Force Fields for Small Molecules

Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning

Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality or even correcting the structure of biomacromolecules. However, vast computational costs and complex quantum mechanics/molecular mechanics (QM/MM) setups limit QR applications. Here we incorporate robust machine learning potentials (MLPs) in multiscale ONIOM(QM:MM) schemes to describe the core parts (e.g., drugs/inhibitors), replacing the expensive QM method. Additionally, two levels of MLPs are combined for the first time to overcome MLP limitations. Our unique MLPs+ONIOM-based QR methods achieve QM-level accuracy with significantly higher efficiency. Furthermore, our refinements provide computational evidence for the existence of bonded and nonbonded forms of the Food and Drug Administration (FDA)-approved drug nirmatrelvir in one SARS-CoV-2 main protease structure. This study highlights that powerful MLPs accelerate QRs for reliable protein-drug complexes, promote broader QR applications and provide more atomistic insights into drug development.

High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques

Structure-based drug design (SBDD) is rapidly evolving to be a fundamental tool for faster and more cost-effective methods of lead drug discovery. SBDD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This "virtual screening" technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of different computational techniques to determine which potential candidates are likely to bind with high affinity and efficacy. It is proposed that high-throughput SBDD (HT-SBDD) will significantly enrich the success rate of HTS methods, which currently fluctuates around ~1%. In this chapter, we focus on the theory and utility of high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics techniques. We also offer a comparative review of the benefits and limitations of traditional methods against more recent SBDD advances. As HT-SBDD is computationally intensive, we will also cover the important role high-performance computing (HPC) clusters play in the future of computational drug discovery.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

Extension of the CHARMM Classical Drude Polarizable Force Field to N- and O-Linked Glycopeptides and Glycoproteins

Molecular dynamic simulations are an effective tool to study complex molecular systems and are contingent upon the availability of an accurate and reliable molecular mechanics force field. The Drude polarizable force field, which allows for the explicit treatment of electronic polarization in a computationally efficient fashion, has been shown to reproduce experimental properties that were difficult or impossible to reproduce with the CHARMM additive force field, including peptide folding cooperativity, RNA hairpin structures, and DNA base flipping. Glycoproteins are essential components of glycoconjugate vaccines, antibodies, and many pharmaceutically important molecules, and an accurate polarizable force field that includes compatibility between the protein and carbohydrate aspect of the force field is essential to study these types of systems. In this work, we present an extension of the Drude polarizable force field to glycoproteins, including both N- and O-linked species. Parameter optimization focused on the dihedral terms using a reweighting protocol targeting NMR solution J-coupling data for model glycopeptides. Validation of the model include eight model glycopeptides and four glycoproteins with multiple N- and O-linked glycosylations. The new glycoprotein carbohydrate force field can be used in conjunction with the remainder of Drude polarizable force field through a variety of MD simulation programs including GROMACS, OPENMM, NAMD, and CHARMM and may be accessed through the Drude Prepper module in the CHARMM-GUI.

Carbon Nanodots from an In Silico Perspective

Carbon nanodots (CNDs) are the latest and most shining rising stars among photoluminescent (PL) nanomaterials. These carbon-based surface-passivated nanostructures compete with other related PL materials, including traditional semiconductor quantum dots and organic dyes, with a long list of benefits and emerging applications. Advantages of CNDs include tunable inherent optical properties and high photostability, rich possibilities for surface functionalization and doping, dispersibility, low toxicity, and viable synthesis (top-down and bottom-up) from organic materials. CNDs can be applied to biomedicine including imaging and sensing, drug-delivery, photodynamic therapy, photocatalysis but also to energy harvesting in solar cells and as LEDs. More applications are reported continuously, making this already a research field of its own. Understanding of the properties of CNDs requires one to go to the levels of electrons, atoms, molecules, and nanostructures at different scales using modern molecular modeling and to correlate it tightly with experiments. This review highlights different in silico techniques and studies, from quantum chemistry to the mesoscale, with particular reference to carbon nanodots, carbonaceous nanoparticles whose structural and photophysical properties are not fully elucidated. The role of experimental investigation is also presented. Hereby, we hope to encourage the reader to investigate CNDs and to apply virtual chemistry to obtain further insights needed to customize these amazing systems for novel prospective applications.

