Molecular modeling of organic and biomolecular systems using BOSS and MCPRO

Monte Carlo simulations for free energies of hydration: Past to present

A summary of the development of Monte Carlo statistical mechanics simulations for the computation of free energies of hydration of organic molecules is followed by presentation of results with the latest version of the optimized potentials for liquid simulations-all atom force field and the TIP4P water model. Scaling of the Lennard-Jones interactions between water, oxygen, and carbon atoms by a factor of 1.25 is found to improve the accuracy of free energies of hydration for 50 prototypical organic molecules from a mean unsigned error of 1.0-1.2 to 0.4 kcal/mol.

PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics

PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100 ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.

Synthesis, Molecular Electron Density Theory Study, Molecular Docking, and Pharmacological Evaluation of New Coumarin-Sulfonamide-Nitroindazolyl-Triazole Hybrids as Monoamine Oxidase Inhibitors

Inhibitors of monoamine oxidases (MAOs) are of interest for the treatment of neurodegenerative disorders and other human pathologies. In this frame, the present work describes different synthetic strategies to obtain MAO inhibitors via the coupling of the aminocoumarin core with arylsulfonyl chlorides followed by copper azide-alkyne cycloaddition, leading to coumarin-sulfonamide-nitroindazolyl-triazole hybrids. The nitration position on the coumarin moiety was confirmed through nuclear magnetic resonance spectroscopy and molecular electron density theory in order to elucidate the molecular mechanism and selectivity of the electrophilic aromatic substitution reaction. The coumarin derivatives were evaluated for their inhibitory potency against monoamine oxidases and cholinesterases. Molecular docking calculations provided a rational binding mode of the best compounds in the series with MAO A and B. The work identified hybrids 14a-c as novel MAO inhibitors, with a selective action against isoform B, of potential interest to combat neurological diseases.

Computational Screening of Hydrophobic Zeolites for the Removal of Emerging Organic Contaminants from Water

The pollution of water resources by pharmaceuticals and agents of personal care products (PPCPs) poses an increasingly pressing issue that has received considerable attention from scientists and government agencies alike. Hydrophobic zeolites can serve as selective adsorbents to remove these contaminants from aqueous solution. So far, the adsorption of PPCPs in zeolites has often been investigated in case studies focusing on a small number of contaminants and one or a few zeolites. We present a computational screening approach to investigate the interaction of 53 PPCPs with 14 all-silica zeolites, aiming at a more comprehensive understanding of factors that are beneficial for a strong host-guest interaction and thus an efficient adsorption. The systems are modelled on the classical force field level of theory, allowing for the efficient computational treatment of a large number of PPCP-zeolite combinations and evaluated in terms of the interaction energy between PPCP and zeolite framework. For selected PPCP-zeolite combinations additional Free Energy Perturbation simulations are employed to compute Free Energies of Transfer between the aqueous phase and the adsorbed state. These results can serve as a starting point for experimental studies of relevant PPCP-zeolite combination or more in-depth theoretical investigations.

Exploring Biomolecular Conformational Dynamics with Polarizable Force Field AMOEBA and Enhanced Sampling Method Milestoning

Conformational dynamics play a crucial role in determining the behavior of the biomolecules. Polarizable force fields, such as AMOEBA, can accurately capture electrostatic interactions underlying the conformational space. However, applying a polarizable force field in molecular dynamics (MD) simulations can be computationally expensive, especially in studying long-time-scale dynamics. To overcome this challenge, we incorporated the AMOEBA potential with Milestoning, an enhanced sampling method in this work. This integration allows us to efficiently sample the rare and important conformational states of a biomolecule by using many short and independent molecular dynamics trajectories with the AMOEBA force field. We applied this method to investigate the conformational dynamics of alanine dipeptide, DNA, and RNA A-B form conversion. Well-converged thermodynamic and kinetic properties were obtained, including the free energy difference, mean first passage time, and critical transitions between states. Our results demonstrate the power of integrating polarizable force fields with enhanced sampling methods in quantifying the thermodynamic and kinetic properties of biomolecules at the atomic level.

Interaction of Norsecurinine-Type Oligomeric Alkaloids with α-Tubulin: A Molecular Docking Study

The medicinal plant Securinega virosa (Roxb ex. Willd) Baill., also known as Flueggea virosa (Roxb. ex Willd.) Royle, is commonly used in traditional medicine in Africa and Asia for the management of diverse pathologies, such as parasite infections, diabetes, and gastrointestinal diseases. Numerous alkaloids have been isolated from the twigs and leaves of the plant, notably a variety of oligomeric indolizidine alkaloids derived from the monomers securinine and norsecurinine which both display anticancer properties. The recent discovery that securinine can bind to tubulin and inhibit microtubule assembly prompted us to investigate the potential binding of two series of alkaloids, fluevirosines A-H and fluevirosinine A-J, with the tubulin dimer by means of molecular modeling. These natural products are rare high-order alkaloids with tri-, tetra-, and pentameric norsecurinine motifs. Despite their large size (up to 2500 Å3), these alkaloids can bind easily to the large drug-binding cavity (about 4800 Å3) on α-tubulin facing the β-tubulin unit. The molecular docking analysis suggests that these hydrophobic macro-alkaloids can form stable complexes with α/β-tubulin. The tubulin-binding capacity varies depending on the alkaloid size and structure. Structure-binding relationships are discussed. The docking analysis identifies the trimer fluevirosine D, tetramer fluevirosinine D, and pentamer fluevirosinine H as the most interesting tubulin ligands in the series. This study is the first to propose a molecular target for these atypical oligomeric Securinega alkaloids.

A molecular docking exploration of the large extracellular loop of tetraspanin CD81 with small molecules

Tetraspanin CD81 is a transmembrane protein used as a co-receptor by different viruses and implicated in some cancer and inflammatory diseases. The design of therapeutic small molecules targeting CD81 lags behind monoclonal antibodies and peptides but different synthetic and natural products binding to CD81 have been identified. We have investigated the interaction between synthetic compounds and CD81, considering both the cholesterol-bound full-length receptor and a truncated protein corresponding to the large extracellular loop (LEL) of the tetraspanin. They represent the closed and open conformations of the protein, respectively. Stable complexes were characterized with bi-aryl compounds (notably the quinolinone-benzothiazole 6) and atypical molecules bearing a 1-amino-boraadamantane scaffold well adapted to interact with CD81 (5a-d). In each case, the mode of binding to CD81 was analyzed, the binding sites identified and the molecular contacts determined. The narrow intra-LEL binding site of CD81 can accommodate the elongated bi-aryl 6 but not a series of isosteric compounds with a bis(bicyclic) scaffold. The bora-adamantane derivatives appeared to bind well to CD81, but essentially to the external surface of the protein loop. The binding selectivity of the compounds was assessed comparing binding to the LEL of tetraspanins CD81, CD9 and Tspan15. A net preference for CD81 over CD9 was evidenced, but the LEL of Tspan15 also provided a suitable binding site for the compounds, notably for the bora-adamantane derivatives. This work provides an aid to the identification and design of tetraspanin-binding small molecules, underlining the distinct behavior of the open and closed conformation of the protein for drug binding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00203-6.

