Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexes in silico , particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

Exploring Structural Insights of Aβ42 and α-Synuclein Monomers and Heterodimer: A Comparative Study Using Implicit and Explicit Solvent Simulations

Protein misfolding, aggregation, and fibril formation play a central role in the development of severe neurological disorders, including Alzheimer's and Parkinson's diseases. The structural stability of mature fibrils in these diseases is of great importance, as organisms struggle to effectively eliminate amyloid plaques. To address this issue, it is crucial to investigate the early stages of fibril formation when monomers aggregate into small, toxic, and soluble oligomers. However, these structures are inherently disordered, making them challenging to study through experimental approaches. Recently, it has been shown experimentally that amyloid-β 42 (Aβ42) and α-synuclein (α-Syn) can coassemble. This has motivated us to investigate the interaction between their monomers as a first step toward exploring the possibility of forming heterodimeric complexes. In particular, our study involves the utilization of various Amber and CHARMM force-fields, employing both implicit and explicit solvent models in replica exchange and conventional simulation modes. This comprehensive approach allowed us to assess the strengths and weaknesses of these solvent models and force fields in comparison to experimental and theoretical findings, ensuring the highest level of robustness. Our investigations revealed that Aβ42 and α-Syn monomers can indeed form stable heterodimers, and the resulting heterodimeric model exhibits stronger interactions compared to the Aβ42 dimer. The binding of α-Syn to Aβ42 reduces the propensity of Aβ42 to adopt fibril-prone conformations and induces significant changes in its conformational properties. Notably, in AMBER-FB15 and CHARMM36m force fields with the use of explicit solvent, the presence of Aβ42 significantly increases the β-content of α-Syn, consistent with the experiments showing that Aβ42 triggers α-Syn aggregation. Our analysis clearly shows that although the use of implicit solvent resulted in too large compactness of monomeric α-Syn, structural properties of monomeric Aβ42 and the heterodimer were preserved in explicit-solvent simulations. We anticipate that our study sheds light on the interaction between α-Syn and Aβ42 proteins, thus providing the atom-level model required to assess the initial stage of aggregation mechanisms related to Alzheimer's and Parkinson's diseases.

Extravascular administration of IGF1R antagonists protects against aortic aneurysm in rodent and porcine models

An abdominal aortic aneurysm (AAA) is a life-threatening cardiovascular disease. We identified plasma insulin-like growth factor 1 (IGF1) as an independent risk factor in patients with AAA by correlating plasma IGF1 with risk. Smooth muscle cell- or fibroblast-specific knockout of Igf1r, the gene encoding the IGF1 receptor (IGF1R), attenuated AAA formation in two mouse models of AAA induced by angiotensin II infusion or CaCl2 treatment. IGF1R was activated in aortic aneurysm samples from human patients and mice with AAA. Systemic administration of IGF1C, a peptide fragment of IGF1, 2 weeks after disease development inhibited AAA progression in mice. Decreased AAA formation was linked to competitive inhibition of IGF1 binding to its receptor by IGF1C and modulation of downstream alpha serine/threonine protein kinase (AKT)/mammalian target of rapamycin signaling. Localized application of an IGF1C-loaded hydrogel was developed to reduce the side effects observed after systemic administration of IGF1C or IGF1R antagonists in the CaCl2-induced AAA mouse model. The inhibitory effect of the IGF1C-loaded hydrogel administered at disease onset on AAA formation was further evaluated in a guinea pig-to-rat xenograft model and in a sheep-to-minipig xenograft model of AAA formation. The therapeutic efficacy of IGF1C for treating AAA was tested through extravascular delivery in the sheep-to-minipig model with AAA established for 2 weeks. Percutaneous injection of the IGF1C-loaded hydrogel around the AAA resulted in improved vessel flow dynamics in the minipig aorta. These findings suggest that extravascular administration of IGF1R antagonists may have translational potential for treating AAA.

System-Specific Parameter Optimization for Nonpolarizable and Polarizable Force Fields

The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer (CT) and polarization (POL) effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding CT and POL. Furthermore, we present an open-source parametrization tool, called FFAFFURR, that enables the (system-specific) parametrization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a zinc-finger protein.

Tuning Potential Functions to Host-Guest Binding Data

Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.

Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data

Biological macromolecules in solution are surrounded by a hydration shell, whose structure differs from the structure of bulk solvent. While the importance of the hydration shell for numerous biological functions is widely acknowledged, it remains unknown how the hydration shell is regulated by macromolecular shape and surface composition, mainly because a quantitative probe of the hydration shell structure has been missing. We show that small-angle scattering in solution using X-rays (SAXS) or neutrons (SANS) provide a protein-specific probe of the protein hydration shell that enables quantitative comparison with molecular simulations. Using explicit-solvent SAXS/SANS predictions, we derived the effect of the hydration shell on the radii of gyration Rg of five proteins using 18 combinations of protein force field and water model. By comparing computed Rg values from SAXS relative to SANS in D2O with consensus SAXS/SANS data from a recent worldwide community effort, we found that several but not all force fields yield a hydration shell contrast in remarkable agreement with experiments. The hydration shell contrast captured by Rg values depends strongly on protein charge and geometric shape, thus providing a protein-specific footprint of protein-water interactions and a novel observable for scrutinizing atomistic hydration shell models against experimental data.

