstract The delicate interplay of covalent and non‐covalent interactions in proteins is inherently quantum mechanical and highly dynamic in nature. To directly interrogate the evolving nature of the electronic structure of proteins, we carry out 100‐ps‐scale ab initio molecular dynamics simulations of three representative small proteins with range‐separated hybrid density functional theory. We quantify the nature and length‐scale of the coupling of residue‐specific charge probability distributions in these proteins. While some nonpolar residues exhibit expectedly narrow charge distributions, most polar and charged residues exhibit broad, multimodal distributions. Even for nonpolar residues, we observe sequence‐specific deviations corresponding to charge accumulation ormore » depletion that would be challenging to capture in a fixed charge force field. We quantify the effect of residue‐residue interactions on charge distributions first with linear cross‐correlations. We then show how additional insight can be gained from evaluating the mutual information of charge distributions. We show that a significant number of residues couple most strongly with residues that are distant in both sequence and space over a range of secondary structures including α‐helical, β‐sheet, disulfide bridging, and lasso motifs. The mutual information analysis is necessary to capture coupling between some polar and charged residues that would be otherwise missed.« less

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal–organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of “traditional” computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganicmore » molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure–property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal–organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.« less

Large scale quantum mechanical simulation systematically reveals length scales over which electronically driven interactions occur at enzyme active sites.

Our work discusses the observation of eigenstates that embody large-amplitude, local-bending vibrational motion in acetylene by stimulated emission pumping spectroscopy via vibrational levels of the S1 state involving excitation in the non-totally symmetric bending modes. The N_b = 14 level, lying at 8971.69 cm^-1 (J = 0), is assigned on the basis of degeneracy due to dynamical symmetry breaking in the local-mode limit. The level pattern for the N_b = 16 level, lying at 10 218.9 cm^-1, is consistent with expectations for increased separation of ℓ = 0 and 2 vibrational angular momentum components. Increasingly poor agreement between our observationsmore » and the predicted positions of these levels highlights the failure of currently available normal mode effective Hamiltonian models to extrapolate to regions of the potential energy surface involving large-amplitude displacement along the acetylene ⇌ vinylidene isomerization coordinate.« less