Enthalpies and entropies of hydration from Monte Carlo simulations

The changes in free energy, enthalpy, and entropy for transfer of a solute from the gas phase into solution are the fundamental thermodynamic quantities that characterize the solvation process. Owing to the development of methods based on free-energy perturbation theory, computation of free energies of solvation has become routine in conjunction with Monte Carlo (MC) statistical mechanics and molecular dynamics (MD) simulations. Computation of the enthalpy change and by inference the entropy change is more challenging. Two methods are considered in this work corresponding to direct averaging for the solvent and solution and to computing the temperature derivative of the free energy in the van't Hoff approach. The application is for neutral organic solutes in TIP4P water using long MC simulations to improve precision. Definitive results are also provided for pure TIP4P water. While the uncertainty in computed free energies of hydration is ca. 0.05 kcal mol-1, it is ca. 0.4 kcal mol-1 for the enthalpy changes from either van't Hoff plots or the direct method with sampling for 5 billion MC configurations. Partial molar volumes of hydration are also computed by the direct method; they agree well with experimental data with an average deviation of 3 cm3 mol-1. In addition, the results permit breakdown of the errors in the free energy changes from the OPLS-AA force field into their enthalpic and entropic components. The excess hydrophobicity of organic solutes is enthalpic in origin.

Unveiling tetrahydroquinolines as promising BVDV entry inhibitors: Targeting the envelope protein

Bovine viral diarrhea virus (BVDV) is known to cause financial losses and decreased productivity in the cattle industry worldwide. Currently, there are no available antiviral treatments for effectively controlling BVDV infections in laboratories or farms. The BVDV envelope protein (E2) mediates receptor recognition on the cell surface and is required for fusion of virus and cell membranes after the endocytic uptake of the virus during the entry process. Therefore, E2 is an attractive target for the development of antiviral strategies. To identify BVDV antivirals targeting E2 function, we defined a binding site in silico located in domain IIIc at the interface between monomers in the disulfide linked dimer of E2. Employing a de novo design methodology to identify compounds with the potential to inhibit the E2 function, compound 9 emerged as a promising candidate with remarkable antiviral activity and minimal toxicity. In line with targeting of E2 function, compound 9 was found to block the virus entry into host cells. Furthermore, we demonstrated that compound 9 selectively binds to recombinant E2 in vitro. Molecular dynamics simulations (MD) allowed describing a possible interaction pattern between compound 9 and E2 and indicated that the S enantiomer of compound 9 may be responsible for the antiviral activity. Future research endeavors will focus on synthesizing enantiomerically pure compounds to further support these findings. These results highlight the usefulness of de novo design strategies to identify a novel class of BVDV inhibitors that block E2 function inhibiting virus entry into the host cell.

OPLS/2020 Force Field for Unsaturated Hydrocarbons, Alcohols, and Ethers

The OPLS all-atom force field was updated and applied to modeling unsaturated hydrocarbons, alcohols, and ethers. Testing has included gas-phase conformational energetics, properties of pure liquids, and free energies of hydration. Monte Carlo statistical mechanics (MC) calculations were used to model 60 liquids. In addition, a robust, automated procedure was devised to compute the free energies of hydration with high precision via free-energy perturbation (FEP) calculations using double annihilation. Testing has included larger molecules than in the past, and parameters are reported for the first time for some less common groups including alkynes, allenes, dienes, and acetals. The average errors in comparison with experimental data for the computed properties of the pure liquids were improved with the modified force field (OPLS/2020). For liquid densities and heats of vaporization, the average unsigned errors are 0.01 g/cm3 and 0.2 kcal/mol. The average error and signed error for free energies of hydration are both 1.2 kcal/mol. As noted before, this reflects a systematic overestimate of the hydrophobicity of organic molecules when the parametrization is done to minimize the errors for properties of pure liquids. Implications for the modeling of biomolecular systems with standard force fields are considered.

Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease

Accurate, routine calculation of absolute binding free energies (ABFEs) for protein-ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (Mpro) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 μs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.

Nonfluorinated Antisolvents for Ultrastable Potassium-Ion Batteries

A robust interface between the electrode and electrolyte is essential for the long-term cyclability of potassium-ion batteries (PIBs). An effective strategy for achieving this objective is to enhance the formation of an anion-derived, robust, and stable solid-electrolyte interphase (SEI) via electrolyte structure engineering. Herein, inspired by the application of antisolvents in recrystallization, we propose a nonfluorinated antisolvent strategy to optimize the electrolyte solvation structure. In contrast to the conventional localized superconcentrated electrolyte introducing high-fluorinated ether solvent, the anion-cation interaction is considerably enhanced by introducing a certain amount of nonfluorinated antisolvent into a phosphate-based electrolyte, thereby promoting the formation of a thin and stable SEI to ensure excellent cycling performance of PIBs. Consequently, the nonfluorinated antisolvent electrolyte exhibits superior stability in the K||graphite cell (negligible capacity degradation after 1000 cycles) and long-term cycling in the K||K symmetric cell (>2200 h), as well as considerably improved oxidation stability. This study demonstrates the feasibility of optimized electrolyte engineering with a nonfluorinated antisolvent, providing an approach to realizing superior electrochemical energy storage systems in PIBs.

Protein compactness and interaction valency define the architecture of a biomolecular condensate across scales

Non-membrane-bound biomolecular condensates have been proposed to represent an important mode of subcellular organization in diverse biological settings. However, the fundamental principles governing the spatial organization and dynamics of condensates at the atomistic level remain unclear. The Saccharomyces cerevisiae Lge1 protein is required for histone H2B ubiquitination and its N-terminal intrinsically disordered fragment (Lge11-80) undergoes robust phase separation. This study connects single- and multi-chain all-atom molecular dynamics simulations of Lge11-80 with the in vitro behavior of Lge11-80 condensates. Analysis of modeled protein-protein interactions elucidates the key determinants of Lge11-80 condensate formation and links configurational entropy, valency, and compactness of proteins inside the condensates. A newly derived analytical formalism, related to colloid fractal cluster formation, describes condensate architecture across length scales as a function of protein valency and compactness. In particular, the formalism provides an atomistically resolved model of Lge11-80 condensates on the scale of hundreds of nanometers starting from individual protein conformers captured in simulations. The simulation-derived fractal dimensions of condensates of Lge11-80 and its mutants agree with their in vitro morphologies. The presented framework enables a multiscale description of biomolecular condensates and embeds their study in a wider context of colloid self-organization.