Analysis of structure-activity relationship of indol-3-yl-N-phenylcarbamic amides as potent STING inhibitors

A structure-activity relationship (SAR) study of stimulator of interferon gene (STING) inhibition was performed using a series of indol-3-yl-N-phenylcarbamic amides and indol-2-yl-N-phenylcarbamic amides. Among these analogs, compounds 10, 13, 15, 19, and 21 inhibited the phosphorylation of STING and interferon regulatory factor 3 (IRF3) to a greater extent than the reference compound, H-151. All five analogs showed stronger STING inhibition than H-151 on the 2',3'-cyclic GMP-AMP-induced expression of interferon regulatory factors (IRFs) in a STINGR232 knock-in THP-1 reporter cell line. The half-maximal inhibitory concentration of the most potent compound, 21, was 11.5 nM. The molecular docking analysis of compound 21 and STING combined with the SAR study suggested that the meta- and para-positions of the benzene ring of the phenylcarbamic amide moiety could be structurally modified by introducing halides or alkyl substituents.

Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering

Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.

Enzymatic β-elimination in natural product O- and C-glycoside deglycosylation

Biological degradation of natural product glycosides involves, alongside hydrolysis, β-elimination for glycosidic bond cleavage. Here, we discover an O-glycoside β-eliminase (OGE) from Agrobacterium tumefaciens that converts the C3-oxidized O-β-D-glucoside of phloretin (a plant-derived flavonoid) into the aglycone and the 2-hydroxy-3-keto-glycal elimination product. While unrelated in sequence, OGE is structurally homologous to, and shows effectively the same Mn2+ active site as, the C-glycoside deglycosylating enzyme (CGE) from a human intestinal bacterium implicated in β-elimination of 3-keto C-β-D-glucosides. We show that CGE catalyzes β-elimination of 3-keto O- and C-β-D-glucosides while OGE is specific for the O-glycoside substrate. Substrate comparisons and mutagenesis for CGE uncover positioning of aglycone for protonic assistance by the enzyme as critically important for C-glycoside cleavage. Collectively, our study suggests convergent evolution of active site for β-elimination of 3-keto O-β-D-glucosides. C-Glycoside cleavage is a specialized feature of this active site which is elicited by substrate through finely tuned enzyme-aglycone interactions.

Marine diterpenoid targets STING palmitoylation in mammalian cells

Natural products are important sources of therapeutic agents and useful drug discovery tools. The fused macrocycles and multiple stereocenters of briarane-type diterpenoids pose a major challenge to total synthesis and efforts to characterize their biological activities. Harnessing a scalable source of excavatolide B (excB) from cultured soft coral Briareum stechei, we generated analogs by late-stage diversification and performed structure-activity analysis, which was critical for the development of functional excB probes. We further used these probes in a chemoproteomic strategy to identify Stimulator of Interferon Genes (STING) as a direct target of excB in mammalian cells. We showed that the epoxylactone warhead of excB is required to covalently engage STING at its membrane-proximal Cys91, inhibiting STING palmitoylation and signaling. This study reveals a possible mechanism-of-action of excB, and expands the repertoire of covalent STING inhibitors.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

Fitting Force Field Parameters to NMR Relaxation Data

We present an approach to optimize force field parameters using time-dependent data from NMR relaxation experiments. To do so, we scan parameters in the dihedral angle potential energy terms describing the rotation of the methyl groups in proteins and compare NMR relaxation rates calculated from molecular dynamics simulations with the modified force fields to deuterium relaxation measurements of T4 lysozyme. We find that a small modification of C^γ methyl groups improves the agreement with experiments both for the protein used to optimize the force field and when validating using simulations of CI2 and ubiquitin. We also show that these improvements enable a more effective a posteriori reweighting of the MD trajectories. The resulting force field thus enables more direct comparison between simulations and side-chain NMR relaxation data and makes it possible to construct ensembles that better represent the dynamics of proteins in solution.

Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations

Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.

Phosphorylation Modification Force Field FB18CMAP Improving Conformation Sampling of Phosphoproteins

Phosphorylation of proteins plays an important regulatory role at almost all levels of cellular organization. Molecular dynamics (MD) simulation is a promising tool to reveal the mechanism of how phosphorylation regulates many key biological processes at the atomistic level. MD simulation accuracy depends on force field precision, while the current force fields for phospho-amino acids have resulted in notable inconsistency with experimental data. Here, a new force field parameter (named FB18CMAP) is generated by fitting against quantum mechanics (QM) energy in aqueous solution with φ/ψ dihedral potential-energy surfaces optimized using CMAP parameters. MD simulations of phosphorylated dipeptides, intrinsically disordered proteins (IDPs), and ordered (folded) proteins show that FB18CMAP can mimic NMR observables and structural characteristics of phosphorylated dipeptides and proteins more accurately than the FB18 force field. These findings suggest that FB18CMAP performs well in both the simulation of ordered and disordered states of phosphorylated proteins.

Capturing the Polarization Response of Solvated Proteins under Constant Electric Fields in Molecular Dynamics Simulations

We capture and compare the polarization response of a solvated globular protein ubiquitin to static electric (E-fields) using atomistic molecular dynamics simulations. We collectively follow E-field induced changes, electrical and structural, occurring across multiple trajectories using the magnitude of the protein dipole vector (P_p ). E-fields antiparallel to P_p induce faster structural changes and more facile protein unfolding relative to parallel fields of the same strength. While weak E-fields (0.1-0.5 V/nm) do not unfold ubiquitin and produce a reversible polarization, strong E-fields (1-2 V/nm) unfold the protein through a pathway wherein the helix:β-strand interactions rupture before those for the β1-β5 clamp. Independent of E-field direction, high E-field induced structural changes are also reversible if the field is switched off before P_p exceeds 2 times its equilibrium value. We critically examine the dependence of water properties, protein rotational diffusion and E-field induced protein unfolding pathways on the thermostat/barostat parameters used in our simulations.