A library of Rhodamine6G-based pH-sensitive fluorescent probes with versatile in vivo and in vitro applications

Acidic pH is critical to the function of the gastrointestinal system, bone-resorbing osteoclasts, and the endolysosomal compartment of nearly every cell in the body. Non-invasive, real-time fluorescence imaging of acidic microenvironments represents a powerful tool for understanding normal cellular biology, defining mechanisms of disease, and monitoring for therapeutic response. While commercially available pH-sensitive fluorescent probes exist, several limitations hinder their widespread use and potential for biologic application. To address this need, we developed a novel library of pH-sensitive probes based on the highly photostable and water-soluble fluorescent molecule, Rhodamine 6G. We demonstrate versatility in terms of both pH sensitivity (i.e., pK a) and chemical functionality, allowing conjugation to small molecules, proteins, nanoparticles, and regenerative biomaterial scaffold matrices. Furthermore, we show preserved pH-sensitive fluorescence following a variety of forms of covalent functionalization and demonstrate three potential applications, both in vitro and in vivo, for intracellular and extracellular pH sensing. Finally, we develop a computation approach for predicting the pH sensitivity of R6G derivatives, which could be used to expand our library and generate probes with novel properties.

Recent Developments in Polarizable Molecular Dynamics Simulations of Electrolyte Solutions

Polarizable molecular dynamics simulations are a fast progressing field in the scientific research of ionic liquids. The fundamentals of polarizable simulations, as well as their application to ionic liquids, were summarized in a review [Bedrov, D.; Piquemal, J.-P.; Borodin, O.; MacKerell, Jr., A. D.; Roux, B.; Schröder, C. Molecular Dynamics Simulations of Ionic Liquids and Electrolytes Using Polarizable Force Fields. Chem. Rev. 2019, 119, 7940–7995] in 2019. Since then, new methods to treat intermolecular interaction of induced dipoles in these highly charged systems were developed. This concerns the damping of these interactions and additional charge transfer as well as the prediction of ionic materials with ultrahigh refractive indices. In addition to the progress of the polarizable force fields, also thermostats and barostats for polarizable simulations evolved recently.

Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field

The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit treatment of electronic polarization, such an extension must be accompanied by optimization of van der Waals (vdW) interactions, in the context of the Lennard-Jones (LJ) formalism in the present study. This is particularly challenging due to the extensive nature of chemical space combined with the correlated nature of LJ parameters. To address this challenge, a deep learning (DL)-based parametrization framework is developed, allowing for sampling of wide ranges of LJ parameters targeting experimental condensed phase thermodynamic properties. The present work utilizes this framework to develop the LJ parameters for atoms associated with four distinct groups covering 10 different atom types. Final parameter selection was facilitated by quantum mechanical data on rare-gas interactions with the training set molecules. The chosen parameters were then validated through experimental hydration free energies and condensed phase thermodynamic properties of validation set molecules to confirm transferability. The ultimate outcome of utilizing this framework is a set of LJ parameters in the context of the polarizable Drude FF, which demonstrated improvement in the reproduction of both experimental pure solvent and crystal properties and hydration free energies of the molecules compared to the additive CHARMM General FF (CGenFF) including the ability of the Drude FF to accurately reproduce both experimental pure solvent properties and hydration free energies. The study also shows how correlations between difference in the reproduction of condensed phase data between model compounds may be used to direct the selection of new atom types and training set molecules during FF development.

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

Recent progress in general force fields of small molecules

Recent advances in computational hardware and free energy algorithms enable a broader application of molecular simulation of binding interactions between receptors and small-molecule ligands. The underlying molecular mechanics force fields (FFs) for small molecules have also achieved advancements in accuracy, user-friendliness, and speed during the past several years (2018-2020). Besides the expansion of chemical space coverage of ligand-like molecules among major popular classical additive FFs and polarizable FFs, new charge models have been proposed for better accuracy and transferability, new chemical perception of avoiding predefined atom types have been applied, and new automated parameterization toolkits, including machine learning approaches, have been developed for users' convenience.

