Protonation States of Homocitrate and Nearby Residues in Nitrogenase Studied by Computational Methods and Quantum Refinement

Nitrogenase is the only enzyme that can break the triple bond in N2 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 MoFe7S9C(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

Does the crystal structure of vanadium nitrogenase contain a reaction intermediate? Evidence from quantum refinement

Abstract: Recently, a crystal structure of V-nitrogenase was presented, showing that one of the µ2 sulphide ions in the active site (S2B) is replaced by a lighter atom, suggested to be NH or NH2, i.e. representing a reaction intermediate. Moreover, a sulphur atom is found 7 Å from the S2B site, suggested to represent a storage site for this ion when it is displaced. We have re-evaluated this structure with quantum refinement, i.e. standard crystallographic refinement in which the empirical restraints (employed to ensure that the final structure makes chemical sense) are replaced by more accurate quantum–mechanical calculations. This allows us to test various interpretations of the structure, employing quantum–mechanical calculations to predict the ideal structure and to use crystallographic measures like the real-space Z-score and electron-density difference maps to decide which structure fits the crystallographic raw data best. We show that the structure contains an OH−-bound state, rather than an N2-derived reaction intermediate. Moreover, the structure shows dual conformations in the active site with ~ 14% undissociated S2B ligand, but the storage site seems to be fully occupied, weakening the suggestion that it represents a storage site for the dissociated ligand. Graphic abstract: [Figure not available: see fulltext.

A base measure of precision for protein stability predictors: structural sensitivity.

BACKGROUND: Prediction of the change in fold stability (ΔΔG) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict ΔΔG. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods. RESULTS: We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity of ~ 0.6 to 0.8 kcal/mol, whereas machine-learning methods display much lower sensitivity (~ 0.1 kcal/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts. CONCLUSIONS: The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that ΔΔG values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure

Water structure in solution and crystal molecular dynamics simulations compared to protein crystal structures

The function of proteins is influenced not only by the atomic structure but also by the detailed structure of the solvent surrounding it. Computational studies of protein structure also critically depend on the water structure around the protein. Herein we compare the water structure obtained from molecular dynamics (MD) simulations of galectin-3 in complex with two ligands to crystallographic water molecules observed in the corresponding crystal structures. We computed MD trajectories both in a water box, which mimics a protein in solution, and in a crystallographic unit cell, which mimics a protein in a crystal. The calculations were compared to crystal structures obtained at both cryogenic and room temperature. Two types of analyses of the MD simulations were performed. First, the positions of the crystallographic water molecules were compared to peaks in the MD density after alignment of the protein in each snapshot. The results of this analysis indicate that all simulations reproduce the crystallographic water structure rather poorly. However, if we define the crystallographic water sites based on their distances to nearby protein atoms and follow these sites throughout the simulations, the MD simulations reproduce the crystallographic water sites much better. This shows that the failure of MD simulations to reproduce the water structure around proteins in crystal structures observed both in this and previous studies is caused by the problem of identifying water sites for a flexible and dynamic protein (traditionally done by overlaying the structures). Our local clustering approach solves the problem and shows that the MD simulations reasonably reproduce the water structure observed in crystals. Furthermore, analysis of the crystal MD simulations indicates a few water molecules that are close to unmodeled electron density peaks in the crystal structures, suggesting that crystal MD could be used as a complementary tool for identifying and modelling water in protein crystallography

Bridging the gap between computational chemistry and macromolecular crystallography

Knowledge of the atomic structure of biomolecules, such as proteins, is paramount to understanding their function and interactions in the human body. For example, knowledge of the atomic structure of a target protein is crucial for developing drugs that bind strongly to it and thus help cure diverse diseases.Macromolecular crystallography is the forefront method for determining the atomic structure of proteins, especially through X-ray diffraction experiments. However, the data obtained from these experiments are not the atomic structure but need to be processed and interpreted before arriving at the individual positions of atoms in a protein. This intepretation is done through computational techniques that share some of the algorithms and problems with computational chemistry.In this thesis, we use several methods that combine computational chemistry and macromolecular crystallography for the study of multiple important proteins. Crystallographic refinement combined with quantum mechanical calculations (quantum refinement) is used to improve the X-ray structures of three metalloenzymes. Furthermore, a quantum refinement procedure for neutron structures is developed and applied to two important enzymes. We also investigate how to use and improve the existing information on dynamics from crystallography experiments. To this end, we test whether conformational entropy can be calculated directly from B-factors. Additionally, ensemble refinement is used to explore ligand dynamics in the binding site of galectin-3 and reveals hidden conformations that were not apparent in traditional crystallographic refinement methods. Finally, we study the modeling of water molecules in protein X-ray and neutron crystal structures. We show that molecular dynamics simulations can reproduce crystal water molecules, if protein movements are correctly taken into account. Moreover, we have developed a method to automatically improve the orientation of water molecules in neutron structures

Protonation and Reduction of the FeMo Cluster in Nitrogenase Studied by Quantum Mechanics/Molecular Mechanics (QM/MM) Calculations

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 favors 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 disfavors hydride ions and instead suggests that 1-3 protons bind to the central carbide ion

Automated orientation of water molecules in neutron crystallographic structures of proteins

The structure and function of proteins are strongly affected by the surrounding solvent water, for example through hydrogen bonds and the hydrophobic effect. These interactions depend not only on the position, but also on the orientation, of the water molecules around the protein. Therefore, it is often vital to know the detailed orientations of the surrounding ordered water molecules. Such information can be obtained by neutron crystallography. However, it is tedious and time-consuming to determine the correct orientation of every water molecule in a structure (there are typically several hundred of them), which is presently performed by manual evaluation. Here, a method has been developed that reliably automates the orientation of a water molecules in a simple and relatively fast way. Firstly, a quantitative quality measure, the real-space correlation coefficient, was selected, together with a threshold that allows the identification of water molecules that are oriented. Secondly, the refinement procedure was optimized by varying the refinement method and parameters, thus finding settings that yielded the best results in terms of time and performance. It turned out to be favourable to employ only the neutron data and a fixed protein structure when reorienting the water molecules. Thirdly, a method has been developed that identifies and reorients inadequately oriented water molecules systematically and automatically. The method has been tested on three proteins, galectin-3C, rubredoxin and inorganic pyrophosphatase, and it is shown that it yields improved orientations of the water molecules for all three proteins in a shorter time than manual model building. It also led to an increased number of hydrogen bonds involving water molecules for all proteins