Resurrected Ancestors Reveal Origins of Metamorphism in XCL1.

In a recent study, Dishman et al. resurrected ancestors of the metamorphic chemokine, XCL1, inferred through phylogenetics, and found that metamorphism arose in the XCL1 lineage ~150 million years ago. A zigzagging evolutionary path suggests that the metamorphic properties are adaptive and reveals three design principles that could be used for technological applications.

Two P, Ten P, White P, Red P: Mechanistic Exploration of the Oligomerization of Red Phosphorus from Diphosphorus with the Ab Initio Nanoreactor.

Phosphorus is critical to humans on many fronts, yet we do not have a mechanistic understanding of some of its most basic transformations and reactions─namely the oligomerization of white phosphorus to red. With heat or under ultraviolet (UV) exposure, it has been experimentally demonstrated that white phosphorus dissociates into diphosphorus units which readily form red phosphorus. However, the mechanism of this process is unknown. The ab initio nanoreactor approach was used to explore the potential energy surface of phosphorus clusters. Density functional theory and metadynamics simulations were used to characterize potential reaction pathways. A mechanism for oligomerization is proposed to take place via diphosphorus additions at π-bonds and weak σ-bonds through three membered ring intermediates. Downhill paths through P6 and P8 clusters eventually result in P10 clusters that can oligomerize into red phosphorus chains. The initial, rate limiting step for this process has an energy barrier of 24.2 kcal/mol.

Predicting Collision-Induced-Dissociation Tandem Mass Spectra (CID-MS/MS) Using Ab Initio Molecular Dynamics.

Compound identification is at the center of metabolomics, usually by comparing experimental mass spectra against library spectra. However, most compounds are not commercially available to generate library spectra. Hence, for such compounds, MS/MS spectra need to be predicted. Machine learning and heuristic models have largely failed except for lipids. Here, quantum chemistry software can be used to predict mass spectra. However, quantum chemistry predictions for collision induced dissociation (CID) mass spectra in LC-MS/MS are rare. We present the CIDMD (Collision-Induced Dissociation via Molecular Dynamics) framework to model CID-based MS/MS spectra. It uses first-principles molecular dynamics (MD) to simulate the physical process of molecular collisions in CID tandem mass spectrometry. First, molecular ions are constructed at specific protonation sites. Using density functional theory, these protonated ions are targeted by argon collider gas atoms at user-specified velocities. Subsequent bond breakages are simulated over time for at least 1,000 fs. Each simulation is repeated multiple times from various collisional directions. Fragmentations are accumulated over those repeated collisions to generate CIDMD in silico mass spectra. Twelve small metabolites (<205 Da) were selected to test the accuracy of this framework in comparison to experimental MS/MS spectra. When testing different protomers, collider velocities, number of simulations, simulation time and impact factor b cutoffs, we yielded 261 predicted mass spectra. These in silico spectra resulted in entropy similarity scores of an average 624 ± 189 for all 261 spectra compared to their corresponding experimental spectra, which improved to 828 ± 77 when using optimal parameters of the most probable protomers for 12 molecules. With increasing molecular mass, higher velocities achieved better results. Similarly, different protomers showed large differences in fragmentation; hence, with increasing numbers of protomers and tautomers, the average CIDMD prediction accuracy decreased. Mechanistic details showed that specific fragment ions can be produced from different protomers via multiple fragmentation pathways. We propose that CIDMD is a suitable tool to predict mass spectra of small metabolites like produced by the gut microbiome.

Impact of Protonation Sites on Collision-Induced Dissociation-MS/MS Using CIDMD Quantum Chemistry Modeling.