Structure Elucidation of Secondary Metabolites: Current Frontiers and Lingering Pitfalls

Analytical methods allow for the structure determination of submilligram quantities of complex secondary metabolites. This has been driven in large part by advances in NMR spectroscopic capabilities, including access to high-field magnets equipped with cryogenic probes. Experimental NMR spectroscopy may now be complemented by remarkably accurate carbon-13 NMR calculations using state-of-the-art DFT software packages. Additionally, microED analysis stands to have a profound effect on structure elucidation by providing X-ray-like images of microcrystalline samples of analytes. Nonetheless, lingering pitfalls in structure elucidation remain, particularly for isolates that are unstable or highly oxidized. In this Account, we discuss three projects from our laboratory that highlight nonoverlapping challenges to the field, with implications for chemical, synthetic, and mechanism of action studies. We first discuss the lomaiviticins, complex unsaturated polyketide natural products disclosed in 2001. The original structures were derived from NMR, HRMS, UV-vis, and IR analysis. Owing to the synthetic challenges presented by their structures and the absence of X-ray crystallographic data, the structure assignments remained untested for nearly two decades. In 2021, the Nelson group at Caltech carried out microED analysis of (-)-lomaiviticin C, leading to the startling discovery that the original structure assignment of the lomaiviticins was incorrect. Acquisition of higher-field (800 MHz 1H, cold probe) NMR data as well as DFT calculations provided insights into the basis for the original misassignment and lent further support to the new structure identified by microED. Reanalysis of the 2001 data set reveals that the two structure assignments are nearly indistinguishable, underscoring the limitations of NMR-based characterization. We then discuss the structure elucidation of colibactin, a complex, nonisolable microbiome metabolite implicated in colorectal cancer. The colibactin biosynthetic gene cluster was detected in 2006, but owing to colibactin's instability and low levels of production, it could not be isolated or characterized. We used a combination of chemical synthesis, mechanism of action studies, and biosynthetic analysis to identify the substructures in colibactin. These studies, coupled with isotope labeling and tandem MS analysis of colibactin-derived DNA interstrand cross-links, ultimately led to a structure assignment for the metabolite. We then discuss the ocimicides, plant secondary metabolites that were studied as agents against drug-resistant P. falciparum. We synthesized the core structure of the ocimicides and found significant discrepancies between our experimental NMR spectroscopic data and that reported for the natural products. We determined the theoretical carbon-13 NMR shifts for 32 diastereomers of the ocimicides. These studies indicated that a revision of the connectivity of the metabolites is likely needed. We end with some thoughts on the frontiers of secondary metabolite structure determination. As modern NMR computational methods are straightforward to execute, we advocate for their systematic use in validating the assignments of novel secondary metabolites.

N-glycosylation reinforces interaction of immune checkpoint TIM-3 with a small molecule ligand

N-glycosylation of eukaryotic proteins plays roles in protein folding, trafficking, and signal transduction. The biological influence of the process is well understood, whereas the pharmacological impact of protein N-glycosylation is not well under discerned. The role of N-glycosylation on drug binding to protein has been rarely studied. We have modeled the influence of a bi-antennary N-glycan introduced at position N78 on the immune checkpoint TIM-3 (T cell immunoglobulin domain and mucin domain-containing molecule 3) on the interaction with a selective drug antagonist. The bulky N-glycan introduced at the consensus sequence Asn-Val-Thr has no influence on drug binding when the glycan adopts an extended conformation. But in a folded conformation, the glycan can interact directly with the triazoloquinazolinone derivative so as to further stabilize the drug-TIM-3 complex. The non-fucosylated glycan at position N78 markedly consolidates the drug interaction, via an additional H-bond interaction with the α3-mannose residue. It provides a gain of empirical potential energy of interaction (ΔE) of about 30 %. The presence of a more rigid fucosylated N-glycan is a little less favorable, with a gain of ΔE of about 20 %. The folded N-glycan appears to protect the ligand bound to the protein cavity, with the tricyclic core of the heterocyclic molecule sandwiched between two indole rings of tryptophan residues. Similar results were obtained when using a biantennary disialyl N-glycan with a bisecting GlcNAc residue and a tetra-antennary N-glycan. The molecular models illustrate the drug-stabilizing capacity of a bulky N-glycan positioned at a validated glycosylation site (N78 corresponding to N100 for the full-length protein). The modeling approach is useful to delineate further the role of the N-glycan of the immune checkpoint TIM-3 in interaction with small molecule ligands, and to guide the design of more potent compounds. The approach is transposable to other proteins to better comprehend the influence of N-glycans on drug-receptor interactions.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Conformations of polyolefins on platinum catalysts control product distribution in plastics recycling

The design of catalysts for the chemical recycling of plastic waste will benefit greatly from an intimate knowledge of the interfacial polymer-catalyst interactions that determine reactant and product distributions. Here, we investigate backbone chain length, side chain length, and concentration effects on the density and conformation of polyethylene surrogates at the interface with Pt(111) and relate them to experimental product distributions resulting from carbon-carbon bond cleavage. Using replica-exchange molecular dynamics simulations, we characterize the polymer conformations at the interface by the distributions of trains, loops, and tails and their first moments. We find that the preponderance of short chains, in the range of 20 carbon atoms, lies entirely on the Pt surface, whereas longer chains exhibit much broader distributions of conformational features. Remarkably, the average length of trains is independent of the chain length but can be tuned via the polymer-surface interaction. Branching profoundly impacts the conformations of long chains at the interface as the distributions of trains become less dispersed and more structured, localized around short trains, with the immediate implication of a wider carbon product distribution upon C-C bond cleavage. The degree of localization increases with the number and size of the side chains. Long chains can adsorb from the melt onto the Pt surface even in melt mixtures containing shorter polymer chains at high concentrations. We confirm experimentally key computational findings and demonstrate that blends may provide a strategy to reduce the selectivity for undesired light gases.