Modern non-polarizable force fields diverge in modeling the enzyme-substrate complex of a canonical serine protease

Classical molecular dynamics simulation is a powerful and established method of modern computational chemistry. Being able to obtain accurate information on molecular behavior is crucial to get valuable insights into structure-function relationships that translate into fundamental findings and practical applications. Active sites of enzymes are known to be particularly intricate, therefore, simpler non-polarizable force fields may provide an inaccurate description. In this work, we addressed this hypothesis in a case of a canonical serine triad protease trypsin in its complex with a substrate-mimicking inhibitor. We tested six modern and popular force fields to find that significantly diverging results may be obtained. Amber FB-15 and OPLS-AA/M turned out to model the active site incorrectly. Amber ff19sb and ff15ipq demonstrated mixed performance. The best performing force fields were CHARMM36m and Amber ff99sb-ildn, therefore, they are recommended for use with this and related systems. We speculate that a similar lack of cross-force field convergence may be characteristic of other enzymatic systems. Therefore, we advocate for careful consideration of different force fields in any study within the field of computational enzymology.

Challenges in Protein QM/MM Simulations with Intra-Backbone Link Atoms

Hybrid quantum mechanical/molecular mechanical (QM/MM) simulations fuel discoveries in many fields of science including computational biochemistry and enzymology. Development of more convenient tools leads to an increase in the number of works in which mechanical insights into enzymes' mode of operation are obtained. Most commonly, these tools feature hydrogen-capping (link atom) approach to provide coupling between QM and MM subsystems across a covalent bond. Extensive studies were conducted to provide a solid foundation for the correctness of such an approach when a bond to a nonpolar MM atom is considered. However, not every task may be accomplished this way. Certain scenarios of using QM/MM in computational enzymology encourage or even necessitate the incorporation of backbone atoms into the QM region. Two out of three backbone atoms are polar, and in QM/MM with electrostatic embedding, a neighboring link atom will be hyperpolarized. Several schemes to mitigate this effect were previously proposed alongside a rigorous assessment of quantitative effects on model systems. However, it was not clear whether they may translate into qualitatively different results and how link atom hyperpolarization may manifest itself in a real-life enzymological scenario. Here, we show that the consequences of such an artifact may be severe and may completely overturn the conclusions drawn from the simulations. Our case advocates for the use of charge redistribution schemes whenever intra-backbone QM/MM boundaries are considered. Moreover, we addressed how different boundary types and charge redistribution schemes influence backbone dynamics. We showed that the results are heavily dependent on which boundary MM terms are retained, with charge alteration being of secondary importance. In the worst case, only three intra-backbone boundaries may be used with relative confidence in the adequacy of resulting simulations, irrespective of the hyperpolarization mitigation scheme. Thus, advances in the field are certainly needed to fuel new discoveries. As of now, we believe that issues raised in this work might encourage authors in the field to report what boundaries, boundary MM terms, and charge redistribution schemes they are using, so their results may be correctly interpreted.

Using physical property surrogate models to perform accelerated multi-fidelity optimization of force field parameters†

Accurate representations of van der Waals dispersion–repulsion interactions play an important role in high-quality molecular dynamics simulations. Training the force field parameters used in the Lennard Jones (LJ) potential typically used to represent these interactions is challenging, generally requiring adjustment based on simulations of macroscopic physical properties. The large computational expense of these simulations, especially when many parameters must be trained simultaneously, limits the size of training data set and number of optimization steps that can be taken, often requiring modelers to perform optimizations within a local parameter region. To allow for more global LJ parameter optimization against large training sets, we introduce a multi-fidelity optimization technique which uses Gaussian process surrogate modeling to build inexpensive models of physical properties as a function of LJ parameters. This approach allows for fast evaluation of approximate objective functions, greatly accelerating searches over parameter space and enabling the use of optimization algorithms capable of searching more globally. In this study, we use an iterative framework which performs global optimization with differential evolution at the surrogate level, followed by validation at the simulation level and surrogate refinement. Using this technique on two previously studied training sets, containing up to 195 physical property targets, we refit a subset of the LJ parameters for the OpenFF 1.0.0 (Parsley) force field. We demonstrate that this multi-fidelity technique can find improved parameter sets compared to a purely simulation-based optimization by searching more broadly and escaping local minima. Additionally, this technique often finds significantly different parameter minima that have comparably accurate performance. In most cases, these parameter sets are transferable to other similar molecules in a test set. Our multi-fidelity technique provides a platform for rapid, more global optimization of molecular models against physical properties, as well as a number of opportunities for further refinement of the technique.

We present a multi-fidelity method for optimizing nonbonded force field parameters against physical property data. Leveraging fast surrogate models, we accelerate the parameter search and find novel solutions that improve force field performance.

Collaborative Assessment of Molecular Geometries and Energies from the Open Force Field

Force fields form the basis for classical molecular simulations, and their accuracy is crucial for the quality of, for instance, protein-ligand binding simulations in drug discovery. The huge diversity of small-molecule chemistry makes it a challenge to build and parameterize a suitable force field. The Open Force Field Initiative is a combined industry and academic consortium developing a state-of-the-art small-molecule force field. In this report, industry members of the consortium worked together to objectively evaluate the performance of the force fields (referred to here as OpenFF) produced by the initiative on a combined public and proprietary dataset of 19,653 relevant molecules selected from their internal research and compound collections. This evaluation was important because it was completely blind; at most partners, none of the molecules or data were used in force field development or testing prior to this work. We compare the Open Force Field "Sage" version 2.0.0 and "Parsley" version 1.3.0 with GAFF-2.11-AM1BCC, OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries and conformer energies compared to reference quantum mechanical data. We show that OPLS4 performs best, and the latest Open Force Field release shows a clear improvement compared to its predecessors. The performance of established force fields such as GAFF-2.11 was generally worse. While OpenFF researchers were involved in building the benchmarking infrastructure used in this work, benchmarking was done entirely in-house within industrial organizations and the resulting assessment is reported here. This work assesses the force field performance using separate benchmarking steps, external datasets, and involving external research groups. This effort may also be unique in terms of the number of different industrial partners involved, with 10 different companies participating in the benchmark efforts.

