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The viomycin biosynthesis enzyme VioC is a nonheme iron and α-ketoglutarate-dependent dioxygenase involved in the selective hydroxylation of l-arginine at the C 3 -position for antibiotics biosynthesis. Interestingly, experimental studies showed that using the substrate analogue, namely, l-homo-arginine, a mixture of products was obtained originating from C 3 -hydroxylation, C 4 -hydroxylation, and C 3 -C 4 -desaturation. To understand how the addition of one CH 2 group to a substrate can lead to such a dramatic change in selectivity and activity, we decided to perform a computational study using quantum mechanical (QM) cluster models. We set up a large active-site cluster model of 245 atoms that includes the oxidant with its first- and second-coordination sphere influences as well as the substrate binding pocket. The model was validated against experimental work from the literature on related enzymes and previous computational studies. Thereafter, possible pathways leading to products and byproducts were investigated for a model containing l-Arg and one for l-homo-Arg as substrate. The calculated free energies of activation predict product distributions that match the experimental observation and give a low-energy C 3 -hydroxylation pathway for l-Arg, while for l-homo-Arg, several barriers are found to be close in energy leading to a mixture of products. We then analyzed the origins of the differences in product distributions using thermochemical, valence bond, and electrostatic models. Our studies show that the C 3 -H and C 4 -H bond strengths of l-Arg and l-homo-Arg are similar; however, external perturbations from an induced electric field of the protein affect the relative C-H bond strengths of l-Arg dramatically and make the C 3 -H bond the weakest and guide the reaction to a selective C 3 -hydroxylation channel. Therefore, the charge distribution in the protein and the induced electric dipole field of the active site of VioC guides the l-Arg substrate activation to C 3 -hydroxylation and disfavors the C 4 -hydroxylation pathway, while this does not occur for l-homo-Arg. Tight substrate positioning and electrostatic perturbations from the second-coordination sphere residues in VioC also result in a slower overall reaction for l-Arg; however, they enable a high substrate selectivity. Our studies highlight the importance of the second-coordination sphere in proteins that position the substrate and oxidant, perturb charge distributions, and enable substrate selectivity.

Motivation: Evolution couples differences in ambient pH to biological function through protonatable groups, in particular, those that switch from buried to exposed and alter protonation state in doing so. We present a tool focusing on structure-based discovery and display of these groups., Results: Since prediction of buried group pKas is computationally intensive, solvent accessibility of ionizable groups is displayed, from which the user can iteratively select pKa calculation centers. Results are color-coded, with emphasis on buried groups. Utility is demonstrated with benchmarking against known pH sensing sites in influenza virus hemagglutinin and in variants of murine hepatitis virus, a coronavirus. A pair of histidine residues, which are conserved in coronavirus spike proteins, are predicted to be electrostatically frustrated at acidic pH in both pre- and post-fusion conformations. We suggest that an intermediate expanded conformation at endosomal pH could relax the frustration, allowing histidine protonation and facilitating conformational conversion of coronavirus spike protein., Availability and Implementation: This tool is available at http://www.protein-sol.manchester.ac.uk/pka/., (© The Author(s) 2020. Published by Oxford University Press.)

The structural stability and solubility of proteins in liquid therapeutic formulations is important, especially since new generations of therapeutics are designed for efficacy before consideration of stability. We introduce an electrostatic binding model to measure the net charge of proteins with bound ions in solution. The electrostatic potential on a protein surface is used to separately group together acidic and basic amino acids into patches, which are then iteratively bound with oppositely charged counterions. This model is aimed toward formulation chemists for initial screening of a range of conditions prior to lab-work. Computed results compare well with experimental zeta potential measurements from the literature covering a range of solution conditions. Importantly, the binding model reproduces the charge reversal phenomenon that is observed with polyvalent ion binding to proteins and its dependence on ion charge and concentration. Intriguingly, protein sequence can be used to give similarly good agreement with experiment as protein structure, interpreted as resulting from the close proximity of charged side chains on a protein surface. Further, application of the model to human proteins suggests that polyanion binding and overcharging, including charge reversal for cationic proteins, is a general feature. These results add to evidence that addition of polyanions to protein formulations could be a general mechanism for modulating solution stability.

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

Modeling charge interactions is important for understanding many aspects of biological structure and function, and continuum methods such as Finite Difference Poisson-Boltzmann (FDPB) are commonly employed. Calculations of pH-dependence have identified separate populations; surface groups that can be modeled with a simple Debye-Hückel (DH) model, and buried groups, with stronger resultant interactions that are dependent on detailed conformation. This observation led to the development of a combined FDPB and DH method for pK(a) prediction (termed FD/DH). This study reports application of this method to ionizable groups, including engineered buried charges, in staphylococcal nuclease. The data had been made available to interested research groups before publication of mutant structures and/or pK(a) values. Overall, FD/DH calculations perform as intended with low ΔpK(a) values for surface groups (RMSD between predicted and experimental pK(a) values of 0.74), and much larger ΔpK(a) values for the engineered internal groups, with RMSD = 1.64, where mutant structures were known and RMSD = 1.80, where they were modeled. The weaker resultant interactions of the surface groups are determined mostly by charge-charge interactions. For the buried groups, R(2) for correlation between predicted and measured ΔpK(a) values is 0.74, despite the high RMSDs. Charge-charge interactions are much less important, with the R(2) value for buried group ΔpK(a) values increasing to 0.80 when the term describing charge desolvation alone is used. Engineered charge burial delivers a relatively uniform, nonspecific effect, in terms of pK(a) . How the protein environment adapts in atomic detail to deliver this resultant effect is still an open question., (Copyright © 2011 Wiley-Liss, Inc.)

Background: The traditional picture of charged amino acids in globular proteins is that they are almost exclusively on the outside exposed to the solvent. Buried charges, when they do occur, are assumed to play an essential role in catalysis and ligand binding, or in stabilizing structure as, for instance, helix caps., Results: By analyzing the amount and distribution of buried charged surface and charges in proteins over a broad range of protein sizes, we show that buried charge is much more common than is generally believed. We also show that the amount of buried charge rises with protein size in a manner which differs from other types of surfaces, especially aromatic and polar uncharged surfaces. In large proteins such as hemocyanin, 35% of all charges are greater than 75% buried. Furthermore, at all sizes few charged groups are fully exposed. As an experimental test, we show that replacement of the buried D178 of muconate lactonizing enzyme by N stabilizes the enzyme by 4.2 degrees C without any change in crystallographic structure. In addition, free energy calculations of stability support the experimental results., Conclusions: Nature may use charge burial to reduce protein stability; not all buried charges are fully stabilized by a prearranged protein environment. Consistent with this view, thermophilic proteins often have less buried charge. Modifying the amount of buried charge at carefully chosen sites may thus provide a general route for changing the thermophilicity or psychrophilicity of proteins.