Interaction of Camptothecin Anticancer Drugs with Ribosomal Proteins L15 and L11: A Molecular Docking Study

The antitumor drug topotecan (TPT) is a potent inhibitor of topoisomerase I, triggering DNA breaks lethal for proliferating cancer cells. The mechanism is common to camptothecins SN38 (the active metabolite of irinotecan) and belotecan (BLT). Recently, TPT was shown to bind the ribosomal protein L15, inducing an antitumor immune activation independent of topoisomerase I. We have modeled the interaction of four camptothecins with RPL15 derived from the 80S human ribosome. Two potential drug-binding sites were identified at Ile135 and Phe129. SN38 can form robust RPL15 complexes at both sites, whereas BLT essentially gave stable complexes with site Ile135. The empirical energy of interaction (ΔE) for SN38 binding to RPL15 is similar to that determined for TPT binding to the topoisomerase I-DNA complex. Molecular models with the ribosomal protein L11 sensitive to topoisomerase inhibitors show that SN38 can form a robust complex at a single site (Cys25), much more stable than those with TPT and BLT. The main camptothecin structural elements implicated in the ribosomal protein interaction are the lactone moiety, the aromatic system and the 10-hydroxyl group. The study provides guidance to the design of modulators of ribosomal proteins L11 and L15, both considered anticancer targets.

Supramolecular Recognition of Phosphodiester-Based Donor and Acceptor Oligomers Forming Gels in Water

Inspired by automated DNA synthesis, electron-rich dialkoxynaphthalene (DAN) donor and electron-deficient naphthalene-tetracarboxylic diimide (NDI) acceptor phosphodiester-linked homohexamers were synthesized by the phosphoramidite method. Two types of hexamers were prepared, one with only one phosphodiester between the aromatics (i.e., DAN or NDI) and a second with two phosphodiesters around a propanediol between the aromatics, leading to the latter more flexible and more hydrophilic hexamers. The folding properties of these homohexamers alone or mixed together, in water only, were studied by UV-visible absorption spectroscopy and atomic force microscopy (AFM). AFM imaging revealed that a 1:1 mixture of hexaDAN and hexaNDI formed fibers by charge transfer donor-acceptor recognition leading to a hydrogel after drying. The organization of the resulting structures is strongly dependent on the nature of the complementary partner, leading to the formation of mono- or multilayer hydrogel networks with different compactness.

Addressing the Structural Organization of Silicone Alternatives in Formulations by Molecular Dynamics Simulations and a Novel Equilibration Protocol

The world of cosmetics is an always-evolving field with constant updates on its formulation components. The current reality asks for an ever-increasing need for natural and sustainable replacements for synthetic compounds in all fields of modern consumer products. However, the research and development stages of finding these alternatives can be an expensive, time-consuming, and often wasteful process that turns this task into a laborious procedure. This study introduces the development of a computational methodology that will aid the research of silicone alternatives, disclosing their structural performance in a formulation. Additionally, an equilibration protocol was developed to measure the distribution and densities of these silicone alternatives to determine how they behave in relation to their counterparts, using molecular dynamics simulations. Two systems were tested, A and B, where the former is composed of one ester (Dipentaerythrityl Hexa C5 Acid Ester) and the latter by an ester combined with an alkane (Triheptanoin and C13-Isoalkane); all three molecules are commercially available and widely used. Both systems were subjected to a 3-step thermal regulation strategy. The systems went through an initial simulation at 25 °C and at 70 °C, then a temperature switch took place (25 °C « 70 °C), then a shock to 200 °C, and finally a Simulated Annealing protocol reaching 250 °C. In the end, all systems converged towards micelle-like structures. These results come to further ascertain the position of computational chemistry and Molecular Dynamics Simulations as an important part of R&D processes in modern sciences and investigation.

The slow but steady rise of binding free energy calculations in drug discovery

Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.

Molecular Docking of Cryptoconcatones to α-Tubulin and Related Pironetin Analogues

Cryptoconcatones A-L represent a series of 12 dihydropyrone derivatives isolated from the evergreen tree Cryptocarya concinna Hance, which is well distributed in southeast Asia. The lead compound in the series, cryptoconcatone L, has revealed antiproliferative activity against cultured cancer cells but its mechanism of action remains unknown. Based on a structural analogy with the anticancer natural product pironetin, which is well known for binding covalently to α-tubulin and for functioning as a microtubule polymerization inhibitor, we investigated the interaction of cryptoconcatones with tubulin dimers using molecular docking. The α-tubulin binding capacity of each compound was quantified (through calculation of the empirical energy of interaction ΔE) and structure-binding relationships were delineated. Two compounds were found to interact with α-tubulin much more potently than pironetin: cryptoconcatones F and L. In both cases, the facile formation of a covalent bond with Cys316 was evidenced, as observed with the parent compound pironetin. A few other pironetin analogues were investigated, including spicigerolide, which is an analogue of another known α-tubulin binder. Altogether, this study points to the identification of a series of 5,6-dihydro-α-pyrones as α-tubulin-binding agents. The study contributes to a better understanding of the mechanism of action of cryptoconcatones and should help the design of analogues targeting the pironetin site of α-tubulin.

Interaction of the renin inhibitor aliskiren with the SARS-CoV-2 main protease: a molecular docking study

The renin protein is an upstream enzymatic regulator of the renin-aldosterone-angiotensin system (RAAS) essential for the maintenance of blood pressure. The angiotensin-converting enzyme-2 (ACE2) is a major component of the RAAS and a cell surface receptor exploited by the SARS-CoV-2 virus to enter host cells. A recent molecular modeling study has revealed that the direct renin peptide inhibitor remikiren can bind to the catalytic site of SARS-CoV-2 main protease (M^pro). By analogy, we postulated that the non-peptidic drug aliskiren, a more potent renin inhibitor than remikiren and a drug routinely used to treat hypertension, may also be able to interact with M^pro. An in silico comparison of the binding of the two compounds to M^pro indicates that aliskiren (ΔE = -75.9 kcal/mol) can form stable complexes with the main viral protease, binding to the active site, as remikiren (ΔE = -83.2 kcal/mol). The comparison with a panoply of 30 references compounds (mainly antiviral drugs) indicated that remikiren is a potent M^pro binder comparable to drugs like glecaprevir and pibrentasvir (ΔE = -96.5 kcal/mol). The energy of interaction (ΔE) of aliskiren with M^pro is about 10% lower than with remikiren, comparable to that calculated with drugs like velpatasvir and sofosbuvir. A model is proposed to define the drug binding site, with the best binders (including remikiren) penetrating deeply into the site, whereas the less potent binders (including aliskiren) interact more superficially with the protein.Communicated by Ramaswamy H. Sarma.