Is an Inductive Effect Explicit Account Required for Atomic Charges Aimed at Use within the Force Fields?

Polarization and inductive effects are the concepts that have been widely used in qualitative and even quantitative descriptions of experimentally observed properties in chemistry. The polarization effect has proven to be important in cases of biomolecular modeling though still the vast majority of molecular simulations use the classical non-polarizable force fields. In the last few decades, a lot of effort has been put into promoting the polarization effect and incorporating it into modern force fields and charge calculation methods. In contrast, the inductive effect has not attracted such attention and is effectively absent in both classic and modern force fields. Thus, a question is whether this difference corresponds to the difference in the physical significance of the effects and their explicit account, or is an artifact that should be corrected in the next generation of force fields. The significance of the electronic effects is studied in this paper through the prism of performance of specific models for atomic charge calculation that take into explicit account a nested set of effects: the formal charge, the nearest neighbors, the inductive effect, and finally the model, which takes into account all effects, which are possible to account for using atomic charges. The specific choice for the methods is the following: formal charges, MMFF94 bond charge increments, Dynamic Electronegativity Relaxation (DENR), and RESP. We propose a special scheme for the separate estimation of each particular effect contribution. By pairwise comparing the residual molecular electrostatic potential (MEP) errors of those charge models (aimed at best reproducing the quantum chemical reference MEP), we sequentially revealed how the account of each effect contributes to the better-quality MEP reproduction. The following relative importance of effects was estimated; thus, the natural hierarchy of the effects was established. First, the account of formal charges is of primordial importance. Second, the nearest neighbors account is the next in significance. Third, the explicit account of inductive effect in empirical charge calculation schemes was shown to significantly─both qualitatively and quantitatively─improve the quality of MEP reproduction. Fourth, the contribution of polarization is indirectly assessed. Surprisingly, it is of the order of magnitude of the inductive effect even for the molecular systems, for which it is anticipated to be more significant. Finally, the relative importance of anisotropic effects in neutral molecules was additionally reviewed.

Orthology-based analysis helps map evolutionary diversification and predict substrate class use of BAHD acyltransferases

Large enzyme families catalyze metabolic diversification by virtue of their ability to use diverse chemical scaffolds. How enzyme families attain such functional diversity is not clear. Furthermore, duplication and promiscuity in such enzyme families limits their functional prediction, which has produced a burgeoning set of incompletely annotated genes in plant genomes. Here, we address these challenges using BAHD acyltransferases as a model. This fast-evolving family expanded drastically in land plants, increasing from one to five copies in algae to approximately 100 copies in diploid angiosperm genomes. Compilation of >160 published activities helped visualize the chemical space occupied by this family and define eight different classes based on structural similarities between acceptor substrates. Using orthologous groups (OGs) across 52 sequenced plant genomes, we developed a method to predict BAHD acceptor substrate class utilization as well as origins of individual BAHD OGs in plant evolution. This method was validated using six novel and 28 previously characterized enzymes and helped improve putative substrate class predictions for BAHDs in the tomato genome. Our results also revealed that while cuticular wax and lignin biosynthetic activities were more ancient, anthocyanin acylation activity was fixed in BAHDs later near the origin of angiosperms. The OG-based analysis enabled identification of signature motifs in anthocyanin-acylating BAHDs, whose importance was validated via molecular dynamic simulations, site-directed mutagenesis and kinetic assays. Our results not only describe how BAHDs contributed to evolution of multiple chemical phenotypes in the plant world but also propose a biocuration-enabled approach for improved functional annotation of plant enzyme families.

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 Neu5,9Ac2α2-3GalβpNP 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.

Mebendazole's Conformational Space and Its Predicted Binding to Human Heat-Shock Protein 90

Recent experimental evidence suggests that mebendazole, a popular antiparasitic drug, binds to heat shock protein 90 (Hsp90) and inhibits acute myeloid leukemia cell growth. In this study we use quantum mechanics (QM), molecular similarity, and molecular dynamics (MD) calculations to predict possible binding poses of mebendazole to the adenosine triphosphate (ATP) binding site of Hsp90. Extensive conformational searches and minimization of the five mebendazole tautomers using the MP2/aug-cc-pVTZ theory level resulted in 152 minima. Mebendazole-Hsp90 complex models were subsequently created using the QM optimized conformations and protein coordinates obtained from experimental crystal structures that were chosen through similarity calculations. Nine different poses were identified from a total of 600 ns of explicit solvent, all-atom MD simulations using two different force fields. All simulations support the hypothesis that mebendazole is able to bind to the ATP binding site of Hsp90.

Generalized Born Implicit Solvent Models Do Not Reproduce Secondary Structures of De Novo Designed Glu/Lys Peptides

We test a range of standard generalized Born (GB) models and protein force fields for a set of five experimentally characterized, designed peptides comprising alternating blocks of glutamate and lysine, which have been shown to differ significantly in α-helical content. Sixty-five combinations of force fields and GB models are evaluated in >800 μs of molecular dynamics simulations. GB models generally do not reproduce the experimentally observed α-helical content, and none perform well for all five peptides. These results illustrate that these models are not usefully predictive in this context. These peptides provide a useful test set for simulation methods.

