We have performed a systematic computational study of the relative energies of possible protonation states of the FeMo cluster in nitrogenase in the E0-E4 states, i.e. the resting state and states with 1-4 electrons and protons added but before N2 binds. We use the combined quantum mechanics and molecular mechanics (QM/MM) approach, including the complete solvated heterotetrameric enzyme in the calculations. The QM system consisted of 112 atoms, i.e. the full FeMo cluster, as well all groups forming hydrogen bonds to it within 3.5 Å. It was treated with either the TPSS-D3 or B3LYP-D3 methods with the def2-SV(P) or def2-TZVPD basis sets. For each redox state, we calculated relative energies of at least 50 different possible positions for the proton, added to the most stable protonation state of the level with one electron less. We show quite conclusively that the resting E0 state is not protonated using quantum refinement and by comparing geometries to the crystal structure. The E1 state is protonated on S2B, in agreement with most previous computational studies. However, for the E2-E4 states, the two QM methods give diverging results, with relative energies that differ by over 300 kJ/mol for the most stable E4 states. TPSS favours hydride ions binding to the Fe ions. The first bridges Fe2 and Fe6, whereas the next two bind terminally to either Fe4, Fe5 or Fe6 with nearly equal energies. On the other hand, B3LYP disfavours hydride ions and instead suggests that 1-3 protons bind to the central carbide ion.

Nitrogenase is the only enzyme that can break the triple bond in N to form two molecules of ammonia. The enzyme has been thoroughly studied with both experimental and computational methods, but there is still no consensus regarding the atomic details of the reaction mechanism. In the most common form, the active site is a MoFeSC(homocitrate) cluster. The homocitrate ligand contains one alcohol and three carboxylate groups. In water solution, the triply deprotonated form dominates, but because the alcohol (and one of the carboxylate groups) coordinate to the Mo ion, this may change in the enzyme. We have performed a series of computational calculations with molecular dynamics (MD), quantum mechanical (QM) cluster, combined QM and molecular mechanics (QM/MM), QM/MM with Poisson-Boltzmann and surface area solvation, QM/MM thermodynamic cycle perturbations, and quantum refinement methods to settle the most probable protonation state of the homocitrate ligand in nitrogenase. The results quite conclusively point out a triply deprotonated form (net charge -3) with a proton shared between the alcohol and one of the carboxylate groups as the most stable at pH 7. Moreover, we have studied eight ionizable protein residues close to the active site with MD simulations and determined the most likely protonation states.

Understanding the driving forces underlying molecular recognition is of fundamental importance in chemistry and biology. The challenge is to unravel the binding thermodynamics into separate contributions and to interpret these in molecular terms. Entropic contributions to the free energy of binding are particularly difficult to assess in this regard. Here we pinpoint the molecular determinants underlying differences in ligand affinity to the carbohydrate recognition domain of galectin-3, using a combination of isothermal titration calorimetry, X-ray crystallography, NMR relaxation, and molecular dynamics simulations followed by conformational entropy and grid inhomogeneous solvation theory (GIST) analyses. Using a pair of diastereomeric ligands that have essentially identical chemical potential in the unbound state, we reduced the problem of dissecting the thermodynamics to a comparison of the two protein–ligand complexes. While the free energies of binding are nearly equal for the R and S diastereomers, greater differences are observed for the enthalpy and entropy, which consequently exhibit compensatory behavior, ΔΔ H°(R – S) = −5 ± 1 kJ/mol and −TΔΔ S°(R – S) = 3 ± 1 kJ/mol. NMR relaxation experiments and molecular dynamics simulations indicate that the protein in complex with the S-stereoisomer has greater conformational entropy than in the R-complex. GIST calculations reveal additional, but smaller, contributions from solvation entropy, again in favor of the S-complex. Thus, conformational entropy apparently dominates over solvation entropy in dictating the difference in the overall entropy of binding. This case highlights an interplay between conformational entropy and solvation entropy, pointing to both opportunities and challenges in drug design.

Particulate methane monooxygenase (pMMO) is one of the few enzymes that can activate methane. The metal content of this enzyme has been highly controversial, with suggestions of a dinuclear Fe site or mono-, di-, or trinuclear Cu sites. Crystal structures have shown a mono- or dinuclear Cu site, but the resolution was low and the geometry of the dinuclear site unusual. We have employed quantum refinement (crystallographic refinement enhanced with quantum-mechanical calculations) to improve the structure of the active site. We compared a number of different mono- and dinuclear geometries, in some cases enhanced with more protein ligands or one or two water molecules, to determine which structure fits the two sets of crystallographic raw data best. In all cases, the best results were obtained with mononuclear Cu sites, occasionally with an extra water molecule. Thus we conclude that there is no crystallographic support for a dinuclear Cu site in pMMO.

Prediction of protein stability changes caused by mutation is of major importance to protein engineering and for understanding protein misfolding diseases and protein evolution. The major limitation to these applications is the fact that different prediction methods vary substantially in terms of performance for specific proteins; i.e., performance is not transferable from one type of mutation or protein to another. In this study, we investigated the performance and transferability of eight widely used methods. We first constructed a new data set composed of 2647 mutations using strict selection criteria for the experimental data and then defined a variety of subdata sets that are unbiased with respect to various aspects such as mutation type, stabilization extent, structure type, and solvent exposure. Benchmarking the methods against these subdata sets enabled us to systematically investigate how data set biases affect predictor performance. In particular, we use a reduced amino acid alphabet to quantify the bias toward mutation type, which we identify as the major bias in current approaches. Our results show that all prediction methods exhibit large biases, stemming not from failures of the models applied but mostly from the selection biases of experimental data used for training or parametrization. Our identification of these biases and the construction of new mutation-type-balanced data should lead to the development of more balanced and transferable prediction methods in the future.