Recent PELE Developments and Applications in Drug Discovery Campaigns

Computer simulation techniques are gaining a central role in molecular pharmacology. Due to several factors, including the significant improvements of traditional molecular modelling, the irruption of machine learning methods, the massive data generation, or the unlimited computational resources through cloud computing, the future of pharmacology seems to go hand in hand with in silico predictions. In this review, we summarize our recent efforts in such a direction, centered on the unconventional Monte Carlo PELE software and on its coupling with machine learning techniques. We also provide new data on combining two recent new techniques, aquaPELE capable of exhaustive water sampling and fragPELE, for fragment growing.

Highly potent natural fungicides identified in silico against the cereal killer fungus Magnaporthe oryzae

Magnaporthe oryzae is one of the most notorious fungal pathogens that causes blast disease in cereals, and results in enormous loss of grain production. Many chemical fungicides are being used to control the pathogen but none of them are fully effective in controlling blast disease. Therefore, there is a demand for the discovery of a new natural biofungicide to manage the blast disease efficiently. A large number of new natural products showed inhibitory activities against M. oryzae in vitro. To find out effective biofungicides, we performed in silico molecular docking analysis of some of the potent natural compounds targeting four enzymes namely, scytalone dehydratase, SDH1 (PDB ID:1STD), trihydroxynaphthalene reductase, 3HNR (PDB ID:1YBV), trehalose-6-phosphate synthase, Tps1 (PDB ID:6JBI) and isocitrate lyase, ICL1 (PDB ID:5E9G) of M. oryzae fungus that regulate melanin biosynthesis and/or appresorium formation. Thirty-nine natural compounds that were previously reported to inhibit the growth of M. oryzae were subjected to rigid and flexible molecular docking against aforementioned enzymes followed by molecular dynamic simulation. The results of virtual screening showed that out of 39, eight compounds showed good binding energy with any one of the target enzymes as compared to reference commercial fungicides, azoxystrobin and strobilurin. Among the compounds, camptothecin, GKK1032A2 and chaetoviridin-A bind with more than one target enzymes of M. oryzae. All of the compounds except tricyclazole showed good bioactivity score. Taken together, our results suggest that all of the eight compounds have the potential to develop new fungicides, and remarkably, camptothecin, GKK1032A2 and chaetoviridin-A could act as multi-site mode of action fungicides against the blast fungus M. oryzae.

Hydrophilic Versus Hydrophobic Coupling in the Pressure Dependence of the Chemical Potential of Alkali Metal and Halide Ions in Water

We computed the chemical potential for some alkali metal ions (K⁺, Rb⁺, and Cs⁺) and two halide ions (Br^- and I^-) in aqueous solution at ambient T and various pressures in the range 1-8000 atm. Results were obtained from classic Monte Carlo simulations in the NPT ensemble by means of the free energy perturbation method. Here, the chemical potential is computed as the sum of a term relative to a Lennard-Jones solute and a term relative to the process in which this solute is transformed into the ion. Hydrophobic and hydrophilic features of these two components of the chemical potential show opposite behaviors under isothermal compression. The increase in pressure determines an increase in the hydrophobic component, which becomes more positive with a stronger effect for larger ions. Correspondingly, the values of the hydrophilic component become more negative for alkali ions, whereas they are only slightly affected by compression for halide ions. Hydrophobic-hydrophilic quasi-compensation in the slopes is observed for Rb⁺. For a smaller ion, such as K⁺, the dependence on pressure of the hydrophilic component is slightly dominant. For a larger ion, as observed in the cases of Cs⁺, Br^-, and I^-, the hydrophobic component assumes the determinant role. Pressure dependence of the chemical potential is little affected by corrections introduced for molecular potential truncation. This view can change for possible boundary artifacts that could have affected the static electrostatic potential. Some inference is obtained from comparison with experimental data at 1 atm on the free energy of hydration. Discrepancies show the characteristic asymmetry between cations and anions. The further addition of a correction based on the static potential significantly reduces these discrepancies with important error cancellation on the sum of chemical potentials of ions of opposite charge. The correction is applied also at higher pressures, and results are compared with those obtained by adding an alternative correction that is based on the water number density. Regardless of the ion, changes of the chemical potential induced by an increase in pressure appear to be dominated by the hydrophobic component, in particular when using the alternative correction. For bromide and iodide electrolytes, the two corrections give chemical potentials in good agreement.

Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules

Force fields for drug-like small molecules play an essential role in molecular dynamics simulations and binding free energy calculations. In particular, the accurate generation of partial charges on small molecules is critical to understanding the interactions between proteins and drug-like molecules. However, it is a time-consuming process. Thus, we generated a force field for small molecules and employed a machine learning (ML) model to rapidly predict partial charges on molecules in less than a minute of time. We performed density functional theory (DFT) calculation for 31770 small molecules that covered the chemical space of drug-like molecules. The partial charges for the atoms in a molecule were predicted using an ML model trained on DFT-based atomic charges. The predicted values were comparable to the charges obtained from DFT calculations. The ML model showed high accuracy in the prediction of atomic charges for external test data sets. We also developed neural network (NN) models to assign atom types, phase angles and periodicities. All the models performed with high accuracy on test data sets. Our code calculated all the descriptors that were needed for the prediction of force field parameters and produced topologies for small molecules by combining results from ML and NN models. To assess the accuracy of the predicted force field parameters, we calculated solvation free energies for small molecules, and the results were in close agreement with experimental free energies. The AI-generated force field was effective in the fast and accurate generation of partial charges and other force field parameters for small drug-like molecules.

Binding of Vialinin A and p-Terphenyl Derivatives to Ubiquitin-Specific Protease 4 (USP4): A Molecular Docking Study

The para-terphenyl derivative vialinin A (Vi-A), isolated from Thelephora fungi, has been characterized as a potent inhibitor of the ubiquitin-specific protease 4 (USP4). Blockade of USP4 contributes to the anti-inflammatory and anticancer properties of the natural product. We have investigated the interaction of Vi-A with USP4 by molecular modeling, to locate the binding site (around residue V98 within the domain in USP segment) and to identify the binding process and interaction contacts. From this model, a series of 32 p-terphenyl compounds were tested as potential USP4 binders, mainly in the vialinin, terrestrin and telephantin series. We identified 11 compounds presenting a satisfactory USP4 binding capacity, including two fungal products, vialinin B and aurantiotinin A, with a more favorable empirical energy of USP4 interaction (ΔE) than the reference product Vi-A. The rare p-terphenyl aurantiotinin A, isolated from the basidiomycete T. aurantiotincta, emerged as a remarkable USP4 binder. Structure-binding relationships have been identified and discussed, to guide the future design of USP4 inhibitors based on the p-terphenyl skeleton. The docking study should help the identification of other protease inhibitors from fungus.