Protonation is the most frequent adduct found in positive electrospray ionization collision-induced mass spectra (CID-MS/MS). In a parallel report Lee, J. J. Chem. Inf. Model. 2024, 10.1021/acs.jcim.4c00760, we developed a quantum chemistry framework to predict mass spectra by collision-induced dissociation molecular dynamics (CIDMD). As different protonation sites affect fragmentation pathways of a given molecule, the accuracy of predicting tandem mass spectra by CIDMD ultimately depends on the choice of its protomers. To investigate the impact of molecular protonation sites on MS/MS spectra, we compared CIDMD-predicted spectra to all available experimental MS/MS spectra by similarity matching. We probed 10 molecules with a total of 43 protomers, the largest study to date, including organic acids (sorbic acid, citramalic acid, itaconic acid, mesaconic acid, citraconic acid, and taurine) as well as aromatic amines including uracil, aniline, bufotenine, and psilocin. We demonstrated how different protomers can converge different fragmentation pathways to the same fragment ions but also may explain the presence of different fragment ions in experimental MS/MS spectra. For the first time, we used in silico MS/MS predictions to test the impact of solvents on proton affinities, comparing the gas phase and a mixture of acetonitrile/water (1:1). We also extended applications of in silico MS/MS predictions to investigate the impact of protonation sites on the energy barriers of isomerization between protomers via proton transfer. Despite our initial hypothesis that the thermodynamically most stable protomer should give the best match to the experiment, we found only weak inverse relationships between the calculated proton affinities and corresponding entropy similarities of experimental and CIDMD-predicted MS/MS spectra. CIDMD-predicted mechanistic details of fragmentation reaction pathways revealed a clear preference for specific protomer forms for several molecules. Overall, however, proton affinity was not a good predictor corresponding to the predicted CIDMD spectra. For example, for uracil, only one protomer predicted all experimental MS/MS fragment ions, but this protomer had neither the highest proton affinity nor the best MS/MS match score. Instead of proton affinity, the transfer of protons during the electrospray process from the initial protonation site (i.e., mobile proton model) better explains the differences between the thermodynamic rationale and experimental data. Protomers that undergo fragmentation with lower energy barriers have greater contributions to experimental MS/MS spectra than their thermodynamic Boltzmann populations would suggest. Hence, in silico predictions still need to calculate MS/MS spectra for multiple protomers, as the extent of distributions cannot be readily predicted.

Revealing elusive conformations of sucrose from hydrogen bond J-coupling in H2O: A combined NMR and quantum mechanics study.

Hydrogen bonding is a crucial feature of biomolecules, but its characterization in glycans dissolved in aqueous solutions is challenging due to rapid hydrogen exchange between hydroxyl groups and H2O. In principle, the scalar (J) coupling constant can reveal the relative orientation of the atoms in the molecule. In contrast to J-coupling through H-bonds reported in proteins and nucleic acids, research on J-coupling through H-bonds in glycans dissolved in water is lacking. Here, we use sucrose as a model system for H-bonding studies; its structure, which consists of glucose (Glc) and fructose (Frc), is well-studied, and it is readily available. We apply the in-phase, antiphase-HSQC-TOCSY and quantify previously unreported through H-bond J-values for Frc-OH1-Glc-OH2 in H2O. While earlier reports of Brown and Levy indicate this H-bond as having only a single direction, our reported findings indicate the potential presence of two involving these same atoms, namely, G2OH âž” F1O and F1OH âž” G2O (where F and G stand for Frc and Glc, respectively). The calculated density functional theory J-values for the G2OH âž” F1O agree with the experimental values. Additionally, we detected four other possible H-bonds in sucrose, which require different phi, psi (ϕ, ψ) torsion angles. The ϕ, ψ values are consistent with previous predictions of du Penhoat et al. and Venable et al. Our results will provide new insights into the molecular structure of sucrose and its interactions with proteins.

The impact of conformational sampling on first-principles calculations of vicinal COCH J-couplings in carbohydrates.

Dihedral angles in organic molecules and biomolecules are vital structural parameters that can be indirectly probed by nuclear magnetic resonance (NMR) measurements of vicinal J-couplings. The empirical relations that map the measured couplings to dihedral angles are typically determined by fitting using static structural models, but this neglects the effects of thermal fluctuations at the finite temperature conditions under which NMR measurements are often taken. In this study, we calculate ensemble-averaged J-couplings for several structurally rigid carbohydrate derivatives using first-principles molecular dynamics simulations to sample the thermally accessible conformations around the minimum energy structure. Our results show that including thermal fluctuation effects significantly shifts the predicted couplings relative to single-point calculations at the energy minima, leading to improved agreement with experiments. This provides evidence that accounting for conformational sampling in first-principles calculations can improve the accuracy of NMR-based structure determination for structurally complex carbohydrates.

Copper(II) Affects the Biochemical Behavior of Proinsulin C-peptide by Forming Ternary Complexes with Serum Albumin.