Guanidinium-rich lipopeptide functionalized bacteria-absorbing sponge as an effective trap-and-kill system for the elimination of focal bacterial infection

Focal bacterial infections are often difficult to treat due to the rapid emergence of antibiotic-resistant bacteria, high risk of relapse, and severe inflammation at local lesions. To address multidrug-resistant skin and soft tissue infections, a bacteria-absorbing sponge was prepared to involve a "trap-and-kill" mechanism. The system describes a guanidinium-rich lipopeptide functionalized lyotropic liquid-crystalline hydrogel with bicontinuous cubic networks. Amphiphilic lipopeptides can be spontaneously anchored to the lipid-water interface, exposing their bacterial targeting sequences to enhance antibacterial trapping/killing activity. Computational simulations supported our structural predictions, and the sponge was confirmed to successfully remove ∼98.8% of the bacteria in the medium. Release and degradation behavior studies indicated that the bacteria-absorbing sponge could degrade, mediate enzyme-responsive lipopeptide release, or generate ∼200 nm lipopeptide nanoparticles with environmental erosion. This implies that the sponge can effectively capture and isolate high concentrations of bacteria at the infected site and then sustainably release antimicrobial lipopeptides into deep tissues for the eradication of residual bacteria. In the animal experiment, we found that the antibacterial performance of the bacterial-absorbing sponge was significant, which demonstrated not only a long-term inhibition effect to disinfect and avoid bacterial rebound, but also a unique advantage to protect tissue from bacterial attack. STATEMENT OF SIGNIFICANCE: Host defense peptides/peptidomimetics (HDPs) have shown potential for the elimination of focal bacterial infections, but the application of their topical formulations suffers from time-consuming preparation processes, indistinctive toxicity reduction effects, and inefficient bacterial capture ability. To explore new avenues for the development of easily prepared, low-toxicity and high-efficiency topical antimicrobials, a guanidinium-rich lipopeptide was encapsulated in a lyotropic liquid-crystalline hydrogel (denoted as "bacteria-absorbing sponge") to achieve complementary superiorities. The superior characteristic of the bacteria-absorbing sponge involves a "trap-and-kill" mechanism, which undergoes not only a long-term inhibition effect to disinfect and avoid bacterial rebound, but also effective bacterial capture and isolating action to confine bacterial diffusion and protect tissues from bacterial attack.

High Order Ab Initio Valence Force Field with Chemical Pattern Based Parameter Assignment

Bonded (or valence) interactions, which directly determine the local structures of the molecules, are fundamental parts of molecular mechanics force fields (FFs). Most popular classical FFs adopt the simple harmonic models for bond stretching and angle bending and ignore cross-coupling effects among the valence terms. This may lead to less accurate vibrational properties and configurations in molecular dynamics (MD) simulations. AMOEBA models utilize an MM3(MM4)-style bonded interaction model, in which the vibrational anharmonicity, the coupling effects among different energy terms, and the out-of-plane bending for sp2-hybridized atoms are considered. In this work, we report the development of bonded interaction parameters for a wide range of chemistry based on quantum mechanics (QM). About 270 atomic types defined by SMARTS strings were used to model the valence interactions. Our results indicate that the resulting valence parameters produce accurate vibrational frequencies (RMSD from QM is less than ~36.6 cm^-1) over a large set of molecules with diverse functional groups (445 molecules). By contrast, the harmonic models usually give an RMS error greater than 60 cm^-1. Meanwhile, this model accurately reflects the potential energy surface of the out-of-plane bending. Our model can generally be applied to the AMOEBA family and any MM3(MM4)-based molecular mechanics FFs.

Obtaining QM/MM binding free energies in the SAMPL8 drugs of abuse challenge: indirect approaches

Accurately predicting free energy differences is essential in realizing the full potential of rational drug design. Unfortunately, high levels of accuracy often require computationally expensive QM/MM Hamiltonians. Fortuitously, the cost of employing QM/MM approaches in rigorous free energy simulation can be reduced through the use of the so-called “indirect” approach to QM/MM free energies, in which the need for QM/MM simulations is avoided via a QM/MM “correction” at the classical endpoints of interest. Herein, we focus on the computation of QM/MM binding free energies in the context of the SAMPL8 Drugs of Abuse host–guest challenge. Of the 5 QM/MM correction coupled with force-matching submissions, PM6-D3H4/MM ranked submission proved the best overall QM/MM entry, with an RMSE from experimental results of 2.43 kcal/mol (best in ranked submissions), a Pearson’s correlation of 0.78 (second-best in ranked submissions), and a Kendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ correlation of 0.52 (best in ranked submissions).

Improving Force Field Accuracy by Training against Condensed-Phase Mixture Properties

Developing a sufficiently accurate classical force field representation of molecules is key to realizing the full potential of molecular simulations as a route to gaining a fundamental insight into a broad spectrum of chemical and biological phenomena. This is only possible, however, if the many complex interactions between molecules of different species in the system are accurately captured by the model. Historically, the intermolecular van der Waals (vdW) interactions have primarily been trained against densities and enthalpies of vaporization of pure (single-component) systems, with occasional usage of hydration free energies. In this study, we demonstrate how including physical property data of binary mixtures can better inform these parameters, encoding more information about the underlying physics of the system in complex chemical mixtures. To demonstrate this, we retrain a select number of Lennard-Jones parameters describing the vdW interactions of the OpenFF 1.0.0 (Parsley) fixed charge force field against training sets composed of densities and enthalpies of mixing for binary liquid mixtures as well as densities and enthalpies of vaporization of pure liquid systems and assess the performance of each of these combinations. We show that retraining against the mixture data improves the force field's ability to reproduce mixture properties, including solvation free energies, correcting some systematic errors that exist when training vdW interactions against properties of pure systems only.