Interaction of panduratin A and derivatives with the SARS-CoV-2 main protease (mpro): a molecular docking study

Panduratin A (Pa-A) is a prenylated cyclohexenyl chalcone isolated from the rhizomes of the medicinal and culinary plant Boesenbergia rotunda (L.) Mansf., commonly called fingerroots. Both an ethanolic plant extract and Pa-A have shown a marked antiviral activity against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic disease. Pa-A functions as a protease inhibitor inhibiting infection of human cells by the virus. We have modeled the interaction of Pa-A, and 26 panduratin analogues with the main protease (M^pro) of SARS-CoV-2 using molecular docking. The natural product 4-hydroxypanduratin showed a higher M^pro binding capacity than Pa-A and isopanduratin A. The interaction with M^Pro of all known panduratin derivatives (Pa-A to Pa-Y) have been compared, together with more than 60 reference products. Three compounds emerged as potential robust M^Pro binders: Pa-R, Pa-V, Pa-S, with a binding capacity significantly higher than 4-OH-Pa-A and Pa-A. The empirical energy of interaction (ΔE) calculated with the best compound in the panduratin series, Pa-R bound to M^pro, surpassed that measured with the top reference protease inhibitors such a ruprintrivir, lufotrelvir, and glecaprevir. Structure-binding relationships are discussed. Compounds with a flavanone moiety (PA-R/S) are the best binders, better than those with a chromene unit (Pa-F/G). The extended molecules (such as Pa-V) exhibit good M^pro binding, but the dimeric compound Pa-Y is too long and protrudes outside the binding cavity. The work provides novel ideas to guide the design of new molecules interacting with M^pro.Communicated by Ramaswamy H. Sarma.

Refinement of the Optimized Potentials for Liquid Simulations Force Field for Thermodynamics and Dynamics of Liquid Alkanes

Torsion and Lennard-Jones parameters of the optimized potentials for liquid simulations (OPLS) all-atom force field have been refined for describing thermodynamics and dynamics of a wide range of liquid alkanes. Monte Carlo statistical mechanics (MC) and molecular dynamics (MD) simulations were carried out. For thermodynamics properties, MC simulations with truncated electrostatic interactions performed very closely to MD simulations with a Verlet neighbor list and the particle mesh Ewald algorithm. The average errors in comparison with experimental data for computed properties were improved with the modified force field (OPLS/2020), especially for long-chain alkanes. For liquid densities, heats of vaporization, and free energies of hydration, the average errors are 0.01 g/cm³, 0.2 kcal/mol, and ca. 0.5 kcal/mol, respectively; significant gains were made for relative heats of vaporization of isomeric series. Results for self-diffusion coefficients also reproduce experimental data well for linear alkane liquids up to hexadecane. The new force field is suitable for use in improved modeling of myriad systems of importance in chemistry, biology, and materials science.

Insights on JAK2 Modulation by Potent, Selective, and Cell-Permeable Pseudokinase-Domain Ligands

JAK2 is a non-receptor tyrosine kinase that regulates hematopoiesis through the JAK-STAT pathway. The pseudokinase domain (JH2) is an important regulator of the activity of the kinase domain (JH1). V617F mutation in JH2 has been associated with the pathogenesis of various myeloproliferative neoplasms, but JAK2 JH2 has been poorly explored as a pharmacological target. In light of this, we aimed to develop JAK2 JH2 binders that could selectively target JH2 over JH1 and test their capacity to modulate JAK2 activity in cells. Toward this goal, we optimized a diaminotriazole lead compound into potent, selective, and cell-permeable JH2 binders leveraging computational design, synthesis, binding affinity measurements for the JH1, JH2 WT, and JH2 V617F domains, permeability measurements, crystallography, and cell assays. Optimized diaminotriazoles are capable of inhibiting STAT5 phosphorylation in both WT and V617F JAK2 in cells.

Modified Hamiltonian in FEP Calculations for Reducing the Computational Cost of Electrostatic Interactions

The free-energy perturbation (FEP) method predicts relative and absolute free-energy changes of biomolecules in solvation and binding with other molecules. FEP is, therefore, one of the most essential tools in in silico drug design. In conventional FEP, to smoothly connect two thermodynamic states, the potential energy is modified as a linear combination of the end-state potential energies by introducing scaling factors. When the particle mesh Ewald is used for electrostatic calculations, conventional FEP requires two reciprocal-space calculations per time step, which largely decreases the computational performance. To overcome this problem, we propose a new FEP scheme by introducing a modified Hamiltonian instead of interpolation of the end-state potential energies. The scheme introduces nonuniform scaling into the electrostatic potential as used in Replica Exchange with Solute Tempering 2 (REST2) and does not require additional reciprocal-space calculations. We tested this modified Hamiltonian in FEP calculations in several biomolecular systems. In all cases, the calculated free-energy changes with the current scheme are in good agreement with those from conventional FEP. The modified Hamiltonian in FEP greatly improves the computational performance, which is particularly marked for large biomolecular systems whose reciprocal-space calculations are the major bottleneck of total computational time.

Molecular docking study of GSK-3β interaction with nomilin, kihadanin B, and related limonoids and triterpenes with a furyl-δ-lactone core

Glycogen synthase kinase-3β (GSK-3β) is a target enzyme considered for the treatment of multiple human diseases, from neurodegenerative pathologies to viral infections and cancers. Numerous inhibitors of GSK-3β have been discovered but thus far only a few have reached clinical trials and only one drug, tideglusib (1), has been registered. Natural products targeting GSK-3β have been identified, including the two anticancer limonoids obacunone (5) and gedunin (4), both presenting a furyl-δ-lactone core. To help identifying novel GSK-3β ligands, we have performed a molecular docking study with 15 complementary natural products bearing a furyl-δ-lactone unit (such as limonin (6) and kihadanins A (8) and B (9)) or a closely related structure (such as cedrelone (10) and nimbolide (11)). The formation of GSK-3β-binding complexes for those natural products was compared to reference GSK-3β ATP-competitive inhibitors LY2090314 (3) and AR-A014418 (2). Our in silico analysis led to the identification of two new GSK-3β-binding natural products: kihadanin B (9) and nomilin (7). The latter surpassed the reference compounds in terms of calculated empirical energy of interaction (ΔE). Nomilin (7) can possibly bind to the active site of GSK-3β, notably via the furyl-δ-lactone core and its 1-acetyl group, implicated in the protein interaction. Compound structure-binding relationships are discussed. The study should help the discovery of novel natural products targeting GSK-3β.

