CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields

Synthesis, pharmacological evaluation, and modeling of novel quaternary ammonium salts derived from β-carboline containing an imidazole moiety as angiogenesis inhibitors

In this study, a series of novel β-carboline condensed imidazolium derivatives (7a-7y) were designed and synthesized by incorporating imidazolium salt structures into β-carboline. The cytotoxicity of compounds 7a-7y was evaluated in various cancer cell lines, including lung cancer (A549), gastric cancer (BGC-823), mouse colon cancer (CT-26), liver cancer (Bel-7402), and breast cancer (MCF-7), using the MTT assay. Most compounds exhibited significant activity against one or more of the cancer cell lines. Notably, compounds 7 g, 7o, 7r, 7 s, 7u, 7v, 7x, and 7w showed the highest cytotoxic activity (IC50 < 2 μM) in the tested tumor cell lines. Compound 7x demonstrated cytotoxic activities of 1.3 ± 0.3 μM (for BGC-823), 2.4 ± 0.4 μM (against A549), 7.8 ± 0.9 μM (for Bel-7402), and 9.8 ± 1.4 μM (against CT-26). The chick chorioallantoic membrane assay revealed significant anti-angiogenic potential of compound 7x. Molecular imprinting studies suggested the anti-angiogenic effect of compound 7x might be attributed to inhibition of VEGFR2 kinase. Molecular docking and molecular dynamics further indicate that its activity may be primarily associated with the potential inhibition of VEGFR2. Our research outcomes have provided valuable lead compounds for the development of novel antitumor drugs and have offered beneficial insights for subsequent drug design and optimization.

High Yield of L-Sorbose via D-Glucose Isomerization in Ethanol over a Bifunctional Titanium-Boron-Beta Zeolite

D-Glucose-to-L-sorbose isomerization on Lewis acidic zeolite is a highly attractive avenue for sorbose production. But the L-sorbose yield is limited by the competing D-glucose-to-D-fructose isomerization and reaction equilibrium. In this work, it is suggested that ethanol directs the glucose conformation for selective D-glucose-to-L-sorbose isomerization. It also reacts with the produced L-sorbose to form ethyl-sorboside, which allows the D-glucose-to-L-sorbose isomerization to proceed beyond the thermodynamic equilibrium limit. It is shown that a bifunctional zeolite Beta containing framework titanium (Ti) and boron (B) is a selective catalyst for this tandem reaction: Lewis acidic framework Ti catalyzes the D-glucose-to-L-sorbose isomerization via an intramolecular 5,1-hydride shift process as confirmed by isotopic tracing experiments followed by 13C-NMR, while weak Brønsted acid framework B selectively promotes the sorbose ketalization with ethanol. A remarkably high yield of L-sorbose with a high fraction of sugar (>95 %: 27 % unreacted glucose, 11.4 % fructose, 57 % sorbose) was obtained after the mixture produced in ethanol was hydrolyzed.

Structural Analysis of Mammalian Sialic Acid Esterase

Sialic acid esterase (SIAE) catalyzes the removal of O-acetyl groups from sialic acids found on cell surface glycoproteins to regulate cellular processes such as B cell receptor signalling and apoptosis. Loss-of-function mutations in SIAE are associated with several common autoimmune diseases including Crohn's, ulcerative colitis, and arthritis. To gain a better understanding of the function and regulation of this protein, we determined crystal structures of SIAE from three mammalian homologs, including an acetate bound structure. The structures reveal that the catalytic domain adopts the fold of the SGNH hydrolase superfamily. The active site is composed of a catalytic dyad, as opposed to the previously reported catalytic triad. Attempts to determine a substrate-bound structure yielded only the hydrolyzed product acetate in the active site. Rigid docking of complete substrates followed by molecular dynamics simulations revealed that the active site does not form specific interactions with substrates, rather it appears to be broadly specific to accept sialoglycans with diverse modifications. Based on the acetate bound structure, a catalytic mechanism is proposed. Structural mapping of disease mutations reveals that most are located on the surface of the enzyme and would only cause minor disruptions to the protein fold, suggesting that these mutations likely affect binding to other factors. These results improve our understanding of SIAE biology and may aid in the development of therapies for autoimmune diseases and cancer.

In Silico Structural Comparison of Aromatic and Aliphatic Chiral Peptoid Oligomers

Atomistic simulations of peptoids have the capability to predict structure-property relationships, depending on the accuracy of the associated force field. This work presents an addendum to the CGenFF-NTOID peptoid force field for aliphatic side chains. We develop parameters for two aliphatic side chains, RN1-tertiary butylethyl glycine (r1tbe) and SN1-tertiary butylethyl glycine (s1tbe). Enhanced sampled (well-tempered metadynamics) atomistic simulations are performed using CGenFF-NTOID to determine the monomer structural preferences for these side chains. The free energy minima attained through these simulations are compared with structural observations obtained from experiments. We also compare the structural preferences of aliphatic s1tbe and aromatic SN1-naphthylethyl glycine (s1ne). This is done through parallel bias metadynamics on monomers and pentamers of s1tbe and s1ne. The structural observations through simulations are also compared with available experimental metrics of the dihedral angles and pitch. The pentamer minima structures are also compared with ab initio optimized structures, which show excellent agreement. This comparison illustrates alternatives to aromatic side chains that can be used to stabilize peptoid secondary structures. The developed parameters help to increase the diversity of peptoid side chains available for materials discovery through computational studies.

Machine-Learning Approach to Identify Potential Dengue Virus Protease Inhibitors: A Computational Perspective

The global prevalence of dengue virus (DENV), a widespread flavivirus, has led to varied epidemiological impacts, economic burdens, and health consequences. The alarming increase in infections is exacerbated by the absence of approved antiviral agents against the DENV. Within flaviviruses, the NS3/NS2B serine protease plays a pivotal role in processing the viral polyprotein into distinct components, making it an attractive target for antiviral drug development. In this study, machine-learning (ML) techniques were employed to build predictive models for the screening of a library containing 32,000 protease inhibitors. Utilizing GNINA for structure-based virtual screening, the top potential candidates underwent a subsequent evaluation of their absorption, distribution, metabolism, excretion, and toxicity properties. Selected compounds were subjected to molecular dynamics simulations and binding free energy calculations via MM/GBSA. The results suggest that comp530 possesses binding potential to DENV protease as a noncovalent inhibitor with multiple positions for chemical substitutions, presenting opportunities for optimizing their selectivity and specificity. However, other compounds predicted via ML models may still provide a promising start for covalent inhibitors.

Protein-free domains in native and ferroptosis-driven oxidized cell membranes: a molecular dynamics study of biophysical properties and doxorubicin uptake

Ferroptosis is a regulated form of cell death characterized by iron-dependent lipid peroxidation of polyunsaturated fatty acids (PUFAs). Despite its significance, the precise molecular mechanisms underlying ferroptosis remain elusive, particularly concerning their impact on membrane properties. This study aimed to investigate the biophysical changes in plasma membranes due to lipid peroxidation during ferroptosis and their impact on the uptake of doxorubicin (DOX), a potent anticancer agent linked to ferroptosis. Using all-atom molecular dynamics simulations, we compared native red blood cell membranes (protein-free domains) with a ferroptosis model, in which PUFAs were replaced with hydroperoxide derivatives. Our findings reveal that the ferroptotic membrane exhibits decreased thickness and increased lipid area while maintaining overall integrity. The hydroperoxide groups localized in the disordered tail regions, enhancing tail mobility and facilitating hydrogen bonding. Lipid lateral diffusion was significantly altered, both layers of the ferroptotic membrane exhibited slower diffusion rates compared to the native membrane. Furthermore, lipid oxidation affected diffusion activation energies. Importantly, we found that DOX could penetrate the oxidized ferroptosis membrane with a lower free-energy barrier (∆GPB) of approximately 38 kJ.mol-1. Consequently, DOX's permeability was approximately seven orders of magnitude higher than that of the native membrane. In summary, lipid peroxidation during ferroptosis induces extensive structural and dynamic changes, influencing membrane behavior and potentially offering insights that could inform future therapeutic strategies.

Theoretically grounded approaches to account for polarization effects in fixed-charge force fields

Non-polarizable, or fixed-charge, force fields are the workhorses of most molecular simulation studies. They attempt to describe the potential energy surface (PES) of the system by including polarization effects in an implicit way. This has historically been done in a rather empirical and ad hoc manner. Recent theoretical treatments of polarization, however, offer promise for getting the most out of fixed-charge force fields by judicious choice of parameters (most significantly the net charge or dipole moment of the model) and application of post facto polarization corrections. This Perspective describes these polarization theories, namely the "halfway-charge" theory and the molecular dynamics in electronic continuum theory, and shows that they lead to qualitatively (and often, quantitatively) similar predictions. Moreover, they can be reconciled into a unified approach to construct a force field development workflow that can yield non-polarizable models with charge/dipole values that provide an optimal description of the PES. Several applications of this approach are reviewed, and avenues for future research are proposed.

Discerning potent CSF-1r inhibitors for targeting and therapy of neuroinflammation using computational approaches

Microglia, the primary cellular mediator of neuroinflammation, plays a pivotal role in numerous neurological disorders. Precise and non-invasive quantification of microglia is of paramount importance. Despite various investigations into cell-specific biomarkers for assessing neuroinflammation, many suffer from poor cellular specificity and low signal-to-noise ratios. Colony-stimulating factor-1 receptor (CSF-1R), also known as FMS kinase, has emerged as a promising neuroinflammation biomarker with significant relevance to inflammatory diseases. Additionally, CSF-1R inhibitors (CSF-1Ri) have shown therapeutic potential in central nervous system (CNS) pathological conditions by depleting microglia. Therefore, the development of more specific CSF-1R inhibitors for targeting and treating various CNS insults and neurological disorders is imperative. This study focuses on the search for novel CSF-1R inhibitors. Based on the literature on CSF-1R inhibitors, we proposed and investigated ten ligands as novel CSF-1R inhibitors. Among these, the top three ligands, selected based on their maximum binding scores in docking calculations, are subjected to 100 nanoseconds of molecular dynamics (MD) simulation, alongside three reference ligands. All protein-ligand complexes remain stable throughout the dynamics and exhibit minimal fluctuations during the analysis. The results obtained through this study may prove significant for the future design of CSF-1R inhibitors with potential applications in the field of biomedicine.

Recent Applications of In Silico Approaches for Studying Receptor Mutations Associated with Human Pathologies

In recent years, the advent of computational techniques to predict the potential activity of a drug interacting with a receptor or to predict the structure of unidentified proteins with aberrant characteristics has significantly impacted the field of drug design. We provide a comprehensive review of the current state of in silico approaches and software for investigating the effects of receptor mutations associated with human diseases, focusing on both frequent and rare mutations. The reported techniques include virtual screening, homology modeling, threading, docking, and molecular dynamics. This review clearly shows that it is common for successful studies to integrate different techniques in drug design, with docking and molecular dynamics being the most frequently used techniques. This trend reflects the current emphasis on developing novel therapies for diseases resulting from receptor mutations with the recently discovered AlphaFold algorithm as the driving force.

The structural and mechanistic bases for the viral resistance to allosteric HIV-1 integrase inhibitor pirmitegravir

Allosteric HIV-1 integrase (IN) inhibitors (ALLINIs) are investigational antiretroviral agents that potently impair virion maturation by inducing hyper-multimerization of IN and inhibiting its interaction with viral genomic RNA. The pyrrolopyridine-based ALLINI pirmitegravir (PIR) has recently advanced into phase 2a clinical trials. Previous cell culture-based viral breakthrough assays identified the HIV-1(Y99H/A128T IN) variant that confers substantial resistance to this inhibitor. Here, we have elucidated the unexpected mechanism of viral resistance to PIR. Although both Tyr99 and Ala128 are positioned within the inhibitor binding V-shaped cavity at the IN catalytic core domain (CCD) dimer interface, the Y99H/A128T IN mutations did not substantially affect the direct binding of PIR to the CCD dimer or functional oligomerization of full-length IN. Instead, the drug-resistant mutations introduced a steric hindrance at the inhibitor-mediated interface between CCD and C-terminal domain (CTD) and compromised CTD binding to the CCDY99H/A128T + PIR complex. Consequently, full-length INY99H/A128T was substantially less susceptible to the PIR-induced hyper-multimerization than the WT protein, and HIV-1(Y99H/A128T IN) conferred >150-fold resistance to the inhibitor compared with the WT virus. By rationally modifying PIR, we have developed its analog EKC110, which readily induced hyper-multimerization of INY99H/A128T in vitro and was ~14-fold more potent against HIV-1(Y99H/A128T IN) than the parent inhibitor. These findings suggest a path for developing improved PIR chemotypes with a higher barrier to resistance for their potential clinical use.IMPORTANCEAntiretroviral therapies save the lives of millions of people living with HIV (PLWH). However, the evolution of multi-drug-resistant viral phenotypes is a major clinical problem, and there are limited or no treatment options for heavily treatment-experienced PLWH. Allosteric HIV-1 integrase inhibitors (ALLINIs) are a novel class of antiretroviral compounds that work by a unique mechanism of binding to the non-catalytic site on the viral protein and inducing aberrant integrase multimerization. Accordingly, ALLINIs potently inhibit both wild-type HIV-1 and all drug-resistant viral phenotypes that have so far emerged against currently used therapies. Pirmitegravir, a highly potent and safe investigational ALLINI, is currently advancing through clinical trials. Here, we have elucidated the structural and mechanistic bases behind the emergence of HIV-1 integrase mutations in infected cells that confer resistance to pirmitegravir. In turn, our findings allowed us to rationally develop an improved ALLINI with substantially enhanced potency against the pirmitegravir-resistant virus.

Comparison of Methodologies for Absolute Binding Free Energy Calculations of Ligands to Intrinsically Disordered Proteins

Modulating the function of Intrinsically Disordered Proteins (IDPs) with small molecules is of considerable importance given the crucial roles of IDPs in the pathophysiology of numerous diseases. Reported binding affinities for ligands to diverse IDPs vary broadly, and little is known about the detailed molecular mechanisms that underpin ligand efficacy. Molecular simulations of IDP ligand binding mechanisms can help us understand the mode of action of small molecule inhibitors of IDP function, but it is still unclear how binding energies can be modeled rigorously for such a flexible class of proteins. Here, we compare alchemical absolute binding free energy calculations (ABFE) and Markov-State Modeling (MSM) protocols to model the binding of the small molecule 10058-F4 to a disordered peptide extracted from a segment of the oncoprotein c-Myc. The ABFE results produce binding energy estimates that are sensitive to the choice of reference structure. In contrast, the MSM results produce more reproducible binding energy estimates consistent with weak mM binding affinities and transient intermolecular contacts reported in the literature.

Committor Guided Estimates of Molecular Transition Rates

The probability that a configuration of a physical system reacts, or transitions from one metastable state to another, is quantified by the committor function. This function contains richly detailed mechanistic information about transition pathways, but a full parametrization of the committor requires the construction of a high-dimensional function, a generically challenging task. Recent efforts to leverage neural networks as a means to solve high-dimensional partial differential equations, often called "physics-informed" machine learning, have brought the committor into computational reach. Here, we build on the semigroup approach to learning the committor and assess its utility for predicting dynamical quantities such as transition rates. We show that a careful reframing of the objective function and improved adaptive sampling strategies provide highly accurate representations of the committor. Furthermore, by directly applying the Hill relation, we show that these committors provide accurate transition rates for molecular systems.

Structural basis of MICAL autoinhibition

MICAL proteins play a crucial role in cellular dynamics by binding and disassembling actin filaments, impacting processes like axon guidance, cytokinesis, and cell morphology. Their cellular activity is tightly controlled, as dysregulation can lead to detrimental effects on cellular morphology. Although previous studies have suggested that MICALs are autoinhibited, and require Rab proteins to become active, the detailed molecular mechanisms remained unclear. Here, we report the cryo-EM structure of human MICAL1 at a nominal resolution of 3.1 Å. Structural analyses, alongside biochemical and functional studies, show that MICAL1 autoinhibition is mediated by an intramolecular interaction between its N-terminal catalytic and C-terminal coiled-coil domains, blocking F-actin interaction. Moreover, we demonstrate that allosteric changes in the coiled-coil domain and the binding of the tripartite assembly of CH-L2α1-LIM domains to the coiled-coil domain are crucial for MICAL activation and autoinhibition. These mechanisms appear to be evolutionarily conserved, suggesting a potential universality across the MICAL family.

Elucidating Protein Structures in the Gas Phase: Traversing Configuration Space with Biasing Methods

Achieving accurate characterization of protein structures in the gas phase continues to be a formidable challenge. To tackle this issue, the present study employs Molecular Dynamics (MD) simulations in tandem with enhanced sampling techniques (methods designed to efficiently explore protein conformations). The objective is to identify suitable structures of proteins by contrasting their calculated Collision Cross-Section (CCS) with those observed experimentally. Significant discrepancies were observed between the initial MD-simulated and experimentally measured CCS values through Ion Mobility-Mass Spectrometry (IMS-MS). To bridge this gap, we employed two distinct enhanced sampling methods, Harmonic Biasing Potential and Adaptive Biasing Force, which help the proteins overcome energy barriers to adopt more compact configurations. These techniques leverage the radius of gyration as a reaction coordinate (guiding parameter), guiding the system toward compressed states that potentially match experimental configurations more closely. The guiding forces are only employed to overcome existing barriers and are removed to allow the protein to naturally arrive at a potential gas phase configuration. The results demonstrated close alignment (within ∼4%) between simulated and experimental CCS values despite using different strengths and/or methods, validating their efficacy. This work lays the groundwork for future studies aimed at optimizing biasing methods and expanding the collective variables used for more accurate gas-phase structural predictions.

Discovery of Five Classes of Bacterial Defensins: Ancestral Precursors of Defensins from Eukarya?

Defensins are present in many organisms and are divided into two evolutionary groups, termed cis- and trans-defensins. Cis-defensins have only recently been reported in bacteria, and knowledge of these defensins is limited, with no family classification. Here, we describe the identification of 74 cis-defensins from bacteria and propose five classes for their classification. We also report the first NMR structure determination of a Myxoccocus xanthus defensin, as well as its in silico expression analysis. Xanthusin-1 has a unique structure among the published defensins, which could indicate that the proposed class II peptides constitute a separate group of defensins. Xanthusin-1 gene expression was observed in casitone-based and Streptomyces coelicolor coculture-grown media. Our results demonstrate a wider distribution of defensins outside the Eukarya domain, shedding light on the origin and distribution of defensins. The sharing of three disulfide defensins between bacteria and eukaryotes points to a possible prokaryotic origin of the CSαβ motif. Moreover, the identification of defensins in Gram-positive and Gram-negative bacteria indicates an early origin but with many gene losses during the evolutionary process, similar to findings for eukaryotic defensins.

Exploring Free Energy Landscapes for Protein Partitioning into Membrane Domains in All-Atom and Coarse-Grained Simulations

It is known that membrane environment can impact the structure and function of integral membrane proteins. As such, elucidation of the thermodynamic driving forces governing protein partitioning between membrane domains of varying lipid composition is a fundamental topic in membrane biophysics. Molecular dynamics simulations provide valuable tools for quantitatively characterizing the free energy landscapes governing protein partitioning at the molecular level. In this study, we propose an efficient simulation methodology for the calculation of free energies for the partitioning of transmembrane proteins between liquid-disorder (Ld) and liquid-ordered (Lo) domains in all-atom (AA) phase-separated lipid bilayers. The computed potential of mean force defining the equilibrium partition coefficients is compared for AA and coarse-grained systems. Energy decomposition is used to identify differences in the underlying thermodynamics. Our findings highlight the importance of employing AA models to accurately estimate relevant free energy changes during protein translation between membrane domains.

The role of lipid oxidation pathway in reactive oxygen species-mediated cargo release from liposomes

Reactive oxygen species (ROS)-mediated photooxidation is an efficient method for triggering a drug release from liposomes. In addition to the release of small molecules, it also allows the release of large macromolecules, making it a versatile tool for controlled drug delivery. However, the exact release mechanism of large macromolecules from ROS-sensitive liposomes is still unclear. There are no studies on the effect of lipid oxidation on the release of cargo molecules of different sizes. By using HPLC-HRMS method we analyzed the oxidation products of ROS-sensitive DOTAP lipid in phthalocyanine-loaded DOTAP:Cholesterol:DSPE-PEG liposomes after 630 nm light irradiation of different durations. Shorter illumination time (1-2 minutes) led to the formation of hydroperoxides and vic-alcohols predominantly. Longer 9-minute irradiation resulted already in aldehydes generation. Interestingly, the presence of epoxides/mono-hydroperoxides and vic-alcohols in a lipid bilayer ensured a high 90% release of small hydrophilic cargo molecules i.e. calcein, but not large (≥10 KDa) macromolecules. Oxidation till aldehydes was mandatory to deliver e.g. dextrans of 10-70 kDa with ca. 30% efficiency. Molecular dynamics simulations revealed that the formation of aldehydes is required to form pores or even fully disrupt the lipid membrane, while e.g. presence of hydroperoxides is enough to make the bilayer more permeable just for water and small molecules. This is an important finding that shed a light on the release mechanism of different cargo molecules from ROS-sensitive drug delivery systems.

Structure and Energetics of PET-Hydrolyzing Enzyme Complexes: A Systematic Comparison from Molecular Dynamics Simulations

Discovered in 2016, the enzyme PETase, secreted by bacterial Ideonella Sakaiensis 201-F6, has an excellent hydrolytic activity toward poly(ethylene terephthalate) (PET) at room temperature, while it decreases at higher temperatures due to the low thermostability. Many variants have been engineered to overcome this limitation, which hinders industrial application. In this work, we systematically compare PETase wild-type (WT) and four mutants (DuraPETase, ThermoPETase, FastPETase, and HotPETase) using standard molecular dynamics (MD) simulations and unbinding free energy calculations. In particular, we analyze the enzymes' structural characteristics and binding to a tetrameric PET chain (PET4) under two temperature conditions: T1─300 K and T2─350 K. Our results indicate that (i) PET4 forms stable complexes with the five enzymes at room temperature (∼300 K) and (ii) most of the interactions are localized close to the active site of the protein, where the W185 and Y87 residues interact with the aromatic rings of the substrate. Specifically, (iii) the W185 side-chain explores different conformations in each variant (a phenomenon known in the literature as "W185 wobbling"). This suggests that the binding pocket retains structural plasticity and flexibility among the variants, facilitating substrate recognition and localization events at moderate temperatures. Moreover, (iv) PET4 establishes aromatic interactions with the catalytic H237 residue, stabilizing the catalytic triad composed of residues S160-H237-D206, and helping the system achieve an effective configuration for the hydrolysis reaction. Conversely, (v) the binding affinity decreases at a higher temperature (∼350 K), retaining moderate interactions only for HotPETase. Finally, (vi) MD simulations of complexes formed with poly(ethylene-2,5-furan dicarboxylate) (PEF) show no persistent interactions, suggesting that these enzymes are not yet optimized for binding this alternative semiaromatic plastic polymer. Our study offers valuable insights into the structural stability of these enzymes and the molecular determinants driving PET binding onto their surfaces, sheds light on the mechanistic steps that precede the onset of hydrolysis, and provides a foundation for future enzyme optimization.

Role of Peptide Associations in Enhancing the Antimicrobial Activity of Adepantins: Comparative Molecular Dynamics Simulations and Design Assessments

Adepantins are peptides designed to optimize antimicrobial biological activity through the choice of specific amino acid residues, resulting in helical and amphipathic structures. This paper focuses on revealing the atomistic details of the mechanism of action of Adepantins and aligning design concepts with peptide behavior through simulation results. Notably, Adepantin-1a exhibits a broad spectrum of activity against both Gram-positive and Gram-negative bacteria, while Adepantin-1 has a narrow spectrum of activity against Gram-negative bacteria. The simulation results showed that one of the main differences is the extent of aggregation. Both peptides exhibit a strong tendency to cluster due to the amphipathicity embedded during design process. However, the more potent Adepantin-1a forms smaller aggregates than Adepantin-1, confirming the idea that the optimal aggregations, not the strongest aggregations, favor activity. Additionally, we show that incorporation of the cell penetration region affects the mechanisms of action of Adepantin-1a and promotes stronger binding to anionic and neutral membranes.

Exploring the Inhibitory Potential of Phytosterols β-Sitosterol, Stigmasterol, and Campesterol on 5-Alpha Reductase Activity in the Human Prostate: An In Vitro and In Silico Approach

Steroidal 5α-reductase type 2 (S5αR2) is a key enzyme involved in the conversion of testosterone (TST) to dihydrotestosterone (DHT), a crucial process in the development of benign prostatic hyperplasia (BPH). Phytosterols (PSs), natural plant-derived compounds, have been proposed as potential inhibitors of S5αR2, but studies on their efficacy are limited. This study evaluates the inhibitory effects of three PSs (β-sitosterol, stigmasterol, and campesterol) on S5αR2 activity using a combined in vitro and in silico approach. The inhibitory activity of the respective PSs was assessed in vitro, by measuring TST and DHT, while molecular docking and dynamics explored PS interactions with S5αR2's active site. The in vitro tests indicated significantly higher IC50 values (β-sitosterol, 3.24 ± 0.32 µM; stigmasterol, 31.89 ± 4.26 µM; and campesterol, 15.75 ± 5.56 µM) for PSs compared to dutasteride (4.88 × 10-3 ± 0.33 µM), suggesting a lower efficiency in inhibiting S5αR2. The in silico studies confirmed these observations, explained by the lower binding affinity identified for PSs to the enzyme's active site in the molecular docking studies and the reduced stability of the interactions with the active site of the enzyme during the molecular dynamics simulations compared to dutasteride. The results suggest that PSs exhibit low-to-negligible inhibitory activity against S5αR2 (µM range) compared to the synthetic inhibitor dutasteride (nM range). Among the three PSs studied, β-sitosterol showed the highest inhibitory activity and the best stability in its interaction with S5αR2, when compared with stigmasterol and campesterol.

Microscopic Structure of Neat Linear Alkylamine Liquids: An X-Ray Scattering and Computer Simulation Study

Linear amines, from propylamine to nonylamine, are studied under ambient conditions by X-ray scattering and molecular dynamics simulations of various force field models. The major finding is that the prepeak in alkylamines is about 1 order of magnitude weaker than that in alkanols, hence suggesting much weaker hydrogen bonding-induced clustering of the amine groups than for the hydroxyl groups. Computer simulation studies reveal that the OPLS-UA model reproduces the prepeak, but with larger amplitudes, while the GROMOS-UA and CHARMM-AA force fields show almost no prepeak. Simulations of all models show the existence of hydrogen-bonded clusters, equally confirmed by the prominent prepeak of the structure factor between the nitrogen atoms. The hydrogen bond strength, as modeled by the Coulomb association in classical force field models, is about the same order of magnitude for both systems. Then, one may ask what is the origin of the weaker prepeak in alkylamines? Simulation data reveal that the existence of the prepeak is controlled through the cancellation of the positive contributions from the charged group correlations by the negative contributions from the cross charged-uncharged correlations. The C2v symmetry of the amine headgroup hinders clustering, which favors cross correlations with the tail atoms. This is opposite to alkanols where the symmetry of the hydroxyl headgroup favors clustering and hinders cross correlations with the alkyl tail. This competition between charged and uncharged atomic groups appears as a general mechanism to explain the existence of scattering prepeaks, including their position and amplitude.

Accuracy and Reproducibility of Lipari-Szabo Order Parameters From Molecular Dynamics

The Lipari-Szabo generalized order parameter probes the picosecond to nanosecond time scale motions of a protein and is useful for rationalizing a multitude of biological processes such as protein recognition and ligand binding. Although these fast motions are an important and intrinsic property of proteins, it remains unclear what simulation conditions are most suitable to reproduce methyl symmetry axis side chain order parameter data (Saxis2) from molecular dynamics simulations. In this study, we show that, while Saxis2 tends to converge within tens of nanoseconds, it is essential to run 10 to 20 replicas starting from configurations close to the experimental structure to obtain the best agreement with experimental Saxis2 values. Additionally, in a comparison of force fields, AMBER ff14SB outperforms CHARMM36m in accurately capturing these fast time scale motions, and we suggest that the origin of this performance gap is likely attributed to differences in side chain torsional parametrization and not due to differences in the global protein conformations sampled by the force fields. This study provides insight into obtaining accurate and reproducible Saxis2 values from molecular simulations and underscores the necessity of using replica simulations to compute equilibrium properties.

Force Fields for Deep Eutectic Mixtures: Application to Structure, Thermodynamics and 2D-Infrared Spectroscopy

Parametrizing energy functions for ionic systems can be challenging. Here, the total energy function for an eutectic system consisting of water, SCN-, K+ and acetamide is improved vis-a-vis experimentally measured properties. Given the importance of electrostatic interactions, two different types of models are considered: the first (model M0) uses atom-centered multipole whereas the other two (models M1 and M2) are based on fluctuating minimal distributed charges (fMDCM) that respond to geometrical changes of SCN-. The Lennard-Jones parameters of the anion are adjusted to best reproduce experimentally known hydration free energies and densities, which are matched to within a few percent for the final models irrespective of the electrostatic model. Molecular dynamics simulations of the eutectic mixtures with varying water content (between 0 and 100%) yield radial distribution functions and frequency correlation functions for the CN-stretch vibration. Comparison with experiments indicates that models based on fMDCM are considerably more consistent than those using multipoles. Computed viscosities from models M1 and M2 are within 30% of measured values and their change with increasing water content is consistent with experiments. This is not the case for model M0.

Predicting the Antifungal Activity of Small Organic Compounds on Aspergillus niger Mold using Molecular Dynamics Simulations

Atomistic models of the plasma membrane of the pathogenic mold Aspergillus niger are developed. These models are described with an empirical molecular mechanical (MM) force field in combination with molecular dynamics (MD) simulations. The solvated plasma membrane models are brought into contact with 35 small organic compounds to observe their impact on a variety of membrane properties. All compounds are added at a constant total mass of 1% of the membrane mass. In addition, the ability of these compounds to inhibit the pathogenic cell growth of mold has been measured. Diffusion of compounds into the membrane model is readily observed during MD simulations. Changes in membrane properties found in simulations are not found to correlate with measured antifungal activities of compounds, suggesting that MD simulations of up to 1 μs are not sufficiently long to adequately describe compound-induced membrane disruption. However, properties related to the position and orientation of compounds relative to the membrane surface as well as hydrogen bonds formed between the compounds and the membrane show clear trends that correlate well with measured activities. A combination of these properties enables an activity prediction of compounds in good agreement with measurements. Activity is found predominantly for compounds that can be decomposed into a single continuous hydrophobic and hydrophilic moiety. Such active compounds can be energetically inserted most favorably into the membrane. These insertions destabilize the membrane by disrupting the internal membrane hydrogen bond network and by sliding between neighboring lipids, thereby separating them.

Computational design and evaluation of a polyvalent vaccine for viral nervous necrosis (VNN) in fish to combat Betanodavirus infection

Viral nervous necrosis (VNN) poses a significant threat to the aquaculture industry, causing substantial losses and economic burdens. The disease, attributed to nervous necrosis viruses within the Betanodavirus genus, is particularly pervasive in the Mediterranean region, affecting various fish species across all production stages with mortality rates reaching 100%. Developing effective preventive measures against VNN is imperative. In this study, we employed rigorous immunoinformatics techniques to design a novel multi-epitope vaccine targeting VNN. Five RNA-directed RNA polymerases, crucial to the lifecycle of Betanodavirus, were selected as vaccine targets. The antigenicity and favorable physicochemical properties of these proteins were confirmed, and epitope mapping identified cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte epitopes essential for eliciting a robust immune response. The selected epitopes, characterized by high antigenicity, non-allergenicity, and non-toxicity, were further enhanced by adding PADRE sequences and hBD adjuvants to increase immunogenicity. Two vaccine constructs were developed by linking epitopes using appropriate linkers, demonstrating high antigenicity, solubility, and stability. Molecular dynamics simulations revealed stable interactions between the vaccine constructs and Toll-like receptors (TLRs), essential for pathogen recognition and immune response activation in fish. Notably, vaccine construct V2 exhibited superior stability and binding affinity with TLR8, suggesting its potential as a promising candidate for VNN prevention. Overall, our study presents a comprehensive approach to VNN vaccine design utilizing immunoinformatics, offering safe, immunogenic, and effective solutions across multiple Betanodavirus species. Further experimental validation in model animals is recommended to fully assess the vaccine's efficacy. This research contributes to improved vaccine development against diverse fish pathogens by addressing emerging challenges and individualized immunization requirements in aquaculture.

Disruption of PHF6 Peptide Aggregation from Tau Protein: Mechanisms of Palmatine Chloride in Preventing Early PHF6 Aggregation

The formation of neurofibrillary tangles (NFTs), composed of tau protein aggregates, is a hallmark of neurodegenerative diseases known as tauopathies, including Alzheimer's disease (AD). NFTs consist of paired helical filaments (PHFs) of tau protein with a dominant β-sheet secondary structure. Within these PHFs, the PHF6 hexapeptide (Val306-Gln-Ile-Val-Tyr-Lys311) has been commonly highlighted as a key site for tau protein nucleation. Palmatine chloride (PC) has been identified as an inhibitor of PHF6 aggregation, capable of reducing aggregation propensity at submicromolar concentrations. In pursuit of novel anti-AD drugs targeting early tau aggregation stages, we conducted an in silico study to elucidate PC's mechanism of action during PHF6 aggregation. Our observations suggest that while PHF6 can still initiate self-aggregation in the presence of PC, PC molecules subtly influence PHF6 aggregation dynamics, favoring smaller aggregates over larger complexes. The study underlined the key roles of aromatic rings in PC binding to different PHF6 aggregates by interacting through π-π stacking with the PHF6 Tyr310 side chain. The presence of aromatic rings in compounds to be able to inhibit the earlier complexation phase seems to be essential. These in silico findings lay a foundation for the design of compounds that could intervene in resolving the neurotoxicity of protein aggregates in AD.

Indole-core inhibitors of influenza a neuraminidase: iterative medicinal chemistry and molecular modeling

Influenza viruses that cause seasonal and pandemic flu are a permanent health threat. The surface glycoprotein, neuraminidase, is crucial for the infectivity of the virus and therefore an attractive target for flu drug discovery campaigns. We have designed and synthesized more than 40 3-indolinone derivatives. We mainly investigated the role of substituents at the 2 position of the core as well as the introduction of substituents or a nitrogen atom in the fused phenyl ring of the core for inhibition of influenza virus neuraminidase activity and replication in vitro and in vivo. After evaluating the compounds for their ability to inhibit the viral neuraminidase, six potent inhibitors 3c, 3e, 7c, 12o, 12v, 18d were progressed to evaluate for cytotoxicity and inhibition of influenza virus A/PR/8/34 replication in in MDCK cells. Two hit compounds 3e and 12o were tested in an animal model of influenza virus infection. Molecular mechanism of the 3-indolinone derivatives interactions with the neuraminidase was revealed in molecular dynamic simulations. Proposed inhibitors bind to the 430-cavity that is different from the conventional binding site of commercial compounds. The most promising 3-indolinone inhibitors demonstrate stronger interactions with the neuraminidase in molecular models that supports proposed binding site.

Computational methods for the study of carboxylases: The case of crotonyl-CoA carboxylase/reductase

The rising levels of atmospheric CO2 and its impact on climate change call for new methods to transform this greenhouse gas into beneficial compounds. Carboxylases have a significant role in the carbon cycle, converting gigatons of CO2 into biomass annually. One of the most effective and fastest carboxylases is crotonyl-CoA carboxylase/reductase (Ccr). To understand its underlying mechanism, we have developed computational methods and protocols based on all-atom molecular dynamics simulations. These methods provide the CO2 binding locations and free energy inside the active site, dependent on different conformations adopted by Ccr and the presence of the crotonyl-CoA substrate. Furthermore, the adaptive string method and quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations outline the CO2 fixation reaction via two different mechanisms. The direct mechanism involves a hydride transfer creating a reactive enolate, which then binds the electrophilic CO2 molecule, resulting in the carboxylated product. Alternatively, another mechanism involves the formation of a covalent adduct. Our simulations suggest that this adduct serves to store the enolate in a much more stable intermediate avoiding its reduction side reaction, explaining the enzyme's efficiency. Overall, this work presents computational methods for studying carboxylation reactions using Ccr as a model, providing general principles that can be applied to modeling other carboxylases.

Single-molecule imaging and molecular dynamics simulations reveal early activation of the MET receptor in cells

Embedding of cell-surface receptors into a membrane defines their dynamics but also complicates experimental characterization of their signaling complexes. The hepatocyte growth factor receptor MET is a receptor tyrosine kinase involved in cellular processes such as proliferation, migration, and survival. It is also targeted by the pathogen Listeria monocytogenes, whose invasion protein, internalin B (InlB), binds to MET, forming a signaling dimer that triggers pathogen internalization. Here we use an integrative structural biology approach, combining molecular dynamics simulations and single-molecule Förster resonance energy transfer (smFRET) in cells, to investigate the early stages of MET activation. Our simulations show that InlB binding stabilizes MET in a conformation that promotes dimer formation. smFRET reveals that the in situ dimer structure closely resembles one of two previously published crystal structures, though with key differences. This study refines our understanding of MET activation and provides a methodological framework for studying other plasma membrane receptors.

AI-assisted generation and in-depth in-silico evaluation of potential inhibitor targeting aurora kinase A (AURKA): An anticancer discovery exploiting synthetic lethality approach

Genetic alterations are lead causative agents behind the complex pathologies of cancers which render all treatments unarmed. Such alterations in oncogenes can be treated by direct inhibition by specific drugs while alteration in tumor suppressor genes mediating loss of function is challenging to treat. Identification of synthetic lethal partners to specific tumor suppressor genes and mediating their inhibition can be a potential approach to deal with loss of function mutations. Aurora kinase A (AURKA) has been established as an effective synthetic lethal partner of several tumor suppressor genes and is overexpressed in cancerous conditions, mediating adverse pathologies. The present AI-assisted study deals with the generation of novel inhibitor compounds against AURKA and the exhaustive evaluation of the best compound using molecular docking, molecular dynamic simulation, MM/PBSA, and QM/MMGBSA-based analysis. Out of the 200 novel compounds generated using features of ATP binding pocket of AURKA and previously reported inhibitor, compound 1 (4-{5-fluoro-6-[(1Z)-3-hydrazinyl-3-oxo-2-phenylprop-1-en-1-yl]pyridin-2-yl}benzoic acid) was identified as the most potent candidate with high negative binding energy of -10.4 kcal/mol in molecular docking analysis. The molecular dynamic simulation analysis resulted in major conformational changes in the conserved DFG motif and loop 277-291 of AURKA in the apo-AURKA compared to AURKA-compound 1 complex thus maintaining open ATP binding cavity in apo-form and inhibiting the entry of ATP to its binding site in complex form. The free energy landscape displayed a persistence of folded states of the enzyme in complex form. The MM/PBSA revealed effective Gibb's free energy of binding of -11 kcal/mol for compound 1 inhibiting AURKA. The QM/MMGBSA analysis resulted in a significantly high negative binding energy of -13.98 kcal/mol proving significant inhibition potential of compound 1 against AURKA. Therefore, further in-vitro investigation can provide a novel effective, and safe treatment against a wide range of cancers by targeting a well-established cancer target AURKA.

Apigenin promotes cell death in NCI-H23 cells by upregulation of PTEN: potential involvement of the binding of apigenin with WWP2 protein

The tumour suppressor protein PTEN is often down-regulated in non-small cell lung cancer. A major protein promoting the lowering of the PTEN protein is WWP2. Polyphenols have been shown to promote the expression of tumour suppressor genes like PTEN. We carry out the study to check for the ability of apigenin to bind with the WWP2 protein using in-silico investigation comprising docking and simulation. We checked for the cytotoxic effect of apigenin upon the non-small cell lung cancer cell line NCI-H23. We checked the PTEN expression status at the gene and protein levels. The expression levels of the apoptotic regulators BCL2, BAX and CASPASE3 genes along with the activity levels of the caspase-3 protein were checked. The ultrastructure of the cells was analysed. Our Autodock analysis showed that apigenin bound favourably with the WWP2 protein. Molecular dynamics simulation revealed that apigenin increased the parameters of RMSD, Rg and SASA when bound with the WWP2 protein. The protein-ligand complex had hydrogen bonding and majorly van der Wal's interactions. PCA analysis revealed greater fluctuations in the apigenin-bound state of the protein. The mutant form of the WWP2 revealed similar results in the presence of apigenin. Apigenin showed efficacy against the NCI-H23 cell line and promoted PTEN protein levels, lowered BCL2 gene expression and up-regulated BAX and CASPASE3 gene expression. Increased caspase-3 activity and ultra-structural analysis revealed the occurrence of apoptosis. Thus the binding of apigenin with WWP2 could promote PTEN protein levels and lead to apoptotic activity in NCI-H23 cells.Communicated by Ramaswamy H. Sarma.

Engineering of Pseudomonas putida to produce medium-chain-length polyhydroxyalkanoate from crude glycerol

The development of biodegradable polymers is crucial for addressing environmental issues and waste management challenges, and a medium-chain-length polyhydroxyalkanoate(MCL-PHA) exhibits significant application potential in diverse industrial and environmental contexts owing to its versatility and biodegradability. Here, Pseudomonas putida was metabolically engineered to produce MCL-PHA from crude glycerol. To increase the precursor pool, we first deleted the phaC1ZC2 operon and introduced a plasmid-based overexpression of phaC2 and phaG, and the MCL-PHA content derived from glycerol increased to 18.27 wt% at 60 h. Subsequently, by optimizing the acoA expression through promoter selection and UTR design, the MCL-PHA content further increased to 19.93 wt% at 72 h. Additionally, a notable increase in MCL-PHA production was achieved using PhaC2 designed to have no substrate-trapping effect (PhaC2A477A478). This improvement was guided by filling structural data gaps using AlphaFold2 and docking simulations that revealed the substrate-trapping phenomenon. High-level production of MCL-PHA was achieved through fed-batch fermentation using the final engineered P. putida from refined glycerol, which yielded 34.9 g/L of MCL-PHA with 44.64 wt% at 180 h. Furthermore, using crude glycerol as the sole carbon source enabled the production of 49.5 g/L of MCL-PHA with 45.41 wt% at 180 h in fed-batch culture.

Exploring ligands that target von Willebrand factor selectively under oxidizing conditions through docking and molecular dynamics simulations

The blood protein von Willebrand factor (VWF) is a large multimeric protein that, when activated, binds to blood platelets, tethering them to the site of vascular injury and initiating blood coagulation. This process is critical for the normal hemostatic response, but especially under inflammatory conditions, it is thought to be a major player in pathological thrombus formation. For this reason, VWF has been the target for the development of anti-thrombotic therapeutics. However, it is challenging to prevent pathological thrombus formation while still allowing normal physiological blood coagulation, as currently available anti-thrombotic therapeutics are known to cause unwanted bleeding, in particular intracranial hemorrhage. This work explores the possibility of inhibiting VWF selectively under the inflammatory conditions present during pathological thrombus formation. In particular, the A2 domain of VWF is known to inhibit the neighboring A1 domain from binding to the platelet surface receptor GpIbα, and this auto-inhibitory mechanism has been shown to be removed by oxidizing agents released during inflammation. Hence, finding drug molecules that bind at the interface between A1 and A2 only under oxidizing conditions could restore such an auto-inhibitory mechanism. Here, by using a combination of computational docking, molecular dynamics simulations, and free energy perturbation calculations, a ligand from the ZINC15 database was identified that binds at the A1A2 interface, with the interaction being stronger under oxidizing conditions. The results provide a framework for the discovery of drug molecules that bind to a protein selectively in the presence of inflammatory conditions.

Pathogenic G6PD variants: Different clinical pictures arise from different missense mutations in the same codon

G6PD deficiency results from mutations in the X-linked G6PD gene. More than 200 variants are associated with enzyme deficiency: each one of them may either cause predisposition to haemolytic anaemia triggered by exogenous agents (class B variants), or may cause a chronic haemolytic disorder (class A variants). Genotype-phenotype correlations are subtle. We report a rare G6PD variant, discovered in a baby presenting with severe jaundice and haemolytic anaemia since birth: the mutation of this class A variant was found to be p.(Arg454Pro). Two variants affecting the same codon were already known: G6PD Union, p.(Arg454Cys), and G6PD Andalus, p.(Arg454His). Both these class B variants and our class A variant exhibit severe G6PD deficiency. By molecular dynamics simulations, we performed a comparative analysis of the three mutants and of the wild-type G6PD. We found that the tetrameric structure of the enzyme is not perturbed in any of the variants; instead, loss of the positively charged Arg residue causes marked variant-specific rearrangement of hydrogen bonds, and it influences interactions with the substrates G6P and NADP. These findings explain severe deficiency of enzyme activity and may account for p.(Arg454Pro) expressing a more severe clinical phenotype.

Engineered odorant receptors illuminate the basis of odour discrimination

How the olfactory system detects and distinguishes odorants with diverse physicochemical properties and molecular configurations remains poorly understood. Vertebrate animals perceive odours through G protein-coupled odorant receptors (ORs)1. In humans, around 400 ORs enable the sense of smell. The OR family comprises two main classes: class I ORs are tuned to carboxylic acids whereas class II ORs, which represent most of the human repertoire, respond to a wide variety of odorants2. A fundamental challenge in understanding olfaction is the inability to visualize odorant binding to ORs. Here we uncover molecular properties of odorant-OR interactions by using engineered ORs crafted using a consensus protein design strategy3. Because such consensus ORs (consORs) are derived from the 17 major subfamilies of human ORs, they provide a template for modelling individual native ORs with high sequence and structural homology. The biochemical tractability of consORs enabled the determination of four cryogenic electron microscopy structures of distinct consORs with specific ligand recognition properties. The structure of a class I consOR, consOR51, showed high structural similarity to the native human receptor OR51E2 and generated a homology model of a related member of the human OR51 family with high predictive power. Structures of three class II consORs revealed distinct modes of odorant-binding and activation mechanisms between class I and class II ORs. Thus, the structures of consORs lay the groundwork for understanding molecular recognition of odorants by the OR superfamily.

A new class of capsid-targeting inhibitors that specifically block HIV-1 nuclear import

HIV-1 capsids cross nuclear pore complexes (NPCs) by engaging with the nuclear import machinery. To identify compounds that inhibit HIV-1 nuclear import, we screened drugs in silico on a three-dimensional model of a CA hexamer bound by Transportin-1 (TRN-1). Among hits, compound H27 inhibited HIV-1 with a low micromolar IC50. Unlike other CA-targeting compounds, H27 did not alter CA assembly or disassembly, inhibited nuclear import specifically, and retained antiviral activity against PF74- and Lenacapavir-resistant mutants. The differential sensitivity of divergent primate lentiviral capsids, capsid stability and H27 escape mutants, together with structural analyses, suggest that H27 makes multiple low affinity contacts with assembled capsid. Interaction experiments indicate that H27 may act by preventing CA from engaging with components of the NPC machinery such as TRN-1. H27 exhibited good metabolic stability in vivo and was efficient against different subtypes and circulating recombinant forms from treatment-naïve patients as well as strains resistant to the four main classes of antiretroviral drugs. This work identifies compounds that demonstrate a novel mechanism of action by specifically blocking HIV-1 nuclear import.

Increasing the flexibility of the substrate binding pocket of Streptomyces phospholipase D can enhance its catalytic efficiency in soybean phosphatidylcholine

The catalytic efficiency of Streptomyces klenkii phospholipase D (SkPLD) in soybean phosphatidylcholine (soy-PC) processing is constrained by its acyl chain specificity. To address this limitation, we engineered the substrate-binding pocket of SkPLD to increase its flexibility. The mutant P343A/Y383L exhibited a 7.14-fold increase in catalytic efficiency toward soy-PC compared to the wild type. This enhancement was attributed to improved substrate-binding pocket flexibility, as evidenced by the significantly higher specific activity of the mutant toward PCs with various acyl chains (58.20-327.76 U/mg vs. 13.56-76.67 U/mg). Monomolecular film experiments demonstrated that the P343A/Y383L mutant reduced the energy barrier for PC binding, facilitating favorable interactions with the soy-PC monolayer. Molecular dynamics simulations revealed that the mutant's increased flexibility allowed for easier diffusion and penetration into the soy-PC monolayer, while the non-polar amino acids in the substrate-binding pocket promoted rapid interactions with the acyl chains of PC, ultimately leading to enhanced catalytic activity.

Elucidating the role of PPARG inhibition in enhancing MERS virus immune response: A network pharmacology and computational drug discovery

BACKGROUND

Middle East Respiratory Syndrome (MERS) has become a severe zoonotic disease, posing significant public health concerns due to the lack of specific medications. This urgently demands the development of novel therapeutic molecules. Understanding MERS's genetic underpinnings and potential therapeutic targets is crucial for developing effective treatments.

METHODS

Two gene expression datasets (GSE81909 and GSE100504) were analyzed to identify differentially expressed genes (DEGs) using GEO2R. Furthermore, gene ontology (GO), pathway enrichment analysis, and protein-protein interaction (PPI) network were performed to understand the gene's functions. A possible drug target was identified, and an FDA-approved drug library was screened against the selected target using molecular docking and validated the findings through molecular dynamics simulation, principal component analysis, free energy landscape, and MM/GBSA calculations.

RESULTS

The study on GSE81909 and GSE100504 datasets with icMERS and MOCK samples at 24 and 48 h revealed an upregulation in 73 and 267 DEGs, respectively. In the network pharmacology, STAT1, MX1, DDX58, EIF2AK2, ISG15, IFIT1, IFIH1, OAS1, IRF9, and OASL were identified as the top 10 hub genes. STAT1 was identified as the most connected hub gene among these top 10 hub genes, which plays a crucial role in the immune response to the MERS virus. Further study on STAT1 showed that PPARG helps reduce STAT1, which could modulate the immune response. Therefore, by inhibiting PPARG, the immunological response can be successfully enhanced. The known inhibitor of PPARG, 570 (Farglitazar), was used as a control. Further, screening using Tanimoto and K-mean clustering was performed, from which three compounds were identified: 2267, 3478, and 40326. Compound 3478 showed characteristics similar to the control, indicating robust binding to PPARG. 3478 showed the highest negative binding free energy with -41.20 kcal/mol, indicating strong binding with PPARG.

CONCLUSIONS

These findings suggest that 3478 promises to be a potential inhibitor of PPARG, and further experimental investigations can explore its potential as a MERS inhibitor.

The application of multi-scale simulation in advanced electronic packaging

Electronic packaging is an essential branch of electronic engineering that aims to protect electronic, microelectronic, and nanoelectronic systems from environmental conditions. The design of electronic packaging is highly complex and requires the consideration of multi-physics phenomena, such as thermal transport, electromagnetic fields, and mechanical stress. This review presents a comprehensive overview of the multiphysics coupling of electric, magnetic, thermal, mechanical, and fluid fields, which are crucial for assessing the performance and reliability of electronic devices. The recent advancements in multi-scale simulation techniques are also systematically summarized, such as finite element methods at the macroscopic scale, molecular dynamics and density functional theory at the microscopic scale, and particularly machine learning methods for bridging different scales. Additionally, we illustrate how these methods can be applied to study various aspects of electronic packaging, such as material properties, interfacial failure, thermal management, electromigration, and stress analysis. The challenges and the potential applications of multi-scale simulation techniques in electronic packaging are also highlighted. Further, some future directions for multi-scale simulation techniques in electronic packaging are concluded for further investigation.

Shifting redox reaction equilibria on demand using an orthogonal redox cofactor

Nature's two redox cofactors, nicotinamide adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate (NADP+), are held at different reduction potentials, driving catabolism and anabolism in opposite directions. In biomanufacturing, there is a need to flexibly control redox reaction direction decoupled from catabolism and anabolism. We established nicotinamide mononucleotide (NMN+) as a noncanonical cofactor orthogonal to NAD(P)+. Here we present the development of Nox Ortho, a reduced NMN+ (NMNH)-specific oxidase, that completes the toolkit to modulate NMNH:NMN+ ratio together with an NMN+-specific glucose dehydrogenase (GDH Ortho). The design principle discovered from Nox Ortho engineering and modeling is facilely translated onto six different enzymes to create NMN(H)-orthogonal biocatalysts with a consistent ~103-106-fold cofactor specificity switch from NAD(P)+ to NMN+. We assemble these enzymes to produce stereo-pure 2,3-butanediol in cell-free systems and in Escherichia coli, enabled by NMN(H)'s distinct redox ratio firmly set by its designated driving forces, decoupled from both NAD(H) and NADP(H).

Enhancement of atmospheric nucleation precursors on formic sulfuric anhydride induced nucleation: Theoretical mechanism

As an intermediate formed by H2SO4 (SA), formic sulfate anhydride (FSA) has been hypothesized to play a role in the nucleation of atmospheric aerosols. It is the first time that the clusters (SA)x(A)y(W)n and (FSA)x(A)y(W)n (x = 1-2; y = 1-2; n = 0-4) were systematically studied in theory on the structures, thermodynamics, intermolecular interactions, humidity dependence, atmospheric dependence and optical properties. FSA is predicted to be more stronger to promote the clustering with ammonia (A) than SA, suggesting that substituent group enhances nucleation capability of FSA. Whereas, the substituent group does not influence the humidity sensitivity of hydrated clusters. The clusters trend to form small hydrated clusters (nwater≦3). The study on atmospheric dependence indicates that the stability of the clusters depends more on temperature other than pressure. Moreover, FSA shows a stronger ability on reducing atmospheric visibility than A, SA and water molecules. This finding aims to draw attention to FSA about atmospheric nucleation.

Mechanistic insights into G-protein coupling with an agonist-bound G-protein-coupled receptor

G-protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by promoting guanine nucleotide exchange. Here, we investigate the coupling of G proteins with GPCRs and describe the events that ultimately lead to the ejection of GDP from its binding pocket in the Gα subunit, the rate-limiting step during G-protein activation. Using molecular dynamics simulations, we investigate the temporal progression of structural rearrangements of GDP-bound Gs protein (Gs·GDP; hereafter GsGDP) upon coupling to the β2-adrenergic receptor (β2AR) in atomic detail. The binding of GsGDP to the β2AR is followed by long-range allosteric effects that significantly reduce the energy needed for GDP release: the opening of α1-αF helices, the displacement of the αG helix and the opening of the α-helical domain. Signal propagation to the Gs occurs through an extended receptor interface, including a lysine-rich motif at the intracellular end of a kinked transmembrane helix 6, which was confirmed by site-directed mutagenesis and functional assays. From this β2AR-GsGDP intermediate, Gs undergoes an in-plane rotation along the receptor axis to approach the β2AR-Gsempty state. The simulations shed light on how the structural elements at the receptor-G-protein interface may interact to transmit the signal over 30 Å to the nucleotide-binding site. Our analysis extends the current limited view of nucleotide-free snapshots to include additional states and structural features responsible for signaling and G-protein coupling specificity.

E2F1-induced autocrine IL-6 inflammatory loop mediates cancer-immune crosstalk that predicts T cell phenotype switching and therapeutic responsiveness

Melanoma is a metastatic, drug-refractory cancer with the ability to evade immunosurveillance. Cancer immune evasion involves interaction between tumor intrinsic properties and the microenvironment. The transcription factor E2F1 is a key driver of tumor evolution and metastasis. To explore E2F1's role in immune regulation in presence of aggressive melanoma cells, we established a coculture system and utilized transcriptome and cytokine arrays combined with bioinformatics and structural modeling. We identified an E2F1-dependent gene regulatory network with IL6 as a central hub. E2F1-induced IL-6 secretion unleashes an autocrine inflammatory feedback loop driving invasiveness and epithelial-to-mesenchymal transition. IL-6-activated STAT3 physically interacts with E2F1 and cooperatively enhances IL-6 expression by binding to an E2F1-STAT3-responsive promoter element. The E2F1-STAT3/IL-6 axis strongly modulates the immune niche and generates a crosstalk with CD4+ cells resulting in transcriptional changes of immunoregulatory genes in melanoma and immune cells that is indicative of an inflammatory and immunosuppressive environment. Clinical data from TCGA demonstrated that elevated E2F1, STAT3, and IL-6 correlate with infiltration of Th2, while simultaneously blocking Th1 in primary and metastatic melanomas. Strikingly, E2F1 depletion reduces the secretion of typical type-2 cytokines thereby launching a Th2-to-Th1 phenotype shift towards an antitumor immune response. The impact of activated E2F1-STAT3/IL-6 axis on melanoma-immune cell communication and its prognostic/therapeutic value was validated by mathematical modeling. This study addresses important molecular aspects of the tumor-associated microenvironment in modulating immune responses, and will contribute significantly to the improvement of future cancer therapies.

Alpha-1 antitrypsin inhibits pertussis toxin

Pertussis (whooping cough) is a vaccine-preventable but re-emerging, highly infectious respiratory disease caused by Bordetella pertussis. There are currently no effective treatments for pertussis, complicating care for nonvaccinated individuals, especially newborns. Disease manifestations are predominantly caused by pertussis toxin (PT), a pivotal virulence factor classified as an ADP-ribosylating AB-type protein toxin. In this work, an unbiased approach using peptide libraries, bioassay-guided fractionation and mass spectrometry revealed α1-antitrypsin (α1AT) as a potent PT inhibitor. Biochemistry-, cell culture-, and molecular modeling-based in vitro experimentation demonstrated that the α1AT mode of action is based on blocking PT-binding to the host target cell surface. In the infant mouse model of severe pertussis, α1AT expression was reduced upon infection. Further, systemic administration of α1AT significantly reduced B. pertussis-induced leukocytosis, which is a hallmark of infant infection and major risk factor for fatal pertussis. Taken together our data demonstrates that α1AT is a novel PT inhibitor and that further evaluation and development of α1AT as a therapeutic agent for pertussis is warranted. Importantly, purified α1AT is already in use clinically as an intravenous augmentation therapy for those with genetic α1AT deficiency and could be repurposed to clinical management of pertussis.

Ionic flow through partially blocked nanopores

Employing atomistic molecular dynamics simulations, we investigate the ionic conductivity mechanisms in a partially blocked nanopore containing a centrally positioned spherical constriction, exploring the effects of pore diameter, surface charge, and blockage size. Our results show that ionic mobilities are significantly influenced by the polarity of the surface charge and the size of the pore gap. Particularly, we observe ion-specific effects for K+ and Cl- ions based on their size and charge, especially in sub-nanometer pore gaps. Furthermore, we find that the current flow in partially blocked nanopores sensitively depends on the surface charges, consistent with the calculated free energy profiles. The percentage of the current drop is found to be correlated to the volume of the spherical constriction with the effects more pronounced when the sizes of the spherical blockage and nanopore are comparable.

Unlocking the potential of biocompatible chitosan-hyaluronic acid nanogels labeled with fluorochromes: A promising step toward enhanced FRET bioimaging

Chitosan is a natural polysaccharide widely used in medical formulations as nanoparticles due to their special properties. Our work aimed to assess the biocompatibility of chitosan-hyaluronic acid nanogels labeled with fluorochromes for use in biomedical applications, based on the FRET effect. The preparation method included the ionic gelation, grafting rhodamine or fluorescein isothiocyanate molecules onto the chitosan backbone. To assess the potential applications as fluorescence imaging tools of chitosan-fluorophores conjugates in diagnostics and therapies, SVEC4-10 cells (simian virus 40-transformed mouse microvascular endothelial cell line) and RAW264.7 murine macrophages were used within this study. Good biocompatibility was observed after 6 and 24 h of incubation with nanogels, with no increase in cell death or membrane damage for concentrations up to 120 μg/mL. Both types of fluorescent nanogels presented the tendency to agglomerate on the cell membrane's surface, and few cells were internalized, especially at the periphery of cells. Molecular dynamics simulations showed that distances between fluorophores fitted at values close to those calculated based on FRET experiments. These formulations can further incorporate gadolinium for better nanomedicine tools.

Investigating the self-assembly of pH-sensitive switchable diamine surfactant using sum frequency generation spectroscopy and molecular dynamics simulations

The responses of the N-alkyl diamine groups to variations in pH affect their conformations and surface activities, making them relevant to applications relying on interfacial interactions, such as controlled emulsification and mineral flotation. An in-depth understanding of interfacial self-assembly is crucial. Herein, a molecular-level study was performed to investigate the adsorption and self-assembly of N-dodecylpropane-1,3-diamine (DPDA) at the air-water (A/W) interface using sum frequency generation (SFG) spectroscopy and molecular dynamics (MD) simulations. The SFG spectra of DPDA, acquired under three pH conditions, suggest that the protonation of the DPDA diamine group influences the alkyl chain arrangement at a varying degree at the A/W interface. Analysis of the di-cationic DPDA SFG spectrum at a low pH showed fewer gauche defects at low concentration, as indicated by the relatively higher intensity ratio (ICH3SS/ICH2SS) of 18.1 ± 0.6. The density profiles from MD simulations at different surface areas per molecule and pH conditions, showing varying degrees of packing, support the observation of gauche defects in SFG. With MD simulation, the radial distribution factor for di-cationic species has the highest probability of forming hydrogen bonds compared to mono-cationic and non-ionic species. These g(r) probability results conform with observations obtained from SFG spectroscopy, where we observed a strong hydrogen bond interaction at low pH conditions with di-cationic species, forming tetrahedrally arranged water molecules at the A/W interface. Overall, comprehensive insights will facilitate the visualization of alkyl diamines and their potential derivatives at the A/W interface, enabling a better understanding of their behavior across various applications.

Computer-Aided Identification and Design of Ligands for Multi-Targeting Inhibition of a Molecular Acute Myeloid Leukemia Network

BACKGROUND/OBJECTIVES

Acute myeloid leukemia (AML) is characterized by therapeutic failure and long-term risk for disease relapses. As several therapeutic targets participate in networks, they can rewire to eventually evade single-target drugs. Hence, multi-targeting approaches are considered on the expectation that interference with many different components could synergistically hinder activation of alternative pathways and demolish the network one-off, leading to complete disease remission.

METHODS

Herein, we established a network-based, computer-aided approach for the rational design of drug combinations and de novo agents that interact with many AML network components simultaneously.

RESULTS

A reconstructed AML network guided the selection of suitable protein hubs and corresponding multi-targeting strategies. For proteins responsive to existing drugs, a greedy algorithm identified the minimum amount of compounds targeting the maximum number of hubs. We predicted permissible combinations of amiodarone, artenimol, fostamatinib, ponatinib, procaine, and vismodegib that interfere with 3-8 hubs, and we elucidated the pharmacological mode of action of procaine on DNMT3A. For proteins that do not respond to any approved drugs, namely cyclins A1, D2, and E1, we used structure-based de novo drug design to generate a novel triple-targeting compound of the chemical formula C15H15NO5, with favorable pharmacological and drug-like properties.

CONCLUSIONS

Overall, by integrating network and structural pharmacology with molecular modeling, we determined two complementary strategies with the potential to annihilate the AML network, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. These target-drug interactions could be prioritized for preclinical and clinical testing toward precision medicine for AML.

p70S6K as a Potential Anti-COVID-19 Target: Insights from Wet Bench and In Silico Studies

The onset of SARS-CoV-2 infection in 2019 sparked a global COVID-19 pandemic. This infection is marked by a significant rise in both viral and host kinase activity. Our primary objective was to identify a pivotal host kinase essential for COVID-19 infection and the associated phenomenon of the cytokine storm, which may lead to long-term COVID-19 complications irrespective of viral genetic variations. To achieve this, our study tracked kinase phosphorylation dynamics in RAW264.7 macrophages following SPIKE transfection over time. Among the kinases surveyed, p70S6K (RPS6KB1) exhibited a 3.5-fold increase in phosphorylation at S418. This significant change prompted the selection of p70S6K for in silico investigation, utilizing its structure bound to M2698 (PDB: 7N93). M2698, an oral dual Akt/p70S6K inhibitor with an IC50 of 1.1 nM, exhibited psychosis side effects in phase I clinical trials, potentially linked to its interaction with Akt2. Our secondary objective was to discover a small-molecule analogue of M2698 that exhibits a distinct binding preference for p70S6K over Akt2 through computational modeling and analysis. The in silico part of our project began with validating the prediction accuracy of the docking algorithm, followed by an OCA analysis pinpointing specific atoms on M2698 that could be modified to enhance selectivity. Subsequently, our investigation led to the identification of an analog of M2698, designated as S34, that showed a superior docking score towards p70S6K compared to Akt2. To further assess the stability of S34 in its protein-ligand (PL) complexes with p70S6K and Akt2, MD simulations were conducted. These simulations suggest that S34, on average, forms two hydrogen bond interactions with p70S6K, whereas it only forms one hydrogen bond interaction with Akt2. This difference in hydrogen bond interactions likely contributed to the observed larger root mean square deviation (RMSD) of 0.3 nm in the S34-Akt2 complex, compared to 0.1 nm in the S34-p70S6K complex. Additionally, we calculated free binding energy to predict the strength of the binding interactions of S34 to p70S6K and Akt2, which showed ~2-fold favorable binding affinity of S34 in the p70S6K binding pocket compared to that in the Akt2 binding pocket. These observations may suggest that the S34-p70S6K complex is more stable than the S34-Akt2 complex. Our work focused on identifying a host kinase target and predicting the binding affinity of a novel small molecule to accelerate the development of effective treatments. The wet bench results specifically highlight p70S6K as a compelling anti-COVID-19 target. Meanwhile, our in silico investigations address the known off-target effects associated with M2698 by identifying a close analog called S34. In conclusion, this study presents novel and intriguing findings that could potentially lead to clinical applications with further investigations.