Automated fitting of transition state force fields for biomolecular simulations

The generation of surrogate potential energy functions (PEF) that are orders of magnitude faster to compute but as accurate as the underlying training data from high-level electronic structure methods is one of the most promising applications of fitting procedures in chemistry. In previous work, we have shown that transition state force fields (TSFFs), fitted to the functional form of MM3* force fields using the quantum guided molecular mechanics (Q2MM) method, provide an accurate description of transition states that can be used for stereoselectivity predictions of small molecule reactions. Here, we demonstrate the applicability of the method for fit TSFFs to the well-established Amber force field, which could be used for molecular dynamics studies of enzyme reaction. As a case study, the fitting of a TSFF to the second hydride transfer in Pseudomonas mevalonii 3-hydroxy-3-methylglutaryl coenzyme A reductase (PmHMGR) is used. The differences and similarities to fitting of small molecule TSFFs are discussed.

Development and Validation of Fluorinated, Aromatic Amino Acid Parameters for Use with the AMBER ff15ipq Protein Force Field

We developed force field parameters for fluorinated, aromatic amino acids enabling molecular dynamics (MD) simulations of fluorinated proteins. These parameters are tailored to the AMBER ff15ipq protein force field and enable the modeling of 4, 5, 6, and 7F-tryptophan, 3F- and 3,5F-tyrosine, and 4F- or 4-CF3-phenylalanine. The parameters include 181 unique atomic charges derived using the implicitly polarized charge (IPolQ) scheme in the presence of SPC/Eb explicit water molecules and 9 unique bond, angle, or torsion terms. Our simulations of benchmark peptides and proteins maintain expected conformational propensities on the μs time scale. In addition, we have developed an open-source Python program to calculate fluorine relaxation rates from MD simulations. The extracted relaxation rates from protein simulations are in good agreement with experimental values determined by 19F NMR. Collectively, our results illustrate the power and robustness of the IPolQ lineage of force fields for modeling the structure and dynamics of fluorine-containing proteins at the atomic level.

Integration of Experimental Data and Use of Automated Fitting Methods in Developing Protein Force Fields

The development of accurate protein force fields has been the cornerstone of molecular simulations for the past 50 years. During this period, many lessons have been learned regarding the use of experimental target data and parameter fitting procedures. Here, we review recent advances in protein force field development. We discuss the recent emergence of polarizable force fields and the role of electronic polarization and areas in which additive force fields fall short. The use of automated fitting methods and the inclusion of additional experimental solution data during parametrization is discussed as a means to highlight possible routes to improve the accuracy of force fields even further.

Comparing the Performance of Different AMBER Protein Forcefields, Partial Charge Assignments, and Water Models for Absolute Binding Free Energy Calculations

Identifying chemical starting points is a vital first step in small molecule drug discovery and can take significant time and money. For this reason, computational approaches to virtual screening are of great interest as they can lower the cost and shorten timeframes. However, simple approaches such as molecular docking and pharmacophore screening are of limited accuracy and provide a low probability of success. Alchemical binding free energies represent a promising approach for virtual screening as they naturally incorporate the key effects of water molecules, protein flexibility, and binding entropy. However, the calculations are technically very challenging, with performance depending on the specific forcefield used. For this reason, it is important that the community has access to benchmark test sets to assess prediction accuracy. In this paper, we present an approach to alchemical binding free energies using OpenMM. We identify effective simulation parameters using an existing BRD4(1) test set and present two new benchmark sets (cMET and PDE2A) that can be used in the community for validation purposes. Our findings also highlight the effectiveness of some AMBER forcefields, in particular, AMBER ff15ipq.

Molecular Dynamics-Assisted Optimization of Protein NMR Relaxation Analysis

NMR relaxation analysis of the mobile residues in globular proteins is sensitive to the form of the experimentally fitted internal autocorrelation function, which is used to represent that motion. Different order parameter representations can precisely fit the same set of ¹⁵N R₁, R₂, and heteronuclear NOE measurements while yielding significantly divergent predictions of the underlying autocorrelation functions, indicating the insufficiency of these experimental relaxation data for assessing which order parameter representation provides the most physically realistic predictions. Molecular dynamics simulations offer an unparalleled capability for discriminating among different order parameter representations to assess which representation can most accurately model a wide range of physically realistic autocorrelation functions. Six currently utilized AMBER and CHARMM force fields were applied to calculate autocorrelation functions for the backbone H–N bond vectors of ubiquitin as an operational test set. An optimized time constant-constrained triexponential (TCCT) representation was shown to markedly outperform the widely used (S_f²,τ_s,S²) extended Lipari–Szabo representation and the more closely related (S_f²,S_H², S_N²) Larmor frequency-selective representation. Optimization of the TCCT representation at both 600 and 900 MHz ¹H converged to the same parameterization. The higher magnetic field yielded systematically larger deviations in the back-prediction of the autocorrelation functions for the mobile amides, indicating little added benefit from multiple field measurements in analyzing amides that lack slower (∼ms) exchange line-broadening effects. Experimental ¹⁵N relaxation data efficiently distinguished among the different force fields with regard to their prediction of ubiquitin backbone conformational dynamics in the ps–ns time frame. While the earlier AMBER 99SB and CHARMM27 force fields underestimate the scale of backbone dynamics, which occur in this time frame, AMBER 14SB provided the most consistent predictions for the well-averaged highly mobile C-terminal residues of ubiquitin.

