GROMOS++ Software for the Analysis of Biomolecular Simulation Trajectories

Stability Assessment in Aqueous and Organic Solvents of Metal-Organic Framework PCN 333 Nanoparticles through a Combination of Physicochemical Characterization and Computational Simulations

Mesoporous metal-organic frameworks (MOFs) have been recognized as powerful platforms for drug delivery, especially for biomolecules. Unfortunately, the application of MOFs is restricted due to their relatively poor stability in aqueous media, which is crucial for drug delivery applications. An exception is the porous coordination network (PCN)-series (e.g., PCN-333 and PCN-332), a series of MOFs with outstanding stability in aqueous media at the pH range from 3 to 9. In this study, we fabricate PCN-333 nanoparticles (nPCN) and investigate their stability in different solvents, including water, ethanol, and methanol. Surprisingly, the experimental characterizations in terms of X-ray diffraction, Brunauer-Emmett-Teller (BET), and scanning electron microscopy demonstrated that nPCN is not as stable in water as previously reported. Specifically, the crystalline structure of nPCN lost its typical octahedral shape and even decomposed into an irregular amorphous form when exposed to water for only 2 h, but not when ethanol and methanol were used. Meanwhile, the porosity of nPCN substantially diminished while being exposed to water, as demonstrated by the BET measurement. With the assistance of computational simulations, the mechanism behind the collapse of PCN-333 is illuminated. By molecular dynamics simulation and umbrella sampling, we elucidate that the degradation of PCN-333 occurs by hydrolysis, wherein polar solvent molecules initiate the attack and subsequent breakage of the metal-ligand reversible coordination bonds.

Exploring the Properties of Curved Lipid Membranes: Comparative Analysis of Atomistic and Coarse-Grained Force Fields

Curvature emerges as a fundamental membrane characteristic crucial for diverse biological processes, including vesicle formation, cell signaling, and membrane trafficking. Increasingly valuable insights into atomistic details governing curvature-dependent membrane properties are provided by computer simulations. Nevertheless, the underlying force field models are conventionally calibrated and tested in relation to experimentally derived parameters of planar bilayers, thereby leaving uncertainties concerning their consistency in reproducing curved lipid systems. In this study we compare the depiction of buckled phosphatidylcholine (POPC) and POPC-cholesterol membranes by four popular force field models. Aside from agreement with respect to general trends in curvature dependence of a number of parameters, we observe a few qualitative differences. Among the most prominent ones is the difference between atomistic and coarse grained force fields in their representation of relative compressibility of the polar headgroup region and hydrophobic lipid core. Through a number of downstream effects, this discrepancy can influence the way in which curvature modulates the behavior of membrane bound proteins depending on the adopted simulation model.

Caspase-Based Fusion Protein Technology: Substrate Cleavability Described by Computational Modeling and Simulation

The Caspase-based fusion protein technology (CASPON) allows for universal cleavage of fusion tags from proteins of interest to reconstitute the native N-terminus. While the CASPON enzyme has been optimized to be promiscuous against a diversity of N-terminal peptides, the cleavage efficacy for larger proteins can be surprisingly low. We develop an efficient means to rationalize and predict the cleavage efficiency based on a structural representation of the intrinsically disordered N-terminal peptides and their putative interactions with the CASPON enzyme. The number of favorably interacting N-terminal conformations shows a very good agreement with the experimentally observed cleavage efficiency, in agreement with a conformational selection model. The method relies on computationally cheap molecular dynamics simulations to efficiently generate a diverse collection of N-terminal conformations, followed by a simple fitting procedure into the CASPON enzyme. It can be readily used to assess the CASPON cleavability a priori.

Molecular Structure Refinement Based on Residual Dipolar Couplings: A Comparison of the Molecular Rotational-Sampling Method with the Alignment-Tensor Approach

In NMR experiments, residual dipolar couplings (RDCs) in a molecule can be measured by averaging the dipolar couplings (DCs) over the rotational motion of a molecule in an environment that induces a slight anisotropic orientation distribution of the molecule. Since the shape of the anisotropic distribution cannot be measured, it is standard practice to use a particular orientation distribution of the molecule with respect to the magnetic field, in the form of a so-called alignment tensor (AT), to calculate RDC-values for the molecule. Since the same alignment tensor is commonly used to calculate the different RDCs of a molecule, this approach rests on the assumption that the rotational motion of the molecule is decoupled from its internal motions and that the molecule is rigid. The validity of these two assumptions is investigated for a small, simple molecule, using a relatively rigid atomic interaction function or force field and a more flexible one. By simulating the molecule using an orientation-biasing force an anisotropic rotational distribution can be generated, for which RDCs can be obtained. Using these RDCs as target RDCs when applying one of the two approaches of structure refinement based on RDCs, it can be investigated how well the target RDCs are approximated in the RDC restraining and whether the corresponding nonuniform orientation distribution is reproduced. For the relatively rigid version of the molecule, the AT approach reproduces the target RDC-values, although the nonuniform orientation distribution of the angle θab,H between the vector r⃗ab connecting two atoms a and b in the molecule and the vector representing the direction of the magnetic field H⃗ as generated in the orientation-biasing simulation cannot be reproduced in the AT RDC-restraining simulation. For the relatively flexible version of the molecule, the AT approach fails to reproduce both the target RDC values and the nonuniform orientation distribution. For biomolecules with flexible parts, the application of the AT approach is thus not recommended. Instead, a method based on sampling of the rotational and internal degrees of freedom of the molecule should be applied in molecular structure determination or refinement based on measured RDCs.

On the Validation of Protein Force Fields Based on Structural Criteria

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, J-coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small. Furthermore, improvements in agreement in one metric were often offset by loss of agreement in another. The work establishes a framework and test set against which protein force fields can be validated. It also highlights the danger of inferring the relative quality of a given force field based on a small range of structural properties or small number of proteins.

Engineering A-type Dye-Decolorizing Peroxidases by Modification of a Conserved Glutamate Residue

Dye-decolorizing peroxidases (DyPs) are recently identified microbial enzymes that have been used in several Biotechnology applications from wastewater treatment to lignin valorization. However, their properties and mechanism of action still have many open questions. Their heme-containing active site is buried by three conserved flexible loops with a putative role in modulating substrate access and enzyme catalysis. Here, we investigated the role of a conserved glutamate residue in stabilizing interactions in loop 2 of A-type DyPs. First, we did site saturation mutagenesis of this residue, replacing it with all possible amino acids in bacterial DyPs from Bacillus subtilis (BsDyP) and from Kitasatospora aureofaciens (KaDyP1), the latter being characterized here for the first time. We screened the resulting libraries of variants for activity towards ABTS and identified variants with increased catalytic efficiency. The selected variants were purified and characterized for activity and stability. We furthermore used Molecular Dynamics simulations to rationalize the increased catalytic efficiency and found that the main reason is the electron channeling becoming easier from surface-exposed tryptophans. Based on our findings, we also propose that this glutamate could work as a pH switch in the wild-type enzyme, preventing intracellular damage.

Comprehensive Characterization of LAT1 Cholesterol-Binding Sites

The human L-type amino acid transporter 1 (LAT1; SLC7A5), is an amino acid exchanger protein, primarily found in the blood-brain barrier, placenta, and testis, where it plays a key role in amino acid homeostasis. Cholesterol is an essential lipid that has been highlighted to play a role in regulating the activity of membrane transporters, such as LAT1, yet little is known about the molecular mechanisms driving this phenomenon. Here we perform a comprehensive computational analysis to investigate cholesterol's role in LAT1 structure and function, focusing on four cholesterol-binding sites (CHOL1-4) identified in a recent LAT1-apo inward-open conformation cryo-EM structure. Through a series of independent molecular dynamics (MD) simulations, molecular docking, MM/GBSA free energy calculations, and other analysis tools, we explored the interactions between LAT1 and cholesterol. Our findings suggest that CHOL3 forms the most stable and favorable interactions with LAT1. Principal component analysis (PCA) and center of mass (COM) distance assessments show that CHOL3 binding stabilizes the inward-open state of LAT1 by preserving the spatial arrangement of the hash and bundle domains. Additionally, we propose an alternative cholesterol-binding site for originally assigned CHOL1. Overall, this study improves the understanding of cholesterol's modulatory effect on LAT1 and proposes candidate sites for the discovery of future allosteric ligands with rational design.