Peptide hormones are essential signaling molecules with therapeutic importance. Identifying regulatory factors that drive their activity gives important insight into their mode of action and clinical development. In this work, we demonstrate the combined impact of Cu(II) and the serum protein albumin on the activity of C-peptide, a 31-mer peptide derived from the same prohormone as insulin. C-peptide exhibits beneficial effects, particularly in diabetic patients, but its clinical use has been hampered by a lack of mechanistic understanding. We show that Cu(II) mediates the formation of ternary complexes between albumin and C-peptide and that the resulting species depend on the order of addition. These ternary complexes notably alter peptide activity, showing differences from the peptide or Cu(II)/peptide complexes alone in redox protection as well as in cellular internalization of the peptide. In standard clinical immunoassays for measuring C-peptide levels, the complexes inflate the quantitation of the peptide, suggesting that such adducts may affect biomarker quantitation. Altogether, our work points to the potential relevance of Cu(II)-linked C-peptide/albumin complexes in the peptide's mechanism of action and application as a biomarker.

Quantum Chemistry Calculations for Metabolomics.

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

O-Acetyl Migration within the Sialic Acid Side Chain: A Mechanistic Study Using the Ab Initio Nanoreactor.

Many disease-causing viruses target sialic acids on the surface of host cells. Some viruses bind preferentially to sialic acids with O-acetyl modification at the hydroxyl group of C7, C8, or C9 on the glycerol-like side chain. Studies of proteins binding to sialosides containing O-acetylated sialic acids are crucial in understanding the related diseases but experimentally difficult due to the lability of the ester group. We recently showed that O-acetyl migration among hydroxyl groups of C7, C8, and C9 in sialic acids occurs in all directions in a pH-dependent manner. In the current study, we elucidate a full mechanistic pathway for the migration of O-acetyl among C7, C8, and C9. We used an ab initio nanoreactor to explore potential reaction pathways and density functional theory, pKa calculations, and umbrella sampling to investigate elementary steps of interest. We found that when a base is present, migration is easy in any direction and involves three key steps: deprotonation of the hydroxyl group, cyclization between the two carbons, and the migration of the O-acetyl group. This dynamic equilibrium may play a defensive role against pathogens that evolve to gain entry to the cell by binding selectively to one acetylation state.

Accumulation and Pulse Electron Paramagnetic Resonance Spectroscopic Investigation of the 4-Oxidobenzyl Radical Generated in the Radical S-Adenosyl-l-methionine Enzyme HydG.

The radical S-adenosyl-l-methionine (SAM) enzyme HydG cleaves tyrosine to generate CO and CN- ligands of the [FeFe] hydrogenase H-cluster, accompanied by the formation of a 4-oxidobenzyl radical (4-OB•), which is the precursor to the HydG p-cresol byproduct. Native HydG only generates a small amount of 4-OB•, limiting detailed electron paramagnetic resonance (EPR) spectral characterization beyond our initial EPR lineshape study employing various tyrosine isotopologues. Here, we show that the concentration of trapped 4-OB• is significantly increased in reactions using HydG variants, in which the "dangler Fe" to which CO and CN- bind is missing or substituted by a redox-inert Zn2+ ion. This allows for the detailed characterization of 4-OB• using high-field EPR and electron nuclear double resonance spectroscopy to extract its g-values and 1H/13C hyperfine couplings. These results are compared to density functional theory-predicted values of several 4-OB• models with different sizes and protonation states, with a best fit to the deprotonated radical anion configuration of 4-OB•. Overall, our results depict a clearer electronic structure of the transient 4-OB• radical and provide new insights into the radical SAM chemistry of HydG.

Source of Rate Acceleration for Carbocation Cyclization in Biomimetic Supramolecular Cages.

The results of quantum chemical and molecular dynamics calculations reveal that polyanionic gallium-based cages accelerate cyclization reactions of pentadienyl alcohols as a result of substrate cage interactions, preferential binding of reactive conformations of substrate/H3O+ pairs, and increased substrate basicity. However, the increase in basicity dominates. Experimental structure-activity relationship studies in which the metal vertices and overall charge of the cage are varied confirm the model derived via calculations.

Exploration and validation of force field design protocols through QM-to-MM mapping.