Global Optimization of the Lennard-Jones Parameters for the Drude Polarizable Force Field

Molecular dynamics (MD) simulations based on atomic models play an important role in the drug-discovery process to screen molecules, estimate binding free energies, and optimize lead compounds in chemical space. Accurate computations of thermodynamic and kinetic properties using MD simulations are highly dependent on the accuracy of the underlying atomic force field. In this context, going beyond the nonpolarizable fixed-charge model by accounting explicitly for induced polarization is highly desirable. The CHARMM polarizable force field based on classical Drude oscillators, in which an auxiliary charged particle is attached via a harmonic spring to its parent nucleus, offers both a computationally convenient and rigorous framework to model explicitly induced electronic polarization in MD simulations. For any molecule of interest, electrostatic partial charges, atomic polarizabilities, and Thole shielding factors, as well as bonded parameters can either be determined from ab initio calculations or ascribed from the knowledge-based library of the CHARMM Generalized force field. While this approach is fairly reliable in general, it is well understood that the overall accuracy of the models with respect to thermodynamic properties such as bulk density, enthalpies, and solvation free energies is particularly sensitive to the nonbonded Lennard-Jones (LJ) parameters. In the present study, we systematically refined the set of LJ parameters for the atom types available in the Drude force field to best match the experimental thermodynamic properties for 416 small druglike organic molecules. To further test the transferability of the optimized parameters, the hydration free energy of 372 molecules was computed. The calculations resulted in a small average error of 0.46 kcal/mol and a Pearson R of 0.9, representing a significant improvement over the additive GAFF force field in our previous study, where an average error of ∼2 kcal/mol was obtained. Such an improvement is consistent with the ability of the polarizable Drude model to more accurately model interactions in different environments. The effort provides a roadmap for the global optimization of force field parameters using experimental data. It is hoped that the present effort will further the application of the Drude polarizable force field in molecular simulations including drug design and discovery.

Identification of Novel CD39 Inhibitors Based on Virtual Screening and Enzymatic Assays

The accumulation of adenosine in the tumor microenvironment mediates immunosuppression and promotes tumor growth and proliferation. Intervention of the adenosine pathway is an important direction of antitumor immunity research. CD39 is an important ecto-nucleotidases for adenosine generation, therefore targeting the CD39-adenosine pathway is an emerging immune checkpoint for anticancer treatment. However, currently no CD39 inhibitor has been approved by the U.S. Food and Drug Administration. The development of CD39 drugs is urgent for clinical application. In this study, we combined homology modeling, virtual screening, and in vitro enzymatic activity to characterize the structural features of the CD39 protein and identify a triazinoindole-based compound as a CD39 inhibitor. The identified inhibitor and one of its analogues could effectively prevent the enzymatic activity of CD39 with IC₅₀ values of 27.42 ± 5.52 and 79.24 ± 12.21 μM, respectively. At the same time, the inhibitor significantly inhibited the adenosine monophosphate production in colorectal cancer cell lines (HT29 and MC38) and thereafter prevented cell proliferation. Molecular docking studies, mutagenesis, and microscale thermophoresis indicated that residues such as R85 could be the main contributor in binding triazinoindole compounds. The binding mode can potentially be utilized for hit-to-lead optimization, and the identified inhibitor can be further tested for its anticancer activity in vivo or may serve as a chemical agent to study CD39-related functions.

Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators

Molecular dynamics (MD) simulations allow researchers to investigate the behavior of desired biological targets at ever-decreasing costs with ever-increasing precision. Among the biological macromolecules, ion channels are remarkable transmembrane proteins, capable of performing special biological processes and revealing a complex regulatory matrix, including modulation by small molecules, either endogenous or exogenous. Recently, given the developments in ion channel structure determination and accessibility of bio-computational techniques, MD and related tools are becoming increasingly popular in the intense research area regarding ligand-channel interactions. This review synthesizes and presents the most important fields of MD involvement in investigating channel-molecule interactions, including, but not limited to, deciphering the binding modes of ligands to their ion channel targets and the mechanisms through which chemical compounds exert their effect on channel function. Special attention is devoted to the importance of more elaborate methods, such as free energy calculations, while principles regarding drug design and discovery are highlighted. Several technical aspects involving the creation and simulation of channel-molecule MD systems (ligand parameterization, proper membrane setup, system building, etc.) are also presented.

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

Molecular simulation and experimental studies on CO2 and N2 adsorption to bituminous coal

A coal fire is one of the most serious disasters in coal mining. To improve the efficiency of an inert gas for extinguishing the fire, the adsorption behavior of coal in CO₂/N₂ mixed gas was investigated in this study. Proximate analysis, ultimate analysis, solid-state ¹³C nuclear magnetic resonance spectroscopy (NMR), X-ray photoelectron spectroscopy (XPS), and molecular dynamics (MD) were applied to analyze and establish the bituminous coal molecular model. The adsorption behavior of bituminous coal in mixed gas mixtures with different proportions was studied using the bituminous coal model and Materials Studio (MS) software. A self-built coal adsorption experimental system was used for experiments. The adsorption of bituminous coal to CO₂ is stronger than that to N₂, and there is a competitive adsorption relationship between them. The amount of CO₂ adsorbed by the coal gradually increases as the CO₂ partial pressure rises, consistent with the Langmuir model. With an increase in CO₂ pressure, the total adsorption capacity, which is divided into the rapid increase stage, slow growth stage, and stable stage, also increases. The coal adsorbs 0.5050 cm³/g, 0.7455 cm³/g, 0.9450 cm³/g, 1.0715 cm³/g, and 1.2000 cm³/g for pure N₂, 2%, 5%, 7%, and 10% CO₂, respectively, in the experiment. The results of the simulation and experiment show the same trend, which means that the injection of a small amount of CO₂ into pure N₂ will greatly improve the gas adsorption volume of the coal, demonstrating that it is feasible to improve the ability of the coal to absorb mixed gases by changing the gas concentration and consequently to increase the efficiency of inert gas for fire extinguishing and suppression.

Quantum-mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

Joint academic-industrial projects supporting drug discovery are frequently pursued to deploy and benchmark cutting-edge methodical developments from academia in a real-world industrial environment at different scales. The dimensionality of tasks ranges from small molecule physicochemical property assessment over protein-ligand interaction up to statistical analyses of biological data. This way, method development and usability both benefit from insights gained at both ends, when predictiveness and readiness of novel approaches are confirmed, but the pharmaceutical drug makers get early access to novel tools for the quality of drug products and benefit of patients. Quantum-mechanical and simulation methods particularly fall into this group of methods, as they require skills and expense in their development but also significant resources in their application, thus are comparatively slowly dripping into the realm of industrial use. Nevertheless, these physics-based methods are becoming more and more useful. Starting with a general overview of these and in particular quantum-mechanical methods for drug discovery we review a decade-long and ongoing collaboration between Sanofi and the Kast group focused on the application of the embedded cluster reference interaction site model (EC-RISM), a solvation model for quantum chemistry, to study small molecule chemistry in the context of joint participation in several SAMPL (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenges. Starting with early application to tautomer equilibria in water (SAMPL2) the methodology was further developed to allow for challenge contributions related to predictions of distribution coefficients (SAMPL5) and acidity constants (SAMPL6) over the years. Particular emphasis is put on a frequently overlooked aspect of measuring the quality of models, namely the retrospective analysis of earlier datasets and predictions in light of more recent and advanced developments. We therefore demonstrate the performance of the current methodical state of the art as developed and optimized for the SAMPL6 pKa and octanol-water log P challenges when re-applied to the earlier SAMPL5 cyclohexane-water log D and SAMPL2 tautomer equilibria datasets. Systematic improvement is not consistently found throughout despite the similarity of the problem class, i.e. protonation reactions and phase distribution. Hence, it is possible to learn about hidden bias in model assessment, as results derived from more elaborate methods do not necessarily improve quantitative agreement. This indicates the role of chance or coincidence for model development on the one hand which allows for the identification of systematic error and opportunities toward improvement and reveals possible sources of experimental uncertainty on the other. These insights are particularly useful for further academia-industry collaborations, as both partners are then enabled to optimize both the computational and experimental settings for data generation.

Adsorption of constitutional isomers of cyclic monoterpenes on hydroxylated silica surfaces

We present a study of four monoterpene isomers (limonene, γ-terpinene, terpinolene, and α-pinene) that are prevalent in indoor environments and their interaction with the hydroxylated SiO₂ surface, a model for the glass surface, by combining infrared spectroscopy and computational simulations. These isomers are molecularly adsorbed onto SiO₂ through π-hydrogen bonds with surface hydroxyl groups. However, experimental results suggest that the strength of interaction of these compounds with the SiO₂ surface varies for each isomer, with α-pinene showing the weakest interaction. This observation is supported by molecular dynamics simulations that α-pinene adsorbed on the SiO₂ surface has lower free energy of desorption and a lower mass accommodation coefficient compared to other isomers. Additionally, our ab initio molecular dynamics simulations show lower π-hydrogen bonding probabilities for α-pinene compared to the other three constitutional isomers. Importantly, these interactions are most likely present for a range of other systems involving organic compounds and solid surfaces and, thus, provide a thorough framework for comparing the interactions of organic molecules on indoor relevant surfaces.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Impact of Adsorbed Water on the Interaction of Limonene with Hydroxylated SiO2: Implications of π-Hydrogen Bonding for Surfaces in Humid Environments

The indoor environment is a dynamic one with many variables impacting indoor air quality and indoor air chemistry. These include relative humidity (RH) and the presence of different surfaces. Although it has been suggested that the indoor concentrations of gas-phase compounds increase at higher relative humidity, because of displacement of these compounds from indoor surfaces, little is known from a molecular perspective about how RH and adsorbed water impact the adsorption of indoor relevant organic compounds such as limonene with indoor relevant surfaces. Herein, we investigate the effects of RH on the adsorption of limonene, a hydrophobic molecule, on hydroxylated SiO₂ surfaces, a model for glass surfaces. Experimental data using infrared spectroscopy to directly measure limonene adsorption are combined with both force field-based molecular dynamics (MD) and ab initio molecular dynamics (AIMD) simulations to understand the competitive interactions between limonene, water, and the SiO₂ surface. The spectroscopic data provide evidence that adsorbed limonene is not completely displaced by adsorbed water, even at high RH (∼80%) when the water layer coverage is close to three monolayers (MLs). These experimental data are supported by AIMD and MD simulations, which indicate that limonene is present at the adsorbed water interface but displaced from direct interactions with SiO₂. This study shows that although some limonene can desorb from the surface, even at the highest RH, more than half the limonene remains adsorbed on the surface that can undergo continued surface reactivity. A complex network of π-hydrogen bonds, water-water hydrogen bonds, and SiO₂-water hydrogen bonds explains these interactions at the air/adsorbed water/SiO₂ interface that hold the hydrophobic limonene molecule at the interface. Importantly, these interactions are most likely present for a range of other systems involving organic compounds and solid surfaces at ambient relative humidity and may be important in a range of scientific areas, from sensor development to cultural heritage science.

Combined inhibition of JAK1/2 and DNMT1 by newly identified small-molecule compounds synergistically suppresses the survival and proliferation of cervical cancer cells

Despite substantial advances in treating cervical cancer (CC) with surgery, radiation and chemotherapy, patients with advanced CC still have poor prognosis and significantly variable clinical outcomes due to tumor recurrence and metastasis. Therefore, to develop more efficacious and specific treatments for CC remains an unmet clinical need. In this study, by virtual screening the SPECS database, we identified multiple novel JAK inhibitor candidates and validated their antitumor drug efficacies that were particularly high against CC cell lines. AH057, the best JAK inhibitor identified, effectively blocked the JAK/STAT pathways by directly inhibiting JAK1/2 kinase activities, and led to compromised cell proliferation and invasion, increased apoptosis, arrested cell cycles, and impaired tumor progression in vitro and in vivo. Next, by screening the Selleck chemical library, we identified SGI-1027, a DNMT1 inhibitor, as the compound that displayed the highest synergy with AH057. By acting on a same set of downstream effector molecules that are dually controlled by JAK1/2 and DNMT1, the combination of AH057 with SGI-1027 potently and synergistically impaired CC cell propagation via dramatically increasing apoptotic cell death and cell-cycle arrest. These findings establish a preclinical proof of concept for combating CC by dual targeting of JAK1/2 and DNMT1, and provide support for launching a clinical trial to evaluate the efficacy and safety of this drug combination in patients with CC and other malignant tumors.