The Effect of Microwaves on Protein Structure: Molecular Dynamics Approach

The impact of microwave (MW) irradiation on protein folding, potentially inciting misfolding, was investigated by employing molecular dynamics (MD) simulations. Twenty-nine proteins were subjected to MD simulations under equilibrium (300 K) and MW conditions, where the rotational temperature was elevated to 700 K. The utilized replacement model captures the microwave effects of δ- and γ-relaxation processes (frequency range of ∼300 MHz to ∼20 GHz). The results disclosed that MW heating incited a shift toward more compact protein conformations, as indicated by decreased root-mean-square deviations, root-mean-square fluctuations, head-to-tail distances, and radii of gyration. This compaction was attributed to the intensification of intramolecular electrostatic interactions and hydrogen bonds within the protein caused by MW-destabilized hydrogen bonds between the protein and solvent. The solvent-accessible surface area (SASA), particularly that of polar amino-acid residues, shrank under MW conditions, corresponding to a reduced polarity of the water solvent. However, MW irradiation produced no significant alterations in protein secondary structures; hence, MW heating was observed to primarily affect the protein tertiary structures.

Understanding the riddle of amine oxidase flavoenzyme reactivity on the stereoisomers of N-methyl-dopa and N-methyl-tyrosine

Enzymes are usually stereospecific against chiral substrates, which is commonly accepted for the amine oxidase family of enzymes as well. However, the FsqB (fumisoquin biosynthesis gene B) enzyme that belongs to the family of sarcosine oxidase and oxidizes L-N-methyl-amino acids, shows surprising activity for both enantiomers of N-methyl-dopa. The aim of this study is to understand the mechanism behind this behavior. Primary docking experiments showed that tyrosine and aspartate residues (121 and 315 respectively) are located on the ceiling of the active site of FsqB and may play a role in fixing the N-methyl-dopa via its catechol moiety and allowing both stereoisomers of this substrate to be in close proximity of the N5 atom of the isoalloxazine ring of the cofactor. Three experimental approaches were used to prove this hypothesis which are: (1) studying the oxidative ability of the variants Y121F and D315A on N-methyl-dopa substrates in comparison with N-methyl-tyrosine substrates; (2) studying the FsqB WT and variants catalyzed biotransformation via high-performance liquid chromatography (HPLC); (3) molecular dynamics simulations to characterize the underlying mechanisms of the molecular recognition. First, we found that the chemical characteristics of the catechol moiety of N-methyl-dopa are important to explain the differences between N-methyl-dopa and N-methyl-tyrosine. Furthermore, we found that Y121 and D315 are specific in FsqB and not found in the model enzyme sarcosine oxidase. The on-bench and theoretical mutagenesis studies show that Y121 residue has a major role in fixing the N-methyl-dopa substrates close to the N5 atom of the isoalloxazine ring of the cofactor. Simultaneously, D315 has a supportive role in this mechanism. Jointly, the experimental and theoretical approaches help to solve the riddle of FsqB amine oxidase substrate specificity.

Amino Acid Residues Controlling Domain Interaction and Interdomain Electron Transfer in Cellobiose Dehydrogenase

The function of cellobiose dehydrogenase (CDH) in biosensors, biofuel cells, and as a physiological redox partner of lytic polysaccharide monooxygenase (LPMO) is based on its role as an electron donor. Before donating electrons to LPMO or electrodes, an interdomain electron transfer from the catalytic FAD-containing dehydrogenase domain to the electron shuttling cytochrome domain of CDH is required. This study investigates the role of two crucial amino acids located at the dehydrogenase domain on domain interaction and interdomain electron transfer by structure-based engineering. The electron transfer kinetics of wild-type Myriococcum thermophilum CDH and its variants M309A, R698S, and M309A/R698S were analyzed by stopped-flow spectrophotometry and structural effects were studied by small-angle X-ray scattering. The data show that R698 is essential to pull the cytochrome domain close to the dehydrogenase domain and orient the heme propionate group towards the FAD, while M309 is an integral part of the electron transfer pathway - its mutation reducing the interdomain electron transfer 10-fold. Structural models and molecular dynamics simulations pinpoint the action of these two residues on the domain interaction and interdomain electron transfer.

Flexible Gaussian Accelerated Molecular Dynamics to Enhance Biological Sampling

Molecular dynamics simulations often struggle to obtain sufficient sampling to study complex molecular events due to high energy barriers separating the minima of interest. Multiple enhanced sampling techniques have been developed and improved over the years to tackle this issue. Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that works by adding a biasing potential, lifting the energy landscape up, and decreasing the height of its barriers. GaMD allows one to increase the sampling of events of interest without the need of a priori knowledge of the system or the relevant coordinates. All required acceleration parameters can be obtained from a previous search run. Upon its development, several improvements for the methodology have been proposed, among them selective GaMD in which the boosting potential is selectively applied to the region of interest. There are currently four selective GaMD methods that have shown promising results. However, all of these methods are constrained on the number, location, and scenarios in which this selective boosting potential can be applied to ligands, peptides, or protein-protein interactions. In this work, we showcase a GROMOS implementation of the GaMD methodology with a fully flexible selective GaMD approach that allows the user to define, in a straightforward way, multiple boosting potentials for as many regions as desired. We show and analyze the advantages of this flexible selective approach on two previously used test systems, the alanine dipeptide and the chignolin peptide, and extend these examples to study its applicability and potential to study conformational changes of glycans and glycosylated proteins.

Accelerated Enveloping Distribution Sampling to Probe the Presence of Water Molecules

Determining the presence of water molecules at protein-ligand interfaces is still a challenging task in free-energy calculations. The inappropriate placement of water molecules results in the stabilization of wrong conformational orientations of the ligand. With classical alchemical perturbation methods, such as thermodynamic integration (TI), it is essential to know the amount of water molecules in the active site of the respective ligands. However, the resolution of the crystal structure and the correct assignment of the electron density do not always lead to a clear placement of water molecules. In this work, we apply the one-step perturbation method named accelerated enveloping distribution sampling (AEDS) to determine the presence of water molecules in the active site by probing them in a fast and straightforward way. Based on these results, we combined the AEDS method with standard TI to calculate accurate binding free energies in the presence of buried water molecules. The main idea is to perturb the water molecules with AEDS such that they are allowed to alternate between regular water molecules and non-interacting dummy particles while treating the ligand with TI over an alchemical pathway. We demonstrate the use of AEDS to probe the presence of water molecules for six different test systems. For one of these, previous calculations showed difficulties to reproduce the experimental binding free energies, and here, we use the combined TI-AEDS approach to tackle these issues.

Unraveling the Aquaporin-3 Inhibitory Effect of Rottlerin by Experimental and Computational Approaches

The natural polyphenolic compound Rottlerin (RoT) showed anticancer properties in a variety of human cancers through the inhibition of several target molecules implicated in tumorigenesis, revealing its potential as an anticancer agent. Aquaporins (AQPs) are found overexpressed in different types of cancers and have recently emerged as promising pharmacological targets. Increasing evidence suggests that the water/glycerol channel aquaporin-3 (AQP3) plays a key role in cancer and metastasis. Here, we report the ability of RoT to inhibit human AQP3 activity with an IC₅₀ in the micromolar range (22.8 ± 5.82 µM for water and 6.7 ± 2.97 µM for glycerol permeability inhibition). Moreover, we have used molecular docking and molecular dynamics simulations to understand the structural determinants of RoT that explain its ability to inhibit AQP3. Our results show that RoT blocks AQP3-glycerol permeation by establishing strong and stable interactions at the extracellular region of AQP3 pores interacting with residues essential for glycerol permeation. Altogether, our multidisciplinary approach unveiled RoT as an anticancer drug against tumors where AQP3 is highly expressed providing new information to aquaporin research that may boost future drug design.

Accelerated Enveloping Distribution Sampling (AEDS) Allows for Efficient Sampling of Orthogonal Degrees of Freedom

One of the most challenging aspects in the molecular simulation of proteins is the study of slowly relaxing processes, such as loop rearrangements or ligands that adopt different conformations in the binding site. State-of-the-art methods used to calculate binding free energies rely on performing several short simulations (lambda steps), in which the ligand is slowly transformed into the endstates of interest. This makes capturing the slowly relaxing processes even more difficult, as they would need to be observed in all of the lambda steps. One attractive alternative is the use of a reference state capable of sampling all of the endstates of interest in a single simulation. However, the energy barriers between the states can be too high to overcome, thus hindering the sampling of all of the relevant conformations. Accelerated enveloping distribution sampling (AEDS) is a recently developed reference state technique that circumvents the high-energy-barrier challenge by adding a boosting potential that flattens the energy landscape without distorting the energy minima. In the present work, we apply AEDS to the well-studied benchmark system T4 lysozyme L99A. The T4 lysozyme L99A mutant contains a hydrophobic pocket in which there is a valine (valine 111), whose conformation influences the binding efficiencies of the different ligands. Incorrectly sampling the dihedral angle can lead to errors in predicted binding free energies of up to 16 kJ mol^-1. This protein constitutes an ideal scenario to showcase the advantages and challenges when using AEDS in the presence of slow relaxing processes. We show that AEDS is able to successfully sample the relevant degrees of freedom, providing accurate binding free energies, without the need of previous information of the system in the form of collective variables. Additionally, we showcase the capabilities of AEDS to efficiently screen a set of ligands. These results represent a promising first step toward the development of free-energy methods that can respond to more intricate molecular events.

Backbone N-Amination Promotes the Folding of β-Hairpin Peptides via a Network of Hydrogen Bonds

Molecular dynamics (MD) simulations have been used to characterize the effects of backbone N-amination of residues in a model β-hairpin peptide. This modification is of considerable interest as N-aminated peptides have been shown to inhibit amyloid-type aggregation. Six derivatives of the β-hairpin peptide, which contain one, two, or four N-aminated residues, have been studied. For each peptide 100 ns MD simulations starting from the folded β-hairpin structure were performed. The effects of the N-amination prove to be very sequence dependent. N-Amination of a residue involved in interstrand hydrogen bonding (Val3) leads to unfolding of the β-hairpin, whereas N-amination of a residue toward the C-terminus (Leu11) gives fraying at the termini of the peptide. In the other derivatives the peptide remains folded, with increasing levels of N-amination reducing the right-handed twist of the β-hairpin and favoring population of a type II' rather than a type I' β-turn. MD simulations (100 ns) have also been run for each peptide starting from an unfolded extended chain. Here, the peptide with four N-aminated residues shows the most folding into the β-hairpin (34%). Analysis of the simulations shows that N-amination favors the population of β (φ, ψ) conformations by the preceding residue due to, at least in part, a network of weak NH₂(i)-CO(i) and NH₂(i)-CO(i-2) hydrogen bonds. It also leads to a reduction of misfolding because of changes in the hydrogen-bonding potential. Both of these features help funnel the peptide to the folded β-hairpin structure. The conformational insights provided through this work give a firm foundation for the design of N-aminated peptide inhibitors for modulating protein-protein interactions and aggregation.

Leveraging the Sampling Efficiency of RE-EDS in OpenMM Using a Shifted Reaction-Field With an Atom-Based Cutoff

Replica-exchange enveloping distribution sampling (RE-EDS) is a pathway-independent multistate free-energy method, currently implemented in the GROMOS software package for molecular dynamics (MD) simulations. It has a high intrinsic sampling efficiency as the interactions between the unperturbed particles have to be calculated only once for multiple end-states. As a result, RE-EDS is an attractive method for the calculation of relative solvation and binding free energies. An essential requirement for reaching this high efficiency is the separability of the nonbonded interactions into solute-solute, solute-environment, and environment-environment contributions. Such a partitioning is trivial when using a Coulomb term with a reaction-field (RF) correction to model the electrostatic interactions, but not when using lattice- sum schemes. To avoid cutoff artifacts, the RF correction is typically used in combination with a charge-group based cutoff, which is not supported by most small-molecule force fields and other MD engines. To address this issue, we investigate the combination of RE-EDS simulations with a recently introduced RF scheme including a shifting function that enables the rigorous calculation of RF electrostatics with atom-based cutoffs. The resulting approach is validated by calculating solvation free energies with the generalized AMBER force field (GAFF) in water and chloroform using both the GROMOS software package and a proof-of-concept implementation in OpenMM.

Combined fragment-based machine learning force field with classical force field and its application in the NMR calculations of macromolecules in solutions

We have developed a combined fragment-based machine learning (ML) force field and molecular mechanics (MM) force field for simulating the structures of macromolecules in solutions, and then compute its NMR chemical shifts with the generalized energy-based fragmentation (GEBF) approach at the level of density functional theory (DFT). In this work, we first construct Gaussian approximation potential based on GEBF subsystems of macromolecules for MD simulations and then a GEBF-based neural network (GEBF-NN) with deep potential model for the studied macromolecule. Then, we develop a GEBF-NN/MM force field for macromolecules in solutions by combining the GEBF-NN force field for the solute molecule and ff14SB force field for solvent molecules. Using the GEBF-NN/MM MD simulation to generate snapshot structures of solute/solvent clusters, we then perform the NMR calculations with the GEBF approach at the DFT level to calculate NMR chemical shifts of the solute molecule. Taking a heptamer of oligopyridine-dicarboxamides in chloroform solution as an example, our results show that the GEBF-NN force field is quite accurate for this heptamer by comparing with the reference DFT results. For this heptamer in chloroform solution, both the GEBF-NN/MM and classical MD simulations could lead to helical structures from the same initial extended structure. The GEBF-DFT NMR results indicate that the GEBF-NN/MM force field could lead to more accurate NMR chemical shifts on hydrogen atoms by comparing with the experimental NMR results. Therefore, the GEBF-NN/MM force field could be employed for predicting more accurate dynamical behaviors than the classical force field for complex systems in solutions.

Directional-path modification strategy enhances PET hydrolase catalysis of plastic degradation

Poly (ethylene terephthalate) (PET) is a widely used type of general plastic that produces a significant amount of waste due to its non-degradable properties. We propose a novel directional-path modification (DPM) strategy, involving positive charge amino acid introduction and binding groove remodeling, and apply it to Thermobifida fusca cutinase to enhance PET degradation. The highest value of PET degradation (90%) was achieved in variant 4Mz (H184S/Q92G/F209I/I213K), exhibiting values almost 30-fold that of the wild-type. We employed molecular docking, molecular dynamics simulations, and QM/MM MD for the degradation process of PET, accompanied by acylation and deacylation. We found that the distance of nucleophilic attack was reduced from about 4.6 Å in the wild type to 3.8 Å in 4Mz, and the free energy barrier of 4Mz dropped from 14.3 kcal/mol to 7.1 kcal/mol at the acylation which was the rate-limiting step. Subsequently, the high efficiency and universality of the DPM strategy were successfully demonstrated in LCC, Est119, and BhrPETase enhancing the degradation activity of PET. Finally, the highest degradation rate of the pretreated commercial plastic bottles had reached to 73%. The present study provides insight into the molecular binding mechanism of PET into the PET hydrolases structure and proposes a novel DPM strategy that will be useful for the engineering of more efficient enzymes for PET degradation.

Replica-Exchange Enveloping Distribution Sampling Using Generalized AMBER Force-Field Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules

Free-energy differences between pairs of end-states can be estimated based on molecular dynamics (MD) simulations using standard pathway-dependent methods such as thermodynamic integration (TI), free-energy perturbation, or Bennett’s acceptance ratio. Replica-exchange enveloping distribution sampling (RE-EDS), on the other hand, allows for the sampling of multiple end-states in a single simulation without the specification of any pathways. In this work, we use the RE-EDS method as implemented in GROMOS together with generalized AMBER force-field (GAFF) topologies, converted to a GROMOS-compatible format with a newly developed GROMOS++ program amber2gromos, to compute relative hydration free energies for a series of benzene derivatives. The results obtained with RE-EDS are compared to the experimental data as well as calculated values from the literature. In addition, the estimated free-energy differences in water and in vacuum are compared to values from TI calculations carried out with GROMACS. The hydration free energies obtained using RE-EDS for multiple molecules are found to be in good agreement with both the experimental data and the results calculated using other free-energy methods. While all considered free-energy methods delivered accurate results, the RE-EDS calculations required the least amount of total simulation time. This work serves as a validation for the use of GAFF topologies with the GROMOS simulation package and the RE-EDS approach. Furthermore, the performance of RE-EDS for a large set of 28 end-states is assessed with promising results.

Improving the Potential of Mean Force and Nonequilibrium Pulling Simulations by Simultaneous Alchemical Modifications

We present an approach combining alchemical modifications and physical-pathway methods to calculate absolute binding free energies. The employed physical-pathway method is either a stratified umbrella sampling to calculate a potential of mean force or nonequilibrium pulling. We devised two basic approaches: the simultaneous approach (S-approach), where, along the physical unbinding pathway, an alchemical transformation of ligand-protein interactions is installed and deinstalled, and the prior-plus-simultaneous approach (PPS-approach), where, prior to the physical-pathway simulation, an alchemical transformation of ligand-protein interactions is installed in the binding site and deinstalled during the physical-pathway simulation. Using a mutant of T4 lysozyme with a benzene ligand as an example, we show that installation and deinstallation of soft-core interactions concurrent with physical ligand unbinding (S-approach) allow successful potential of mean force calculations and nonequilibrium pulling simulations despite the problems posed by the occluded nature of the lysozyme binding pocket. Good agreement between the potential of the mean-force-based S-approach and double decoupling simulations as well as a remarkable efficiency and accuracy of the nonequilibrium-pulling-based S-approach is found. The latter turned out to be more compute-efficient than the potential of mean force calculation by approximately 70%. Furthermore, we illustrate the merits of reducing ligand-protein interactions prior to potential of mean force calculations using the murine double minute homologue protein MDM2 with a p53-derived peptide ligand (PPS-approach). Here, the problem of breaking strong interactions in the binding pocket is transferred to a prior alchemical transformation that reduces the free-energy barrier between the bound and unbound state in the potential of mean force. Besides, disentangling physical ligand displacement from the deinstallation of ligand-protein interactions was seen to allow a more uniform sampling of distance histograms in the umbrella sampling. In the future, physical ligand unbinding combined with simultaneous alchemical modifications may prove useful in the calculation of protein-protein binding free energies, where sampling problems posed by multiple, possibly sticky interactions and potential steric clashes can thus be reduced.

A Method to Derive Structural Information on Molecules from Residual Dipolar Coupling NMR Data

A method for structure refinement of molecules based on residual dipolar coupling (RDC) data is proposed. It calculates RDC values using rotational and molecule-internal configurational sampling instead of the common refinement procedure that is based on the approximation of the nonuniform rotational distribution of the molecule by a single alignment tensor representing the average nonuniformity of this distribution. Using rotational sampling, as is occurring in the experiment leading to observable RDCs, the method stays close to the experiment. It avoids the use of an alignment tensor and thus the assumption that the overall rotation of the molecule is decoupled from its internal motions and that the molecule be rigid. Two simple molecules, two-united-atomic ethane and a cyclooctane molecule with eight side chains, containing 24 united atoms, serve as the so-called "toy model" test systems. The method demonstrates the influence of molecular flexibility and force-field deficiencies on the outcome of structure refinement based on RDCs. For a molecule of a given size (number of atoms N_at), there must be a sufficiently large number N_RDC of measured RDC values available to allow the restraining forces to bias the overall orientation distribution of the molecule. If the ratio N_RDC/N_at gets too small, the RDC-restraining forces will either not be strong enough to change the overall rotational direction of the molecule such that the target RDC values are approximated well or will be so strong that they induce a local deformation of the molecule. In the latter case, the size or inertia of the molecule hinders a restraining-induced overall rotation and the internal structure of the molecule is not strong enough to avoid local deformation due to the restraining forces.

Molecular dynamics simulation or structure refinement of proteins: are solvent molecules required? A case study using hen lysozyme

In protein simulation or structure refinement based on values of observable quantities measured in (aqueous) solution, solvent (water) molecules may be explicitly treated, omitted, or represented by a potential of mean-solvation-force term, depending on protein coordinates only, in the force field used. These three approaches are compared for hen egg white lysozyme (HEWL). This 129-residue non-spherical protein contains a variety of secondary-structure elements, and ample experimental data are available: 1630 atom–atom Nuclear Overhauser Enhancement (NOE) upper distance bounds, 213 ³ J-couplings and 200 S² order parameters. These data are used to compare the performance of the three approaches. It is found that a molecular dynamics (MD) simulation in explicit water approximates the experimental data much better than stochastic dynamics (SD) simulation in vacuo without or with a solvent-accessible-surface-area (SASA) implicit-solvation term added to the force field. This is due to the missing energetic and entropic contributions and hydrogen-bonding capacities of the water molecules and the missing dielectric screening effect of this high-permittivity solvent. Omission of explicit water molecules leads to compaction of the protein, an increased internal strain, distortion of exposed loop and turn regions and excessive intra-protein hydrogen bonding. As a consequence, the conformation and dynamics of groups on the surface of the protein, which may play a key role in protein–protein interactions or ligand or substrate binding, may be incorrectly modelled. It is thus recommended to include water molecules explicitly in structure refinement of proteins in aqueous solution based on nuclear magnetic resonance (NMR) or other experimentally measured data.

On the effects of induced polarizability at the water-graphene interface via classical charge-on-spring models

Molecular models of the water-graphene interaction are essential to describe graphene in condensed phases. Different challenges are associated with the generation of these models, in particular π-π and dispersion interactions; thus quantum and classical models have been developed and due to the numerical efficiency of the latter, they have been extensively employed. In this work, we have systematically studied, via molecular dynamics, two polarizable graphene models, denominated CCCP and CCCPD, employing the charge-on-spring model of the GROMOS forcefield, both being compatible with the polarizable water models COS/G2 and COS/D2, respectively. These models were compared with non-polarizable graphene and SPC water models. We focused the study on the water-graphene interface in two distinct systems and under the influence of an electric field: one composed of graphene immersed in water and the other composed of graphene with a water droplet above it. In the former, the orientation of water close to the graphene layer is affected by polarizable graphene in comparison to non-polarizable graphene. This effect is emphasised when an electric field is applied. In the latter, carbon polarizability reduced water contact angles, but graphene retained its hydrophobicity and the computed angles are within the experimental data. Given the significant extra computational cost, the use of polarizable models instead of the traditional fixed-charged approach for the graphene-water interaction may be justified when polarizability effects are relevant, for example, when applying relatively strong fields or in very anisotropic systems, such as the vacuum-bulk interface, as these models are more responsive to such conditions.

Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.

RestraintMaker: a graph-based approach to select distance restraints in free-energy calculations with dual topology

The calculation of relative binding free energies (RBFE) involves the choice of the end-state/system representation, of a sampling approach, and of a free-energy estimator. System representations are usually termed "single topology" or "dual topology". As the terminology is often used ambiguously in the literature, a systematic categorization of the system representations is proposed here. In the dual-topology approach, the molecules are simulated as separate molecules. Such an approach is relatively easy to automate for high-throughput RBFE calculations compared to the single-topology approach. Distance restraints are commonly applied to prevent the molecules from drifting apart, thereby improving the sampling efficiency. In this study, we introduce the program RestraintMaker, which relies on a greedy algorithm to find (locally) optimal distance restraints between pairs of atoms based on geometric measures. The algorithm is further extended for multi-state methods such as enveloping distribution sampling (EDS) or multi-site [Formula: see text]-dynamics. The performance of RestraintMaker is demonstrated for toy models and for the calculation of relative hydration free energies. The Python program can be used in script form or through an interactive GUI within PyMol. The selected distance restraints can be written out in GROMOS or GROMACS file formats. Additionally, the program provides a human-readable JSON format that can easily be parsed and processed further. The code of RestraintMaker is freely available on GitHub https://github.com/rinikerlab/restraintmaker.

A cytochrome b-glucose dehydrogenase chimeric enzyme capable of direct electron transfer

The development of third generation biosensors depends on the availability of direct electron transfer (DET) capable enzymes. A successful strategy is to fuse a cytochrome domain to an enzyme to fulfil the function of a built-in redox mediator between the catalytic center and the electrode. In this study, we fused the cytochrome domain of Neurospora crassa CDH IIA (NcCYT) N-terminally to glucose dehydrogenase from Glomerella cingulata (GcGDH) to generate the chimeric enzyme NcCYT-GcGDH in a large amount for further studies. Heterologous expression in P. pastoris and chromatographic purification resulted in 1.8 g of homogeneous chimeric enzyme. Biochemical and electrochemical characterization confirmed that the chimeric enzyme is catalytically active, able to perform interdomain electron transfer (IET) and direct electron transfer (DET) via the fused cytochrome domain. The midpoint redox potential of the fused b-type cytochrome is 91 mV vs. SHE at pH 6.5 and the specific current obtained on a porous graphite electrode is 2.3 μA cm^-2. The high current obtained on this simple, unmodified electrode at a rather low redox potential is a promising starting point for further optimization. The high yield of NcCYT-GcGDH and its high specific activity supports the application of the chimeric enzyme in bioelectrocatalytic applications.

Relative free-energy calculations for scaffold hopping-type transformations with an automated RE-EDS sampling procedure

The calculation of relative free-energy differences between different compounds plays an important role in drug design to identify potent binders for a given protein target. Most rigorous methods based on molecular dynamics simulations estimate the free-energy difference between pairs of ligands. Thus, the comparison of multiple ligands requires the construction of a “state graph”, in which the compounds are connected by alchemical transformations. The computational cost can be optimized by reducing the state graph to a minimal set of transformations. However, this may require individual adaptation of the sampling strategy if a transformation process does not converge in a given simulation time. In contrast, path-free methods like replica-exchange enveloping distribution sampling (RE-EDS) allow the sampling of multiple states within a single simulation without the pre-definition of alchemical transition paths. To optimize sampling and convergence, a set of RE-EDS parameters needs to be estimated in a pre-processing step. Here, we present an automated procedure for this step that determines all required parameters, improving the robustness and ease of use of the methodology. To illustrate the performance, the relative binding free energies are calculated for a series of checkpoint kinase 1 inhibitors containing challenging transformations in ring size, opening/closing, and extension, which reflect changes observed in scaffold hopping. The simulation of such transformations with RE-EDS can be conducted with conventional force fields and, in particular, without soft bond-stretching terms.

Structure-guided glyco-engineering of ACE2 for improved potency as soluble SARS-CoV-2 decoy receptor

Infection and viral entry of SARS-CoV-2 crucially depends on the binding of its Spike protein to angiotensin converting enzyme 2 (ACE2) presented on host cells. Glycosylation of both proteins is critical for this interaction. Recombinant soluble human ACE2 can neutralize SARS-CoV-2 and is currently undergoing clinical tests for the treatment of COVID-19. We used 3D structural models and molecular dynamics simulations to define the ACE2 N-glycans that critically influence Spike-ACE2 complex formation. Engineering of ACE2 N-glycosylation by site-directed mutagenesis or glycosidase treatment resulted in enhanced binding affinities and improved virus neutralization without notable deleterious effects on the structural stability and catalytic activity of the protein. Importantly, simultaneous removal of all accessible N-glycans from recombinant soluble human ACE2 yields a superior SARS-CoV-2 decoy receptor with promise as effective treatment for COVID-19 patients.

Building quantum mechanics quality force fields of proteins with the generalized energy-based fragmentation approach and machine learning

We combined our generalized energy-based fragmentation (GEBF) approach and machine learning (ML) technique to construct quantum mechanics (QM) quality force fields for proteins. In our scheme, the training sets for a protein are only constructed from its small subsystems, which capture all short-range interactions in the target system. The energy of a given protein is expressed as the summation of atomic contributions from QM calculations of various subsystems, corrected by long-range Coulomb and van der Waals interactions. With the Gaussian approximation potential (GAP) method, our protocol can automatically generate training sets with high efficiency. To facilitate the construction of training sets for proteins, we store all trained subsystem data in a library. If subsystems in the library are detected in a new protein, corresponding datasets can be directly reused as a part of the training set on this new protein. With two polypeptides, 4ZNN and 1XQ8 segment, as examples, the energies and forces predicted by GEBF-GAP are in good agreement with those from conventional QM calculations, and dihedral angle distributions from GEBF-GAP molecular dynamics (MD) simulations can also well reproduce those from ab initio MD simulations. In addition, with the training set generated from GEBF-GAP, we also demonstrate that GEBF-ML force fields constructed by neural network (NN) methods can also show QM quality. Therefore, the present work provides an efficient and systematic way to build QM quality force fields for biological systems.

PROFICS: A bacterial selection system for directed evolution of proteases

Proteases serve as important tools in biotechnology and as valuable drugs or drug targets. Efficient protein engineering methods to study and modulate protease properties are thus of great interest for a plethora of applications. We established PROFICS (PRotease Optimization via Fusion-Inhibited Carbamoyltransferase-based Selection), a bacterial selection system, which enables the optimization of proteases for biotechnology, therapeutics or diagnosis in a simple overnight process. During the PROFICS process, proteases are selected for their ability to specifically cut a tag from a reporter enzyme and leave a native N-terminus. Precise and efficient cleavage after the recognition sequence reverses the phenotype of an Escherichia coli knockout strain deficient in an essential enzyme of pyrimidine synthesis. A toolbox was generated to select for proteases with different preferences for P1' residues (the residue immediately following the cleavage site). The functionality of PROFICS is demonstrated with viral proteases and human caspase-2. PROFICS improved caspase-2 activity up to 25-fold after only one round of mutation and selection. Additionally, we found a significantly improved tolerance for all P1' residues caused by a mutation in a substrate interaction site. We showed that this improved activity enables cells containing the new variant to outgrow cells containing all other mutants, facilitating its straightforward selection. Apart from optimizing enzymatic activity and P1' tolerance, PROFICS can be used to reprogram specificities, erase off-target activity, optimize expression via tags/codon usage, or even to screen for potential drug-resistance-conferring mutations in therapeutic targets such as viral proteases in an unbiased manner.

Reaction intermediate rotation during the decarboxylation of coproheme to heme b in C. diphtheriae

Monoderm bacteria utilize coproheme decarboxylases (ChdCs) to generate heme b by a stepwise decarboxylation of two propionate groups of iron coproporphyrin III (coproheme), forming two vinyl groups. This work focuses on actinobacterial ChdC from Corynebacterium diphtheriae (CdChdC) to elucidate the hydrogen peroxide-mediated decarboxylation of coproheme via monovinyl monopropionyl deuteroheme (MMD) to heme b, with the principal aim being to understand the reorientation mechanism of MMD during turnover. Wild-type CdChdC and variants, namely H118A, H118F, and A207E, were studied by resonance Raman and ultraviolet-visible spectroscopy, mass spectrometry, and molecular dynamics simulations. As actinobacterial ChdCs use a histidine (H118) as a distal base, we studied the H118A and H118F variants to elucidate the effect of 1) the elimination of the proton acceptor and 2) steric constraints within the active site. The A207E variant mimics the proximal H-bonding network found in chlorite dismutases. This mutation potentially increases the rigidity of the proximal site and might impair the rotation of the reaction intermediate MMD. We found that both wild-type CdChdC and the variant H118A convert coproheme mainly to heme b upon titration with H2O2. Interestingly, the variant A207E mostly accumulates MMD along with small amounts of heme b, whereas H118F is unable to produce heme b and accumulates only MMD. Together with molecular dynamics simulations, the spectroscopic data provide insight into the reaction mechanism and the mode of reorientation of MMD, i.e., a rotation in the active site versus a release and rebinding.

Machine Learning for Electronically Excited States of Molecules

Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

Machine Learning for Electronically Excited States of Molecules

Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

Evaluation of nine condensed-phase force fields of the GROMOS, CHARMM, OPLS, AMBER, and OpenFF families against experimental cross-solvation free energies

Experimental solvation free energies are nowadays commonly included as target properties in the validation of condensed-phase force fields, sometimes even in their calibration. In a previous article [Kashefolgheta et al., J. Chem. Theory. Comput., 2020, 16, 7556-7580], we showed how the involved comparison between experimental and simulation results could be made more systematic by considering a full matrix of cross-solvation free energies . For a set of N molecules that are all in the liquid state under ambient conditions, such a matrix encompasses N×N entries for considering each of the N molecules either as solute (A) or as solvent (B). In the quoted study, a cross-solvation matrix of 25 × 25 experimental value was introduced, considering 25 small molecules representative for alkanes, chloroalkanes, ethers, ketones, esters, alcohols, amines, and amides. This experimental data was used to compare the relative accuracies of four popular condensed-phase force fields, namely GROMOS-2016H66, OPLS-AA, AMBER-GAFF, and CHARMM-CGenFF. In the present work, the comparison is extended to five additional force fields, namely GROMOS-54A7, GROMOS-ATB, OPLS-LBCC, AMBER-GAFF2, and OpenFF. Considering these nine force fields, the correlation coefficients between experimental values and simulation results range from 0.76 to 0.88, the root-mean-square errors (RMSEs) from 2.9 to 4.8 kJ mol-1, and average errors (AVEEs) from -1.5 to +1.0 kJ mol-1. In terms of RMSEs, GROMOS-2016H66 and OPLS-AA present the best accuracy (2.9 kJ mol-1), followed by OPLS-LBCC, AMBER-GAFF2, AMBER-GAFF, and OpenFF (3.3 to 3.6 kJ mol-1), and then by GROMOS-54A7, CHARM-CGenFF, and GROMOS-ATB (4.0 to 4.8 kJ mol-1). These differences are statistically significant but not very pronounced, and are distributed rather heterogeneously over the set of compounds within the different force fields.

On the Track of Long-Range Electron Transfer in B-Type Dye-Decolorizing Peroxidases: Identification of a Tyrosyl Radical by Computational Prediction and Electron Paramagnetic Resonance Spectroscopy

The catalytic activity of dye-decolorizing peroxidases (DyPs) toward bulky substrates, including anthraquinone dyes, phenolic lignin model compounds, or 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), is in strong contrast to their sterically restrictive active site. In two of the three known subfamilies (A- and C/D-type DyPs), catalytic protein radicals at surface-exposed sites, which are connected to the heme cofactor by electron transfer path(s), have been identified. So far in B-type DyPs, there has been no evidence for protein radical formation after activation by hydrogen peroxide. Interestingly, B-type Klebsiella pneumoniae dye-decolorizing peroxidase (KpDyP) displays a persistent organic radical in the resting state composed of two species that can be distinguished by W-band electron spin echo electron paramagnetic resonance (EPR) spectroscopy. Here, on the basis of a comprehensive mutational and EPR study of computationally predicted tyrosine and tryptophan variants of KpDyP, we demonstrate the formation of tyrosyl radicals (Y247 and Y92) and a radical-stabilizing Y-W dyad between Y247 and W18 in KpDyP, which are unique to enterobacterial B-type DyPs. Y247 is connected to Y92 by a hydrogen bonding network, is solvent accessible in simulations, and is involved in ABTS oxidation. This suggests the existence of long-range electron path(s) in B-type DyPs. The mechanistic and physiological relevance of the reaction mechanism of B-type DyPs is discussed.

pyPolyBuilder: Automated Preparation of Molecular Topologies and Initial Configurations for Molecular Dynamics Simulations of Arbitrary Supramolecules

The construction of a molecular topology file is a prerequisite for any classical molecular dynamics simulation. However, the generation of such a file may be very challenging at times, especially for large supramolecules. While many tools are available to provide topologies for large proteins and other biomolecules, the scientific community researching nonbiological systems is not equally well equipped. Here, we present a practical tool to generate topologies for arbitrary supramolecules: The pyPolyBuilder. In addition to linear polymer chains, it also provides the possibility to generate topologies of arbitrary, large, branched molecules, such as, e.g., dendrimers. Furthermore, it also generates reasonable starting structures for simulations of these molecules. pyPolyBuilder is a standalone command-line tool implemented in python. Therefore, it may be easily incorporated in persisting simulation pipelines on any operating systems and with different simulation engines. pyPolyBuilder is freely available on github: https://github.com/mssm-labmmol/pypolybuilder.

Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems

Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are computationally expensive compared to fully classical simulations as all valence electrons are treated explicitly and a self-consistent field (SCF) procedure is required. Recently, approaches have been proposed to replace the QM description with machine-learned (ML) models. However, condensed-phase systems pose a challenge for these approaches due to long-range interactions. Here, we establish a workflow, which incorporates the MM environment as an element type in a high-dimensional neural network potential (HDNNP). The fitted HDNNP describes the potential-energy surface of the QM particles with an electrostatic embedding scheme. Thus, the MM particles feel a force from the polarized QM particles. To achieve chemical accuracy, we find that even simple systems require models with a strong gradient regularization, a large number of data points, and a substantial number of parameters. To address this issue, we extend our approach to a Δ-learning scheme, where the ML model learns the difference between a reference method (density functional theory (DFT)) and a cheaper semiempirical method (density functional tight binding (DFTB)). We show that such a scheme reaches the accuracy of the DFT reference method while requiring significantly less parameters. Furthermore, the Δ-learning scheme is capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. It is validated by performing MD simulations of retinoic acid in water and the interaction between S-adenoslymethioniat and cytosine in water. The presented results indicate that Δ-learning is a promising approach for (QM)ML/MM MD simulations of condensed-phase systems.

Modulation of the Passive Permeability of Semipeptidic Macrocycles: N- and C-Methylations Fine-Tune Conformation and Properties

Incorporating small modifications to peptidic macrocycles can have a major influence on their properties. For instance, N-methylation has been shown to impact permeability. A better understanding of the relationship between permeability and structure is of key importance as peptidic drugs are often associated with unfavorable pharmacokinetic profiles. Starting from a semipeptidic macrocycle backbone composed of a tripeptide tethered head-to-tail with an alkyl linker, we investigated two small changes: peptide-to-peptoid substitution and various methyl placements on the nonpeptidic linker. Implementing these changes in parallel, we created a collection of 36 compounds. Their permeability was then assessed in parallel artificial membrane permeability assay (PAMPA) and Caco-2 assays. Our results show a systematic improvement in permeability associated with one peptoid position in the cycle, while the influence of methyl substitution varies on a case-by-case basis. Using a combination of molecular dynamics simulations and NMR measurements, we offer hypotheses to explain such behavior.

Chimeric Cellobiose Dehydrogenases Reveal the Function of Cytochrome Domain Mobility for the Electron Transfer to Lytic Polysaccharide Monooxygenase

The natural function of cellobiose dehydrogenase (CDH) to donate electrons from its catalytic flavodehydrogenase (DH) domain via its cytochrome (CYT) domain to lytic polysaccharide monooxygenase (LPMO) is an example of a highly efficient extracellular electron transfer chain. To investigate the function of the CYT domain movement in the two occurring electron transfer steps, two CDHs from the ascomycete Neurospora crassa (NcCDHIIA and NcCDHIIB) and five chimeric CDH enzymes created by domain swapping were studied in combination with the fungus’ own LPMOs (NcLPMO9C and NcLPMO9F). Kinetic and electrochemical methods and hydrogen/deuterium exchange mass spectrometry were used to study the domain movement, interaction, and electron transfer kinetics. Molecular docking provided insights into the protein–protein interface, the orientation of domains, and binding energies. We find that the first, interdomain electron transfer step from the catalytic site in the DH domain to the CYT domain depends on steric and electrostatic interface complementarity and the length of the protein linker between both domains but not on the redox potential difference between the FAD and heme b cofactors. After CYT reduction, a conformational change of CDH from its closed state to an open state allows the second, interprotein electron transfer (IPET) step from CYT to LPMO to occur by direct interaction of the b-type heme and the type-2 copper center. Chimeric CDH enzymes favor the open state and achieve higher IPET rates by exposing the heme b cofactor to LPMO. The IPET, which is influenced by interface complementarity and the heme b redox potential, is very efficient with bimolecular rates between 2.9 × 10⁵ and 1.1 × 10⁶ M^–1 s^–1.

Interacting with Hemoglobin: Paracoccidioides spp. Recruits hsp30 on Its Cell Surface for Enhanced Ability to Use This Iron Source

Paracoccidioides spp. are thermally dimorphic fungi that cause paracoccidioidomycosis and can affect both immunocompetent and immunocompromised individuals. The infection can lead to moderate or severe illness and death. Paracoccidioides spp. undergo micronutrients deprivation within the host, including iron. To overcome such cellular stress, this genus of fungi responds in multiple ways, such as the utilization of hemoglobin. A glycosylphosphatidylinositol (GPI)-anchored fungal receptor, Rbt5, has the primary role of acquiring the essential nutrient iron from hemoglobin. Conversely, it is not clear if additional proteins participate in the process of using hemoglobin by the fungus. Therefore, in order to investigate changes in the proteomic level of P. lutzii cell wall, we deprived the fungus of iron and then treated those cells with hemoglobin. Deprived iron cells were used as control. Next, we performed cell wall fractionation and the obtained proteins were submitted to nanoUPLC-MS^E. Protein expression levels of the cell wall F1 fraction of cells exposed to hemoglobin were compared with the protein expression of the cell wall F1 fraction of iron-deprived cells. Our results showed that P. lutzii exposure to hemoglobin increased the level of adhesins expression by the fungus, according to the proteomic data. We confirmed that the exposure of the fungus to hemoglobin increased its ability to adhere to macrophages by flow cytometry. In addition, we found that HSP30 of P. lutzii is a novel hemoglobin-binding protein and a possible heme oxygenase. In order to investigate the importance of HSP30 in the Paracoccidioides genus, we developed a Paracoccidioides brasiliensis knockdown strain of HSP30 via Agrobacterium tumefaciens-mediated transformation and demonstrated that silencing this gene decreases the ability of P. brasiliensis to use hemoglobin as a nutrient source. Additional studies are needed to establish HSP30 as a virulence factor, which can support the development of new therapeutic and/or diagnostic approaches.

On the use of 3J-coupling NMR data to derive structural information on proteins

Values of ³J-couplings as obtained from NMR experiments on proteins cannot easily be used to determine protein structure due to the difficulty of accounting for the high sensitivity of intermediate ³J-coupling values (4-8 Hz) to the averaging period that must cover the conformational variability of the torsional angle related to the ³J-coupling, and due to the difficulty of handling the multiple-valued character of the inverse Karplus relation between torsional angle and ³J-coupling. Both problems can be solved by using ³J-coupling time-averaging local-elevation restraining MD simulation. Application to the protein hen egg white lysozyme using 213 backbone and side-chain ³J-coupling restraints shows that a conformational ensemble compatible with the experimental data can be obtained using this technique, and that accounting for averaging and the ability of the algorithm to escape from local minima for the torsional angle induced by the Karplus relation, are essential for a comprehensive use of ³J-coupling data in protein structure determination.

On the Effect of the Various Assumptions and Approximations used in Molecular Simulations on the Properties of Bio-Molecular Systems: Overview and Perspective on Issues

Computer simulations of molecular systems enable structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic or supra-molecular level and plays an increasingly important role in chemistry, biology and physics. To interpret the results of such simulations appropriately, the degree of uncertainty and potential errors affecting the calculated properties must be considered. Uncertainty and errors arise from (1) assumptions underlying the molecular model, force field and simulation algorithms, (2) approximations implicit in the interatomic interaction function (force field), or when integrating the equations of motion, (3) the chosen values of the parameters that determine the accuracy of the approximations used, and (4) the nature of the system and the property of interest. In this overview, advantages and shortcomings of assumptions and approximations commonly used when simulating bio-molecular systems are considered. What the developers of bio-molecular force fields and simulation software can do to facilitate and broaden research involving bio-molecular simulations is also discussed.

On the Use of Side-Chain NMR Relaxation Data to Derive Structural and Dynamical Information on Proteins: A Case Study Using Hen Lysozyme

Values of S2CH and S2NH order parameters derived from NMR relaxation measurements on proteins cannot be used straightforwardly to determine protein structure because they cannot be related to a single protein structure, but are defined in terms of an average over a conformational ensemble. Molecular dynamics simulation can generate a conformational ensemble and thus can be used to restrain S2CH and S2NH order parameters towards experimentally derived target values S2CH (exp) and S2NH (exp). Application of S2CH and S2NH order‐parameter restraining MD simulation to bond vectors in 63 side chains of the protein hen egg white lysozyme using 51 S2CH (exp) target values and 28 S2NH (exp) target values shows that a conformational ensemble compatible with the experimentally derived data can be obtained by using this technique. It is observed that S2CH order‐parameter restraining of C−H bonds in methyl groups is less reliable than S2NH order‐parameter restraining because of the possibly less valid assumptions and approximations used to derive experimental S2CH (exp) values from NMR relaxation measurements and the necessity to adopt the assumption of uniform rotational motion of methyl C−H bonds around their symmetry axis and of the independence of these motions from each other. The restrained simulations demonstrate that side chains on the protein surface are highly dynamic. Any hydrogen bonds they form and that appear in any of four different crystal structures, are fluctuating with short lifetimes in solution.

Connecting the conformational behavior of cyclic octadepsipeptides with their ionophoric property and membrane permeability

Cyclic octadepsipeptides such as PF1022A and its synthetic derivative emodepside exhibit anthelmintic activity with the latter sold as a commercial drug treatment against gastrointestinal nematodes for animal health use. The structure-permeability relationship of these cyclic depsipeptides that could ultimately provide insights into the compound bioavailability is not yet well understood. The fully N-methylated amide backbone and apolar sidechain residues do not allow for the formation of intramolecular hydrogen bonds, normally observed in the membrane-permeable conformations of cyclic peptides. Hence, any understanding gained on these depsipeptides would serve as a prototype for future design strategies. In previous nuclear magnetic resonance (NMR) studies, two macrocyclic core conformers of emodepside were detected, one with all backbone amides in trans-configuration (hereon referred as the symmetric conformer) and the other with one amide in cis-configuration (hereon referred as the asymmetric conformer). In addition, these depsipeptides were also reported to be ionophores with a preference of potassium over sodium. In this study, we relate the conformational behavior of PF1022A, emodepside, and closely related analogs with their ionophoric characteristic probed using NMR and molecular dynamics (MD) simulations and finally evaluated their passive membrane permeability using PAMPA. We find that the equilibrium between the two core conformers shifts more towards the symmetric conformer upon addition of monovalent cations with selectivity for potassium over sodium. Both the NMR experiments and the theoretical Markov state models based on extensive MD simulations indicate a more rigid backbone for the asymmetric conformation, whereas the symmetric conformation shows greater flexibility. The experimental results further advocate for the symmetric conformation binding the cation. The PAMPA results suggest that the investigated depsipeptides are retained in the membrane, which may be advantageous for the likely target, a membrane-bound potassium channel.

Molecular Dynamics and In Vitro Quantification of Safrole DNA Adducts Reveal DNA Adduct Persistence Due to Limited DNA Distortion Resulting in Inefficient Repair

The formation and repair of N²-(trans-isosafrol-3'-yl)-2'-deoxyguanosine (S-3'-N²-dG) DNA adduct derived from the spice and herbal alkenylbenzene constituent safrole were investigated. DNA adduct formation and repair were studied in vitro and using molecular dynamics (MD) simulations. DNA adduct formation was quantified using liquid chromatography-mass spectrometry (LCMS) in wild type and NER (nucleotide excision repair) deficient CHO cells and also in HepaRG cells and primary rat hepatocytes after different periods of repair following exposure to safrole or 1'-hydroxysafrole (1'-OH safrole). The slower repair of the DNA adducts found in NER deficient cells compared to that in CHO wild type cells indicates a role for NER in repair of S-3'-N²-dG DNA adducts. However, DNA repair in liver cell models appeared to be limited, with over 90% of the adducts remaining even after 24 or 48 h recovery. In our further studies, MD simulations indicated that S-3'-N²-dG adduct formation causes only subtle changes in the DNA structure, potentially explaining inefficient activation of NER. Inefficiency of NER mediated repair of S-3'-N²-dG adducts points at persistence and potential bioaccumulation of safrole DNA adducts upon daily dietary exposure.

Molecular modelling of sorption processes of a range of diverse small organic molecules in Leonardite humic acid

Soil organic matter (SOM) is abundant in the environment and plays an important role in several biogeochemical processes, including microbial activity, soil aggregation, plant growth and carbon storage. One of its key functions is the retention and release of various chemical compounds, primarily governed by the sorption process, which strongly affects the environmental fate of nutrients and pollutants. Sorption largely depends on the composition of SOM, as well as its structure, dynamics and the thermodynamic conditions. Although several approaches are available, experimental characterization of sorption mechanisms is not easy. Computational models for predicting sorption coefficients often require a wealth of experimental data for training and are only applicable to compounds and conditions related to the training dataset. Here, we use molecular dynamics (MD) simulations to study the sorption of a range of small organic compounds. As a model SOM system we use the standard Leonardite humic acid (LHA) sample, which physicochemical properties have recently been characterized computationally in detail. This model allowed us to estimate sorption propensities of the systems at two different hydration levels (water activities close to 0 and 1), showing a remarkable correlation with experimental data. Importantly, this molecular modelling approach based on perturbation free-energy calculations is rigorously derived from statistical thermodynamics and requires no experimental sorption data for training. It is therefore in principle applicable to any SOM model or thermodynamic condition. Moreover, the power of MD simulations to provide high-resolution insight into atomistic and molecular interactions was employed to explore how sorbate molecules associate with the LHA matrix and which contacts they form. The heteroatoms of both sorbate and sorbent play an important role and water molecules are identified as further key players in facilitating the sorption process.

HIGHLIGHTS

Modelling of the sorption processes in soil organic matter at atomistic level.Rigorous, physics-based approach applicable to a range of SOM systems and conditions.Remarkable level of matching with experimental data with additional insight into the molecular mechanism.Interactions between the sorbate and local environment strongly affects the sorption process.

Actinobacterial Coproheme Decarboxylases Use Histidine as a Distal Base to Promote Compound I Formation

Coproheme decarboxylases (ChdCs) catalyze the final step in heme b biosynthesis of monoderm and some diderm bacteria. In this reaction, coproheme is converted to heme b via monovinyl monopropionate deuteroheme (MMD) in two consecutive decarboxylation steps. In Firmicutes decarboxylation of propionates 2 and 4 of coproheme depend on hydrogen peroxide and the presence of a catalytic tyrosine. Here we demonstrate that ChdCs from Actinobacteria are unique in using a histidine (H118 in ChdC from Corynebacterium diphtheriae, CdChdC) as a distal base in addition to the redox-active tyrosine (Y135). We present the X-ray crystal structures of coproheme-CdChdC and MMD-CdChdC, which clearly show (i) differences in the active site architecture between Firmicutes and Actinobacteria and (ii) rotation of the redox-active reaction intermediate (MMD) after formation of the vinyl group at position 2. Distal H118 is shown to catalyze the heterolytic cleavage of hydrogen peroxide (k_app = (4.90 ± 1.25) × 10⁴ M^–1 s^–1). The resulting Compound I is rapidly converted to a catalytically active Compound I* (oxoiron(IV) Y135^•) that initiates the radical decarboxylation reactions. As a consequence of the more efficient Compound I formation, actinobacterial ChdCs exhibit a higher catalytic efficiency in comparison to representatives from Firmicutes. On the basis of the kinetic data of wild-type CdChdC and the variants H118A, Y135A, and H118A/Y135A together with high-resolution crystal structures and molecular dynamics simulations, we present a molecular mechanism for the hydrogen peroxide dependent conversion of coproheme via MMD to heme b and discuss differences between ChdCs from Actinobacteria and Firmicutes.

Protein Conformational Change Is Essential for Reductive Activation of Lytic Polysaccharide Monooxygenase by Cellobiose Dehydrogenase

Large-scale protein domain dynamics and electron transfer are often associated. However, as protein motions span a broad range of time and length scales, it is often challenging to identify and thus link functionally relevant dynamic changes to electron transfer in proteins. It is hypothesized that large-scale domain motions direct electrons through a FAD and a heme b cofactor of the fungal cellobiose dehydrogenase (CDH) enzymes to the type-II copper center (T2Cu) of the polysaccharide-degrading lytic polysaccharide monooxygenases (LPMOs). However, as of yet, domain motions in CDH have not been linked formally to enzyme-catalyzed electron transfer reactions. The detailed structural features of CDH, which govern the functional conformational landscapes of the enzyme, have only been partially resolved. Here, we use a combination of pressure, viscosity, ionic strength, and temperature perturbation stopped-flow studies to probe the conformational landscape associated with the electron transfer reactions of CDH. Through the use of molecular dynamics simulations, potentiometry, and stopped-flow spectroscopy, we investigated how a conserved Tyr99 residue plays a key role in shaping the conformational landscapes for both the interdomain electron transfer reactions of CDH (from FAD to heme) and the delivery of electrons from the reduced heme cofactor to the LPMO T2Cu. Our studies show how motions gate the electron transfer within CDH and from CDH to LPMO and illustrate the conformational landscape for interdomain and interprotein electron transfer in this extracellular fungal electron transfer chain.