The scale of the parameter optimisation problem in traditional molecular mechanics force field construction means that design of a new force field is a long process, and sub-optimal choices made in the early stages can persist for many generations. We hypothesise that careful use of quantum mechanics to inform molecular mechanics parameter derivation (QM-to-MM mapping) should be used to significantly reduce the number of parameters that require fitting to experiment and increase the pace of force field development. Here, we design and train a collection of 15 new protocols for small, organic molecule force field derivation, and test their accuracy against experimental liquid properties. Our best performing model has only seven fitting parameters, yet achieves mean unsigned errors of just 0.031 g cm-3 and 0.69 kcal mol-1 in liquid densities and heats of vaporisation, compared to experiment. The software required to derive the designed force fields is freely available at https://github.com/qubekit/QUBEKit.

Benchmarking Density Functionals, Basis Sets, and Solvent Models in Predicting Thermodynamic Hydricities of Organic Hydrides.

Many renewable energy technologies, such as hydrogen gas synthesis and carbon dioxide reduction, rely on chemical reactions involving hydride anions (H-). When selecting molecules to be used in such applications, an important quantity to consider is the thermodynamic hydricity, which is the free energy required for a species to donate a hydride anion. Theoretical calculations of thermodynamic hydricity depend on several parameters, mainly the density functional, basis set, and solvent model. In order to assess the effects of the above three parameters, we carry out hydricity calculations with different combinations of density functionals, basis sets, and solvent models for a set of organic molecules with known experimental hydricity values. The data are analyzed by comparing the R2 and root-mean-squared error (RMSE) of linear fits with a fixed slope of 1 and using the Akaike Information Criterion to determine statistical significance of the RMSE rank ordering. Based on these results, we quantified the accuracy of theoretical predictions of hydricity and found that the best compromise between accuracy and computational cost was obtained by using the B3LYP-D3 density functional for the geometry optimization and free-energy corrections, either ωB97X-D3 or M06-2X-D3 for single-point energy corrections, combined with a basis set no larger than def-TZVP and the C-PCM ISWIG solvation model. At this level of theory, the RMSEs of hydricity calculations for organic molecules in acetonitrile and dimethyl sulfoxide were found to be <4 and <10 kcal/mol, respectively, for an experimental data set with a dynamic range of 20-150 kcal/mol.

Multireference configuration interaction study of the predissociation of C2 via its F1Πu state.

Photodissociation is one of the main destruction pathways for dicarbon (C2) in astronomical environments, such as diffuse interstellar clouds, yet the accuracy of modern astrochemical models is limited by a lack of accurate photodissociation cross sections in the vacuum ultraviolet range. C2 features a strong predissociative F1Πu-X1Σg + electronic transition near 130 nm originally measured in 1969; however, no experimental studies of this transition have been carried out since, and theoretical studies of the F1Πu state are limited. In this work, potential energy curves of excited electronic states of C2 are calculated with the aim of describing the predissociative nature of the F1Πu state and providing new ab initio photodissociation cross sections for astrochemical applications. Accurate electronic calculations of 56 singlet, triplet, and quintet states are carried out at the DW-SA-CASSCF/MRCI+Q level of theory with a CAS(8,12) active space and the aug-cc-pV5Z basis set augmented with additional diffuse functions. Photodissociation cross sections arising from the vibronic ground state to the F1Πu state are calculated by a coupled-channel model. The total integrated cross section through the F1Πu v = 0 and v = 1 bands is 1.198 × 10-13 cm2 cm-1, giving rise to a photodissociation rate of 5.02 × 10-10 s-1 under the standard interstellar radiation field, much larger than the rate in the Leiden photodissociation database. In addition, we report a new 21Σu + state that should be detectable via a strong 21Σu +-X1Σg + band around 116 nm.

SARS-CoV-2 and MERS-CoV Spike Protein Binding Studies Support Stable Mimic of Bound 9-O-Acetylated Sialic Acids.

Many disease-causing viruses target sialic acids (Sias), a class of nine-carbon sugars known to coat the surface of many cells, including those in the lungs. Human beta coronaviridae, known for causing respiratory tract diseases, often bind Sias, and some preferentially bind to those with 9-O-Ac-modification. Currently, co-binding of SARS-CoV-2, a beta coronavirus responsible for the COVID-19 pandemic, to human Sias has been reported and its preference towards α2-3-linked Neu5Ac has been shown. Nevertheless, O-acetylated Sias-protein binding studies are difficult to perform, due to the ester lability. We studied the binding free energy differences between Neu5,9Ac2α2-3GalßpNP and its more stable 9-NAc mimic binding to SARS-CoV-2 spike protein using molecular dynamics and alchemical free energy simulations. We identified multiple Sia-binding pockets, including two novel sites, with similar binding affinities to those of MERS-CoV, a known co-binder of sialic acid. In our binding poses, 9-NAc and 9-OAc Sias bind similarly, suggesting an experimentally reasonable mimic to probe viral mechanisms.

Quantum Chemical Study of a Radical Relay Mechanism for the HydG-Catalyzed Synthesis of a Fe(II)(CO)2(CN)cysteine Precursor to the H-Cluster of [FeFe] Hydrogenase.

The [FeFe] hydrogenase catalyzes the redox interconversion of protons and H2 with a Fe-S "H-cluster" employing CO, CN, and azadithiolate ligands to two Fe centers. The biosynthesis of the H-cluster is a highly interesting reaction carried out by a set of Fe-S maturase enzymes called HydE, HydF, and HydG. HydG, a member of the radical S-adenosylmethionine (rSAM) family, converts tyrosine, cysteine, and Fe(II) into an organometallic Fe(II)(CO)2(CN)cysteine "synthon", which serves as the substrate for HydE. Although key aspects of the HydG mechanism have been experimentally determined via isotope-sensitive spectroscopic methods, other important mechanistic questions have eluded experimental determination. Here, we use computational quantum chemistry to refine the mechanism of the HydG catalytic reaction. We utilize quantum mechanics/molecular mechanics simulations to investigate the reactions at the canonical Fe-S cluster, where a radical cleavage of the tyrosine substrate takes place and proceeds through a relay of radical intermediates to form HCN and a COO•- radical anion. We then carry out a broken-symmetry density functional theory study of the reactions at the unusual five-iron auxiliary Fe-S cluster, where two equivalents of CN- and COOH• coordinate to the fifth "dangler iron" in a series of substitution and redox reactions that yield the synthon as the final product for further processing by HydE.

Identification and characterization of metamorphic proteins: Current and future perspectives.

Proteins that can reversibly alternate between distinctly different folds under native conditions are described as being metamorphic. The "metamorphome" is the collection of all metamorphic proteins in the proteome, but it remains unknown the extent to which the proteome is populated by this class of proteins. We propose that uncovering the metamorphome will require a synergy of computational screening of protein sequences to identify potential metamorphic behavior and validation through experimental techniques. This perspective discusses computational and experimental approaches that are currently used to predict and characterize metamorphic proteins as well as the need for developing improved methodologies. Since metamorphic proteins act as molecular switches, understanding their properties and behavior could lead to novel applications of these proteins as sensors in biological or environmental contexts.

The automated optimisation of a coarse-grained force field using free energy data.

Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment.

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs.

Community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

Sequence-Based Prediction of Metamorphic Behavior in Proteins.

An increasing number of proteins have been demonstrated in recent years to adopt multiple three-dimensional folds with different functions. These metamorphic proteins are characterized by having two or more folds with significant differences in their secondary structure, in which each fold is stabilized by a distinct local environment. So far, ∼90 metamorphic proteins have been identified in the Protein Databank, but we and others hypothesize that a far greater number of metamorphic proteins remain undiscovered. In this work, we introduce a computational model to predict metamorphic behavior in proteins using only knowledge of the sequence. In this model, secondary structure prediction programs are used to calculate diversity indices, which are measures of uncertainty in predicted secondary structure at each position in the sequence; these are then used to assign protein sequences as likely to be metamorphic versus monomorphic (i.e., having just one fold). We constructed a reference data set to train our classification method, which includes a novel compilation of 136 likely monomorphic proteins and a set of 201 metamorphic protein structures taken from the literature. Our model is able to classify proteins as metamorphic versus monomorphic with a Matthews correlation coefficient of ∼0.36 and true positive/true negative rates of ∼65%/80%, suggesting that it is possible to predict metamorphic behavior in proteins using only sequence information.