Molecular recognition is fundamental to biological signaling. A central question is how individual interactions between molecular moieties affect the thermodynamics of ligand binding to proteins and how these effects might propagate beyond the immediate neighborhood of the binding site. Here, we investigate this question by introducing minor changes in ligand structure and characterizing the effects of these on ligand affinity to the carbohydrate recognition domain of galectin-3, using a combination of isothermal titration calorimetry, X-ray crystallography, NMR relaxation, and computational approaches including molecular dynamics (MD) simulations and grid inhomogeneous solvation theory (GIST). We studied a congeneric series of ligands with a fluorophenyl-triazole moiety, where the fluorine substituent varies between the ortho , meta , and para positions (denoted O, M, and P). The M and P ligands have similar affinities, whereas the O ligand has 3-fold lower affinity, reflecting differences in binding enthalpy and entropy. The results reveal surprising differences in conformational and solvation entropy among the three complexes. NMR backbone order parameters show that the O-bound protein has reduced conformational entropy compared to the M and P complexes. By contrast, the bound ligand is more flexible in the O complex, as determined by 19 F NMR relaxation, ensemble-refined X-ray diffraction data, and MD simulations. Furthermore, GIST calculations indicate that the O - bound complex has less unfavorable solvation entropy compared to the other two complexes. Thus, the results indicate compensatory effects from ligand conformational entropy and water entropy, on the one hand, and protein conformational entropy, on the other hand. Taken together, these different contributions amount to entropy–entropy compensation among the system components involved in ligand binding to a target protein.

Summary Only a limited number of dominant resistance genes acting against plant viruses have been cloned, and further functional studies of these have been almost entirely limited to the resistance genes Rx against Potato virus X (PVX) and N against Tobacco mosaic virus (TMV). Recently, the cell‐to‐cell movement protein (NS M ) of Tomato spotted wilt virus (TSWV) has been identified as the avirulence determinant (Avr) of Sw‐5b‐mediated resistance, a dominant resistance gene which belongs to the class of SD‐CC‐NB‐LRR (Solanaceae domain‐coiled coil‐nucleotide‐binding‐leucine‐rich repeat, SD‐CNL) resistance genes. On transient expression of the NS M protein in tomato and transgenic Nicotiana benthamiana harbouring the Sw‐5b gene, a hypersensitive cell death response (HR) is triggered. Here, it is shown that high accumulation of the Sw‐5b protein in N. benthamiana leaves, achieved by co‐expression of the Sw‐5b protein with RNA silencing suppressors (RSSs), leads to auto‐activity in the absence of NS M . In a similar approach, Sw‐5a, the highest conserved paralogue of Sw‐5b from Solanum peruvianum , also triggered HR by auto‐activation, whereas the highest conserved orthologue from susceptible S. lycopersicum , named Sw‐5a S , did not. However, neither of the last two homologues was able to trigger an NS M ‐dependent HR. Truncated and mutated versions of these Sw‐5 proteins revealed that the NB‐ARC [nucleotide‐binding adaptor shared by Apaf‐1 (from humans), R proteins and CED‐4 (from nematodes)] domain is sufficient for the triggering of HR and seems to be suppressed by the SD‐CC domain. Furthermore, a single mutation was sufficient to restore auto‐activity within the NB‐ARC domain of Sw‐5a S . When the latter domain was fused to the Sw‐5b LRR domain, NS M ‐dependent HR triggering was regained, but not in the presence of its own Sw‐5a S LRR domain. Expression analysis in planta revealed a nucleocytoplasmic localization pattern of Sw‐5b, in which the SD‐CC domain seems to be required for nuclear translocation. Although the Sw‐5 N‐terminal CC domain, in contrast with Rx , contains an additional SD, most findings from this study support a conserved role of domains within NB‐LRR (NLR) proteins against plant viruses.

Accurate prediction of protein stability upon mutation enables rational engineering of new proteins and insights into protein evolution and monogenetic diseases caused by singlepoint amino acid substitutions. Many tools have been developed to this aim, ranging from energy-based models to machine-learning methods that use large amounts of experimental data. However, as the methods become more complex, the interpretation of the chemistry underlying the protein stability effects becomes obscure. It is thus of interest to identify the simplest prediction model that retains complete amino acid specific interpretation; for a given number of input descriptors, we expect such a model to be almost universal. In this study, we identify such a limiting model, SimBa, a simple multilinear regression model trained on a substitution-type-balanced experimental data set. The model accounts only for the solvent accessibility of the site, volume difference, and polarity difference caused by mutation. Our results show that this very simple and directly applicable model performs comparably to other much more complex, widely used protein stability prediction methods. This suggests that a hard limit of ∼1 kcal/mol numerical accuracy and an R ∼ 0.5 trend accuracy exists and that new features, such as account of unfolded states, water colocalization, and amino acid correlations, are required to improve accuracy to, e.g., 1/2 kcal/mol.