Development of OPLS-AA/M Parameters for Simulations of G Protein-Coupled Receptors and Other Membrane Proteins

G protein-coupled receptors (GPCRs) and other membrane proteins are valuable drug targets, and their dynamic nature makes them attractive systems for study with molecular dynamics (MD) simulations and free energy approaches. Here, we report the development, implementation, and validation of OPLS-AA/M force field parameters to enable simulations of these systems. These efforts include the introduction of post-translational modifications including lipidations and phosphorylation. We also modify previously reported parameters for lipids to be more consistent with the OPLS-AA force field standard and extend their coverage. These new parameters are validated on a variety of test systems, with the results compared to high-level quantum mechanics calculations, experimental data, and simulations with other force fields. The results demonstrate that the new parameters reliably reproduce the behavior of membrane protein systems.

Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.

The oxytocin signaling complex reveals a molecular switch for cation dependence

Oxytocin (OT) and vasopressin (AVP) are conserved peptide signaling hormones that are critical for diverse processes including osmotic homeostasis, reproduction, lactation and social interaction. OT acts through the oxytocin receptor (OTR), a magnesium-dependent G protein-coupled receptor that is a therapeutic target for treatment of postpartum hemorrhage, dysfunctional labor and autism. However, the molecular mechanisms that underlie OTR activation by OT and the dependence on magnesium remain unknown. Here we present the wild-type active-state structure of human OTR bound to OT and miniG_q/i determined by cryo-EM. The structure reveals a unique activation mechanism adopted by OTR involving both the formation of a Mg²⁺ coordination complex between OT and the receptor, and disruption of transmembrane helix 7 (TM7) by OT. Our functional assays demonstrate the role of TM7 disruption and provide the mechanism of full agonism by OT and partial agonism by OT analogs. Furthermore, we find that the identity of a single cation-coordinating residue across vasopressin family receptors determines whether the receptor is cation-dependent. Collectively, these results demonstrate how the Mg²⁺-dependent OTR is activated by OT, provide essential information for structure-based drug discovery efforts and shed light on the molecular determinants of cation dependence of vasopressin family receptors throughout the animal kingdom.

Plasticity in ligand recognition at somatostatin receptors

Somatostatin is a signaling peptide that plays a pivotal role in physiologic processes relating to metabolism and growth through its actions at somatostatin receptors (SSTRs). Members of the SSTR subfamily, particularly SSTR2, are key drug targets for neuroendocrine neoplasms, with synthetic peptide agonists currently in clinical use. Here, we show the cryogenic-electron microscopy structures of active-state SSTR2 in complex with heterotrimeric Gi3 and either the endogenous ligand SST14 or the FDA-approved drug octreotide. Complemented by biochemical assays and molecular dynamics simulations, these structures reveal key details of ligand recognition and receptor activation at SSTRs. We find that SSTR ligand recognition is highly diverse, as demonstrated by ligand-induced conformational changes in ECL2 and substantial sequence divergence across subtypes in extracellular regions. Despite this complexity, we rationalize several known sources of SSTR subtype selectivity and identify an additional interaction for specific binding. These results provide valuable insights for structure-based drug discovery at SSTRs.

Thymidylate synthase O-GlcNAcylation: a molecular mechanism of 5-FU sensitization in colorectal cancer

Alteration of O-GlcNAcylation, a dynamic posttranslational modification, is associated with tumorigenesis and tumor progression. Its role in chemotherapy response is poorly investigated. Standard treatment for colorectal cancer (CRC), 5-fluorouracil (5-FU), mainly targets Thymidylate Synthase (TS). TS O-GlcNAcylation was reported but not investigated yet. We hypothesize that O-GlcNAcylation interferes with 5-FU CRC sensitivity by regulating TS. In vivo, we observed that combined 5-FU with Thiamet-G (O-GlcNAcase (OGA) inhibitor) treatment had a synergistic inhibitory effect on grade and tumor progression. 5-FU decreased O-GlcNAcylation and, reciprocally, elevation of O-GlcNAcylation was associated with TS increase. In vitro in non-cancerous and cancerous colon cells, we showed that 5-FU impacts O-GlcNAcylation by decreasing O-GlcNAc Transferase (OGT) expression both at mRNA and protein levels. Reciprocally, OGT knockdown decreased 5-FU-induced cancer cell apoptosis by reducing TS protein level and activity. Mass spectrometry, mutagenesis and structural studies mapped O-GlcNAcylated sites on T251 and T306 residues and deciphered their role in TS proteasomal degradation. We reveal a crosstalk between O-GlcNAcylation and 5-FU metabolism in vitro and in vivo that converges to 5-FU CRC sensitization by stabilizing TS. Overall, our data propose that combining 5-FU-based chemotherapy with Thiamet-G could be a new way to enhance CRC response to 5-FU.

Interaction of obtusilactone B and related butanolide lactones with the barrier-to-autointegration factor 1 (BAF1). A computational study

The barrier-to-autointegration factor 1 (BAF1) protein is a DNA-binding protein implicated in nuclear envelop repair and reformation after mitosis. This nuclear protein is frequently overexpressed in cancer cells and plays a role in the occurrence and development of different tumors. It is a potential therapeutic target for gastric cancer, breast cancer and other malignancies. For this reason, BAF1 inhibitors are searched. The butanolide lactone obtusilactone B (Ob-B) has been found to inhibit VRK1-dependent phosphorylation of BAF1, upon direct binding to the nuclear protein. Taking advantage of the known crystallographic structure of BAF1, we have elaborated molecular models of Ob-B bound to BAF1 to delimit the binding site and binding configuration. The long endoolefinic alkyl side chain of Ob-B extends into a small groove on the protein surface, and the adjacent exomethylene-γ-lactone moiety occupies a pocket comprising to the Ser-4 phosphorylation site of BAF1. Twenty butanolide lactones structurally close to ObB were screened for BAF1 binding. Several natural products with BAF1-binding capacity potentially superior to Ob-B were identified, including mahubanolide, kotomolide B, epilitsenolide D2, and a few other known anticancer plant natural products. Our study provides new ideas to guide the discovery and design of BAF1 inhibitors.

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Obtusilactone B (Ob-B) is an anticancer inhibitor of VRK1-mediated BAF1 phosphorylation.

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Molecular models of Ob-B bound to BAF1 have been constructed and the binding site determined.

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Screening of 20 butanolide lactones led to the identification of new potential BAF1 binders.

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Mahubanolide, kotomolide B and epilitsenolide D2 emerge as potential BAF1 inhibitors.

Molecular docking study of britannin binding to PD-L1 and related anticancer pseudoguaianolide sesquiterpene lactones

The pseudoguaianolide-type sesquiterpene lactone (SL) britannin (BRT), found in different Inula species, has been characterized as a potent anticancer agent acting via modulation of the transcription factor NFkB and the Nrf2-Keap1 signaling pathway. In addition, a BRT-induced down-regulation of the immune checkpoint PD-L1 (programmed cell death ligand 1) expressed on cancer cells has been evidenced. Here we have performed a docking analysis of the direct binding of BRT to the PD-L1 protein, both in its monomeric and dimeric state. BRT appears to form stable complexes with PD-L1, with a preference for the dimeric form, binding at the interface of the two monomers. The calculated empirical energy of interaction (ΔE) value reaches -63.1 kcal/mol for the BRT-PD-L1 dimer complex, not far from the value calculated with the reference PD-L1 ligand BMS-202 (ΔE = -73.4 kcal/mol) under identical conditions. We also studied the potential PD-L1 dimer binding of 15 pseudoguaianolide sesquiterpene lactones analogues to BRT, including helenalin, gaillardin, bigelovin, coronopilin, and others. The docking analysis predicted that the SL chamissonolide (CHM) can also form equally stable complexes with PD-L1 dimer (ΔE = -64.8 kcal/mol). Preliminary compound structure-PD-L1 binding relationships have been delineated. This computational study supports the proposed interaction of BRT with PD-L1 and provides a guidance to the design of novel PD-L1 binders incorporating a SL-like tricyclic core unit.

Machine Learning-Assisted Hybrid ReaxFF Simulations

We have developed a machine learning (ML)-assisted Hybrid ReaxFF simulation method ("Hybrid/Reax"), which alternates reactive and non-reactive molecular dynamics simulations with the assistance of ML models to simulate phenomena that require longer time scales and/or larger systems than are typically accessible to ReaxFF. Hybrid/Reax uses a specialized tracking tool during the reactive simulations to further accelerate chemical reactions. Non-reactive simulations are used to equilibrate the system after the reactive simulation stage. ML models are used between reactive and non-reactive stages to predict non-reactive force field parameters of the system based on the updated bond topology. Hybrid/Reax simulation cycles can be continued until the desired chemical reactions are observed. As a case study, this method was used to study the cross-linking of a polyethylene (PE) matrix analogue (decane) with the cross-linking agent dicumyl peroxide (DCP). We were able to run relatively long simulations [>20 million molecular dynamics (MD) steps] on a small test system (4660 atoms) to simulate cross-linking reactions of PE in the presence of DCP. Starting with 80 PE molecules, more than half of them cross-linked by the end of the Hybrid/Reax cycles on a single Xeon processor in under 48 h. This simulation would take approximately 1 month if run with pure ReaxFF MD on the same machine.

Influence of water content on the [2σ+2σ+2π] cycloaddition of dimethyl azodicarboxylate with quadricyclane in mixed methanol-water solvents from QM/MM Monte Carlo simulations

Mixed quantum mechanics/molecular mechanics Monte Carlo (QM/MM/MC) simulations combined with the free energy perturbation (FEP) theory have been performed to investigate the mechanism and solvent effect of the [2σ+2σ+2π] cycloaddition reaction between dimethyl azodicarboxylate and quadricyclanes in the binary mixture solvents of methanol and water by varying the water content from 0 to 100 vol%. The two-dimensional potentials of mean force (2D PMF) calculations demonstrated that the mechanism of the reaction is a collaborative asynchronous procedure. The transition structures do not show large variation among different solvents. The calculated free energies of activation indicated that the QM/MM/MC method reproduced well the tendency of rate enhancement from pure methanol to methanol-water mixtures to "on water" with the water content increasing obtained in the experimental observation. The analyses of the energy pair distribution and radial distribution functions illustrated that hydrogen bonding plays an indispensable role in the stabilization of the transition structures. According to the results in methanol-water mixtures at different volume ratios, it is clear that the site-specific hydrogen bond effects are the central reason which leads to fast rate increases in progressing from a methanol-water volume ratio of 3 : 1 to 1 : 1. This work provides a new insight into the solvent effect for the [2σ+2σ+2π] cycloaddition reaction.

Optimization of Triarylpyridinone Inhibitors of the Main Protease of SARS-CoV-2 to Low-Nanomolar Antiviral Potency

Non-covalent inhibitors of the main protease (Mpro) of SARS-CoV-2 having a pyridinone core were previously reported with IC50 values as low as 0.018 μM for inhibition of enzymatic activity and EC50 values as low as 0.8 μM for inhibition of viral replication in Vero E6 cells. The series has now been further advanced by consideration of placement of substituted five-membered-ring heterocycles in the S4 pocket of Mpro and N-methylation of a uracil ring. Free energy perturbation calculations provided guidance on the choice of the heterocycles, and protein crystallography confirmed the desired S4 placement. Here we report inhibitors with EC50 values as low as 0.080 μM, while remdesivir yields values of 0.5-2 μM in side-by-side testing with infectious SARS-CoV-2. A key factor in the improvement is enhanced cell permeability, as reflected in PAMPA measurements. Compounds 19 and 21 are particularly promising as potential therapies for COVID-19, featuring IC50 values of 0.044-0.061 μM, EC50 values of ca. 0.1 μM, good aqueous solubility, and no cytotoxicity.

Indoloxytriazines as binding molecules for the JAK2 JH2 pseudokinase domain and its V617F variant

Small molecules that selectively bind to the pseudokinase JH2 domain over the JH1 kinase domain of JAK2 kinase are sought. Virtual screening led to the purchase of 17 compounds among which 9 were found to bind to V617F JAK2 JH2 with affinities of 40 - 300 μM in a fluorogenic assay. Ten analogues were then purchased yielding 9 additional active compounds. Aminoanilinyltriazine 22 was particularly notable as it shows no detectable binding to JAK2 JH1, and it has a 65-μM dissociation constant K _d with V617F JAK2 JH2. A crystal structure for 22 in complex with wild-type JAK2 JH2 was obtained to elucidate the binding mode. Additional de novo design led to the synthesis of 19 analogues of 22 with the most potent being 33n with K _d values of 2-3 μM for WT and V617F JAK2 JH2, and with 16-fold selectivity relative to binding with WT JAK2 JH1.