A Benchmark Protocol for DFT Approaches and Data-Driven Models for Halide-Water Clusters

Dissolved ions in aqueous media are ubiquitous in many physicochemical processes, with a direct impact on research fields, such as chemistry, climate, biology, and industry. Ions play a crucial role in the structure of the surrounding network of water molecules as they can either weaken or strengthen it. Gaining a thorough understanding of the underlying forces from small clusters to bulk solutions is still challenging, which motivates further investigations. Through a systematic analysis of the interaction energies obtained from high-level electronic structure methodologies, we assessed various dispersion-corrected density functional approaches, as well as ab initio-based data-driven potential models for halide ion-water clusters. We introduced an active learning scheme to automate the generation of optimally weighted datasets, required for the development of efficient bottom-up anion-water models. Using an evolutionary programming procedure, we determined optimized and reference configurations for such polarizable and first-principles-based representation of the potentials, and we analyzed their structural characteristics and energetics in comparison with estimates from DF-MP2 and DFT+D quantum chemistry computations. Moreover, we presented new benchmark datasets, considering both equilibrium and non-equilibrium configurations of higher-order species with an increasing number of water molecules up to 54 for each F, Cl, Br, and I anions, and we proposed a validation protocol to cross-check methods and approaches. In this way, we aim to improve the predictive ability of future molecular computer simulations for determining the ongoing conflicting distribution of different ions in aqueous environments, as well as the transition from nanoscale clusters to macroscopic condensed phases.

Impact of the R292K Mutation on Influenza A (H7N9) Virus Resistance towards Peramivir: A Molecular Dynamics Perspective

In March 2013, a novel avian influenza A (H7N9) virus emerged in China. By March 2021, it had infected more than 1500 people, raising concerns regarding its epidemic potential. Similar to the highly pathogenic H5N1 virus, the H7N9 virus causes severe pneumonia and acute respiratory distress syndrome in most patients. Moreover, genetic analysis showed that this avian H7N9 virus carries human adaptation markers in the hemagglutinin and polymerase basic 2 (PB2) genes associated with cross-species transmissibility. Clinical studies showed that a single mutation, neuraminidase (NA) R292K (N2 numbering), induces resistance to peramivir in the highly pathogenic H7N9 influenza A viruses. Therefore, to evaluate the risk for human public health and understand the possible source of drug resistance, we assessed the impact of the NA-R292K mutation on avian H7N9 virus resistance towards peramivir using various molecular dynamics approaches. We observed that the single point mutation led to a distorted peramivir orientation in the enzyme active site which, in turn, perturbed the inhibitor's binding. The R292K mutation induced a decrease in the interaction among neighboring amino acid residues when compared to its wild-type counterpart, as shown by the high degree of fluctuations in the radius of gyration. MM/GBSA calculations revealed that the mutation caused a decrease in the drug binding affinity by 17.28 kcal/mol when compared to the that for the wild-type enzyme. The mutation caused a distortion of hydrogen bond-mediated interactions with peramivir and increased the accessibility of water molecules around the K292 mutated residue.

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.

Changes in the Local Conformational States Caused by Simple Na+ and K+ Ions in Polyelectrolyte Simulations: Comparison of Seven Force Fields with and without NBFIX and ECC Corrections

Electrostatic interactions have a determining role in the conformational and dynamic behavior of polyelectrolyte molecules. In this study, anionic polyelectrolyte molecules, poly(glutamic acid) (PGA) and poly(aspartic acid) (PASA), in a water solution with the most commonly used K+ or Na+ counterions, were investigated using atomistic molecular dynamics (MD) simulations. We performed a comparison of seven popular force fields, namely AMBER99SB-ILDN, AMBER14SB, AMBER-FB15, CHARMM22*, CHARMM27, CHARMM36m and OPLS-AA/L, both with their native parameters and using two common corrections for overbinding of ions, the non-bonded fix (NBFIX), and electronic continuum corrections (ECC). These corrections were originally introduced to correct for the often-reported problem concerning the overbinding of ions to the charged groups of polyelectrolytes. In this work, a comparison of the simulation results with existing experimental data revealed several differences between the investigated force fields. The data from these simulations and comparisons with previous experimental data were then used to determine the limitations and strengths of these force fields in the context of the structural and dynamic properties of anionic polyamino acids. Physical properties, such as molecular sizes, local structure, and dynamics, were studied using two types of common counterions, namely potassium and sodium. The results show that, in some cases, both the macroion size and dynamics depend strongly on the models (parameters) for the counterions due to strong overbinding of the ions and charged side chain groups. The local structures and dynamics are more sensitive to dihedral angle parameterization, resulting in a preference for defined monomer conformations and the type of correction used. We also provide recommendations based on the results.

Combined Pharmacophore and Grid-Independent Molecular Descriptors (GRIND) Analysis to Probe 3D Features of Inositol 1,4,5-Trisphosphate Receptor (IP3R) Inhibitors in Cancer

Inositol 1, 4, 5-trisphosphate receptor (IP₃R)-mediated Ca²⁺ signaling plays a pivotal role in different cellular processes, including cell proliferation and cell death. Remodeling Ca²⁺ signals by targeting the downstream effectors is considered an important hallmark in cancer progression. Despite recent structural analyses, no binding hypothesis for antagonists within the IP₃-binding core (IBC) has been proposed yet. Therefore, to elucidate the 3D structural features of IP₃R modulators, we used combined pharmacoinformatic approaches, including ligand-based pharmacophore models and grid-independent molecular descriptor (GRIND)-based models. Our pharmacophore model illuminates the existence of two hydrogen-bond acceptors (2.62 Å and 4.79 Å) and two hydrogen-bond donors (5.56 Å and 7.68 Å), respectively, from a hydrophobic group within the chemical scaffold, which may enhance the liability (IC₅₀) of a compound for IP₃R inhibition. Moreover, our GRIND model (PLS: Q² = 0.70 and R² = 0.72) further strengthens the identified pharmacophore features of IP₃R modulators by probing the presence of complementary hydrogen-bond donor and hydrogen-bond acceptor hotspots at a distance of 7.6-8.0 Å and 6.8-7.2 Å, respectively, from a hydrophobic hotspot at the virtual receptor site (VRS). The identified 3D structural features of IP₃R modulators were used to screen (virtual screening) 735,735 compounds from the ChemBridge database, 265,242 compounds from the National Cancer Institute (NCI) database, and 885 natural compounds from the ZINC database. After the application of filters, four compounds from ChemBridge, one compound from ZINC, and three compounds from NCI were shortlisted as potential hits (antagonists) against IP₃R. The identified hits could further assist in the design and optimization of lead structures for the targeting and remodeling of Ca²⁺ signals in cancer.

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.

Generation of Quantum Configurational Ensembles Using Approximate Potentials

Conformational analysis is of paramount importance in drug design: it is crucial to determine pharmacological properties, understand molecular recognition processes, and characterize the conformations of ligands when unbound. Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC) and molecular dynamics (MD), are usually employed to generate ensembles of structures due to their ability to extensively sample the conformational space of molecules. The accuracy of these MM-based schemes strongly depends on the functional form of the force field (FF) and its parametrization, components that often hinder their performance. High-level methods, such as ab initio MD, provide reliable structural information but are still too computationally expensive to allow for extensive sampling. Therefore, to overcome these limitations, we present a multilevel MC method that is capable of generating quantum configurational ensembles while keeping the computational cost at a minimum. We show that FF reparametrization is an efficient route to generate FFs that reproduce QM results more closely, which, in turn, can be used as low-cost models to achieve the gold standard QM accuracy. We demonstrate that the MC acceptance rate is strongly correlated with various phase space overlap measurements and that it constitutes a robust metric to evaluate the similarity between the MM and QM levels of theory. As a more advanced application, we present a self-parametrizing version of the algorithm, which combines sampling and FF parametrization in one scheme, and apply the methodology to generate the QM/MM distribution of a ligand in aqueous solution.

Development and Validation of AMBER-FB15-Compatible Force Field Parameters for Phosphorylated Amino Acids

Phosphorylation of select amino acid residues is one of the most common biological mechanisms for regulating protein structures and functions. While computational modeling can be used to explore the detailed structural changes associated with phosphorylation, most molecular mechanics force fields developed for the simulation of phosphoproteins have been noted to be inconsistent with experimental data. In this work, we parameterize force fields for the phosphorylated forms of the amino acids serine, threonine, and tyrosine using the ForceBalance software package with the goal of improving agreement with experiments for these residues. Our optimized force field, denoted as FB18, is parameterized using high-quality ab initio potential energy scans and is designed to be fully compatible with the AMBER-FB15 protein force field. When utilized in MD simulations together with the TIP3P-FB water model, we find that FB18 consistently enhances the prediction of experimental quantities such as ³J NMR couplings and intramolecular hydrogen-bonding propensities in comparison to previously published models. As was reported with AMBER-FB15, we also see improved agreement with the reference QM calculations in regions at and away from local minima. We thus believe that the FB18 parameter set provides a promising route for the further investigation of the varied effects of protein phosphorylation.

High-efficiency charge transfer on SERS-active semiconducting K2Ti6O13 nanowires enables direct transition of photoinduced electrons to protein redox centers

Photoinduced charge transfer (PICT) plays a crucial role in the chemical mechanism of surface-enhanced Raman scattering (SERS), in which small organic molecules are generally used as probes. Herein, semiconducting K₂Ti₆O₁₃ nanowires (NWs) are synthesized and are found to exhibit high SERS activity probed by 4-mercaptobenzoic acid (4-MBA). Density functional theory (DFT) calculations reveal high-efficiency CT on the K₂Ti₆O₁₃ nanowires. Furthermore, PICT on the K₂Ti₆O₁₃ NWs is for the first time evidenced by a redox protein, cytochrome c (Cyt c). Under optimized experimental conditions, the transformation of oxidized Cyt c to its reduced state clearly verifies the electron transfer (ET) from the K₂Ti₆O₁₃ nanowire to the protein. The ET mechanism is explored based on energy levels of semiconductors and molecular dynamics simulations, thus revealing the importance of energy level matching and electron tunneling from the semiconductor surface to the redox center. This study indicates a great potential of multiple-layered K₂Ti₆O₁₃ NWs in the application of SERS on semiconducting materials and more importantly, it provides a new route for the rational design of protein-semiconductor interfaces for investigating electron transfer processes of redox proteins and biocatalytic reactions.

Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field

We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

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.

Mebendazole Mediates Proteasomal Degradation of GLI Transcription Factors in Acute Myeloid Leukemia

The prognosis of elderly AML patients is still poor due to chemotherapy resistance. The Hedgehog (HH) pathway is important for leukemic transformation because of aberrant activation of GLI transcription factors. MBZ is a well-tolerated anthelmintic that exhibits strong antitumor effects. Herein, we show that MBZ induced strong, dose-dependent anti-leukemic effects on AML cells, including the sensitization of AML cells to chemotherapy with cytarabine. MBZ strongly reduced intracellular protein levels of GLI1/GLI2 transcription factors. Consequently, MBZ reduced the GLI promoter activity as observed in luciferase-based reporter assays in AML cell lines. Further analysis revealed that MBZ mediates its anti-leukemic effects by promoting the proteasomal degradation of GLI transcription factors via inhibition of HSP70/90 chaperone activity. Extensive molecular dynamics simulations were performed on the MBZ-HSP90 complex, showing a stable binding interaction at the ATP binding site. Importantly, two patients with refractory AML were treated with MBZ in an off-label setting and MBZ effectively reduced the GLI signaling activity in a modified plasma inhibitory assay, resulting in a decrease in peripheral blood blast counts in one patient. Our data prove that MBZ is an effective GLI inhibitor that should be evaluated in combination to conventional chemotherapy in the clinical setting.

Machine Learning Directed Optimization of Classical Molecular Modeling Force Fields

Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow.