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

Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set

Small molecule empirical force fields (FFs), including the CHARMM General Force Field (CGenFF), are designed to have wide coverage of organic molecules and to rapidly assign parameters to molecules not explicitly included in the FF. Assignment of parameters to new molecules in CGenFF is based on a trained bond-angle-dihedral charge increment linear interpolation scheme for the partial atomic charges along with bonded parameters assigned based on analogy using a rules-based penalty score scheme associated with atom types and chemical connectivity. Accordingly, the accuracy of CGenFF is related to the extent of the training set of available parameters. In the present study that training set is extended by 1390 molecules selected to represent connectivities new to CGenFF training compounds. Quantum mechanical (QM) data for optimized geometries, bond, valence angle, and dihedral angle potential energy scans, interactions with water, molecular dipole moments, and electrostatic potentials were used as target data. The resultant bonded parameters and partial atomic charges were used to train a new version of the CGenFF program, v5.0, which was used to generate parameters for a validation set of molecules, including drug-like molecules approved by the FDA, which were then benchmarked against both experimental and QM data. CGenFF v5.0 shows overall improvements with respect to QM intramolecular geometries, vibrations, dihedral potential energy scans, dipole moments and interactions with water. Tests of pure solvent properties of 216 molecules show small improvements versus the previous release of CGenFF v2.5.1 reflecting the high quality of the Lennard-Jones parameters that were explicitly optimized during the initial optimization of both the CGenFF and the CHARMM36 force field. CGenFF v5.0 represents an improvement that is anticipated to more accurately model intramolecular geometries and strain energies as well as noncovalent interactions of drug-like and other organic molecules.

Accelerated peptide bond formation at air-water interfaces

Peptides and proteins, essential components of living organisms, are composed of amino acids linked by peptide bonds. However, the mechanism of peptide bond formation during the prebiotic era remains unclear. In this study, advanced Born-Oppenheimer molecular dynamics (BOMD) simulations were used to investigate the mechanisms and kinetics of peptide bond formation at air-water interfaces using diglycine, the simplest dipeptide, as a model molecule. The results show that peptide bonds can be rapidly formed via a unique isomerization-then-OH--elimination pathway. In this mechanism, the diglycine initially isomerizes into its acidic form at the air-water interface, followed by a reaction that releases an OH- anion rather than the previously hypothesized H2O. The free-energy barriers for the interfacial pathway with the assistance of an interfacial electric field are much lower than those in the gas phase by >25 kcal/mol. Further calculations suggest that this mechanism can be extended to the formation of some larger peptides, such as tetraglycine. This pathway offers insights into the origin of life and could inform the development of methods for peptide synthesis.

Mutagenesis-based optimal design of plant peptide phytosulfokine for enhanced biological activity

Recognition of phytosulfokine (PSK), a sulfated pentapeptide, by its receptor PSKRs is crucial in regulating plant growth, development, and reproduction. However, designing highly active PSK remains a formidable challenge due to the lack of understanding of the structure-property relationship, structural dynamics, and the binding characteristics of PSK. Here, with a combined theoretical and experimental approach, we have investigated the binding dynamics of key interactions between PSK and AtPSKR1LRR to reveal the molecular mechanism of PSK recognition. Our molecular dynamics simulations and free energy perturbation calculations demonstrate that the sulfated tyrosines (PSKsY1 and PSKsY3) are indispensable for forming stable PSK-AtPSKR1LRR complex, while the alanine substitution at PSKQ5 site is rather tolerated. Furthermore, two promising PSK peptide analogs (PSKQ5A and PSKQ5K) with enhanced biological activity have been designed through in silico mutagenesis studies and in vivo experiments. They have a strong promoting effect (20 % enhancement) on stimulating root development compared with the wild-type PSK treatment. This work offers an effective strategy to design new peptide-based drugs for facilitating plant growth and consequent crop productivity, potentially benefiting efforts to address the global food crisis.

FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free-Energy Calculation Workflow for Drug Discovery

The binding affinity between a drug molecule and its target, measured by the absolute binding free energy (ABFE), is a crucial factor in the lead discovery phase of drug development. Recent research has highlighted the potential of in silico ABFE predictions to directly aid drug development by allowing for the ranking and prioritization of promising candidates. This work introduces an open-source Python workflow called FEP-SPell-ABFE, designed to automate ABFE calculations with minimal user involvement. The workflow requires only three key inputs: a receptor protein structure in PDB format, candidate ligands in SDF format, and a configuration file (config.yaml) that governs both the workflow and molecular dynamics simulation parameters. It produces a ranked list of ligands along with their binding free energies in the comma-separated values (CSV) format. The workflow leverages SLURM (Simple Linux Utility for Resource Management) for automating task execution and resource allocation across the modules. A usage example and several benchmark systems for validation are provided. The FEP-SPell-ABFE workflow, along with a practical example, is publicly accessible on GitHub at https://github.com/freeenergylab/FEP-SPell-ABFE, distributed under the MIT License.

Detection of Putative Ligand Dissociation Pathways in Proteins Using Site-Identification by Ligand Competitive Saturation

Drug efficacy often correlates better with dissociation kinetics than binding affinity alone. To study binding kinetics computationally, it is necessary to identify all of the possible ligand dissociation pathways. The site identification by ligand competitive saturation (SILCS) method involves the precomputation of a set of maps (FragMaps), which describe the free energy landscapes of typical chemical functionalities in and around a target protein or RNA. In the current work, we present and implement a method to use SILCS to identify ligand dissociation pathways, termed "SILCS-Pathway." The A* pathfinding algorithm is utilized to enumerate ligand dissociation pathways between the ligand binding site and the surrounding bulk solvent environment defined on evenly spaced points around the protein based on a Fibonacci lattice. The cost function for the A* algorithm is calculated using the SILCS exclusion maps and the SILCS grid free energy scores, thereby identifying paths that account for local protein flexibility and potential favorable interactions with the ligand. By traversing all evenly distributed bulk solvent points around the protein, we located all possible dissociation pathways and clustered them to identify general ligand unbinding pathways. The procedure is verified by using proteins studied previously with enhanced sampling molecular dynamics (MD) techniques and is shown to be capable of capturing important ligand dissociation routes in a highly computationally efficient manner. The identified pathways will act as the foundation for determining ligand dissociation kinetics using SILCS free energy profiles, which will be described in a subsequent article.

The Dynamical Asymmetry in SARS-CoV2 Protease Reveals the Exchange Between Catalytic Activity and Stability in Homodimers

The molecular approach to understanding the mechanisms of emerging diseases, like COVID-19, has largely accelerated the search for successful therapeutical strategies. In this work, we present an extensive molecular dynamics (MD) analysis of two forms of the SARS-CoV-2 main protease MPro. We analyzed the free form (apo) and compared the results with those coming from the (holo) form bound to the inhibitor Boceprevir, an FDA-approved drug repurposed for COVID-19 therapy. We applied Dynamic Cross Correlation (DCC) analysis to the MD simulations to trace the concerted motion patterns within the protein structure. Although symmetric, the homodimer in the bound form showed clearly asymmetric dynamical behavior. In particular, the presence of concerted motions was detected in the protomer where the expulsion of the substrate from the active site happened. Such behavior was not observed in the same time lapses in the apo form. These results highlight a sort of 'symmetry breaking', making a symmetric structure to display functional induced asymmetric behavior in response to a perturbation. This highly coordinated dynamics in response to an external cue confirms the character of 'complex molecular machines' of biopolymers.

Antiviral Activity of Halogenated Compounds Derived from L-Tyrosine Against SARS-CoV-2

INTRODUCTION

Currently, there are no effective medications for treating all the clinical conditions of patients with COVID-19. We aimed to evaluate the antiviral activity of compounds derived from L-tyrosine against the B.1 lineage of SARS-CoV-2 in vitro and in silico.

METHODOLOGY

The cytotoxicities of 15 halogenated compounds derived from L-tyrosine were evaluated in Vero-E6 cells by the MTT assay. The antiviral activity of the compounds was evaluated using four strategies, and viral quantification was performed by a plaque assay and qRT-PCR. The toxicity of the compounds was evaluated by ADMET predictor software. The affinity of these compounds for viral or cellular proteins and the stability of their conformations were determined by docking and molecular dynamics, respectively.

RESULTS

TODC-3M, TODI-2M, and YODC-3M reduced the viral titer >40% and inhibited the replication of viral RNA without significant cytotoxicity. In silico analyses revealed that these compounds presented low toxicity and binding energies between -4.3 and -5.2 Kcal/mol for three viral proteins (spike, Mpro, and RdRp). TODC-3M and YODC-3M presented the most stable conformations with the evaluated proteins.

CONCLUSIONS

The most promising compounds were TODC-3M, TODI-2M, and YODC-3M, which presented low in vitro and in silico toxicity, antiviral potential through different strategies, and favorable affinities for viral targets. Therefore, they are candidates for in vivo studies.

Anomalous membrane organization by omega-6 and omega-9 fatty acids

Omega fatty acids are currently being marketed as healthy food supplements as they have been implicated in multiple pathophysiological conditions, such as reducing plaque formation of Aβ peptide and inhibiting SARS-CoV-2 infection. Their mode of action has been hypothesized to be via membrane reorganization by the unsaturated acyl chains, leading to the modulation of lipid-protein cross-talk. However, the lack of molecular details led us to evaluate the molecular effect of omega-6 (linolenic acid) and omega-9 (oleic acid) fatty acids on membrane organization using a consolidated approach of fluorescence spectroscopy and all-atom molecular dynamics simulation. Our results show that the effect of these omega fatty acids is sensitive to their protonation states. Contrary to the accepted notion that chain unsaturation causes membrane disordering, both experimental and simulation results demonstrate that protonated linoleic acid promotes membrane ordering, despite having two unsaturations at the fatty acyl chain. However, protonated oleic fatty acid, with reduced unsaturation, disordered the acyl chain area of the lipid membranes. Equally surprisingly, deprotonated oleic acid orders, whereas deprotonated linoleic acid disorders, the membrane core region. Interestingly, while the lipid order parameter measurements from simulations did not capture these subtle differences, the calculated rotational autocorrelation function of a membrane dye was in line with experimentally measured apparent rotational correlation times. Our work provides a comprehensive revised molecular picture of the effect of omega fatty acids on membranes and highlights the importance of rigorous comparative approaches, as experimental and simulation studies in isolation can sometimes lead to inconsistent results.

Changes in the dynamic characteristics of G-protein can alter the immune-protection efficacy of rabies virus vaccine

The efficacy of the G-protein is influenced by N-linked glycosylation, which serves as the sole immunogen of the rabies virus vaccine. However, achieving satisfactory immune-protection efficacy remains challenging, owing to the heterogeneous glycosylation of G-proteins. Within molecular dynamics, examining the impact of N-glycan heterogeneity on the structural characteristics of G-proteins provides insights into the relationship between antigens and the efficacy of rabies virus vaccines. Glycosylation is regulated by host cells. In rabies virus cultured in Vero cells (VRV), all N-glycosylation sites of the G-protein underwent modification. In contrast, rabies virus G-protein cultured in KMB17 cells (human diploid cell vaccine [HDCV]) was only modified by N-glycans at amino acid positions 247 and 319. Furthermore, treatment of VRV with de-glycosylation significantly improved its immune-protective efficacy, whereas de-glycosylation did not alter the immune-protective efficacy of HDCV. To support the impact of glycosylation on VRV efficacy, the structures and dynamics of G-proteins were analyzed using GROMACS. Specifically, the hydrophobicity, flexibility, and radius of gyration of the G-protein trimer in VRV were significantly altered by excessive hydrogen bonds formed by the three-branched hybrid glycan at the aa 319 site. These changes increase the instability of the G-protein trimer and may lead to a decrease in vaccine protective efficacy. Ultimately, we determined that N-glycan heterogeneity affects the immune-protection effect of antigen proteins by altering their dynamic characteristics, enhancing our understanding of the correlation between antigen structural characteristics and efficacy.

IMPORTANCE

N-glycosylation of rabies virus glycoprotein dynamically regulates protein folding, stability, and antigenicity. Therefore, regulation of N-glycan modification is key to improving vaccine stability and protective efficacy. How the type and modification sites of N-glycans affect the protective efficacy of rabies vaccines remains unclear. Our research indicates that there are differences in the protective efficacy of rabies virus G-proteins modified with different N-glycans. Moreover, the modification of the three-branched hybrid glycan at the aa 319 site of G-protein significantly altered the hydrophobicity, flexibility, and radius, and increased its trimeric antigen instability through molecular dynamics demonstrations. These findings update the current understanding of the impact of glycans on vaccine antigenicity and develop a system to evaluate the stability of antigen glycoproteins based on molecular dynamics.

Crystal Structure, Modeling, and Identification of Key Residues Provide Insights into the Mechanism of the Key Toxoflavin Biosynthesis Protein ToxD

Toxoflavin, a toxic secondary metabolite produced by a variety of bacteria, has been implicated as a causative agent in food poisoning and a virulence factor in phytopathogenic bacteria. This toxin is produced by genes encoded in the tox operon in Burkholderia glumae, in which the encoded protein, ToxD, was previously characterized as essential for toxoflavin production. To better understand the function of ToxD in toxoflavin biosynthesis and provide a basis for future work to develop inhibitors of ToxD, we undertook the identification of structurally and catalytically important amino acid residues through a combination of X-ray crystallography and site directed mutagenesis. We solved the structure of BgToxD, which crystallized as a dimer, to 1.8 Å resolution. We identified a citrate molecule in the putative active site. To investigate the role of individual residues, we used Pseudomonas protegens Pf-5, a BL1 plant protective bacterium known to produce toxoflavin, and created mutants in the ToxD-homologue PFL1035. Using a multiple sequence alignment and the BgToxD structure, we identified and explored the functional importance of 12 conserved residues in the putative active site. Eight variants of PFL1035 resulted in no observable production of toxoflavin. In contrast, four ToxD variants resulted in reduced but detectable toxoflavin production suggesting a nonessential role. The crystal structure and structural models of the substrate and intermediate bound enzyme provide a molecular interpretation for the mutagenesis data.

Weighted Ensemble Simulations Reveal Novel Conformations and Modulator Effects in Hepatitis B Virus Capsid Assembly

Molecular dynamics (MD) simulations provide a detailed description of biophysical processes allowing mechanistic questions to be addressed at the atomic level. The promise of such approaches is partly hampered by well known sampling issues of typical simulations, where time scales available are significantly shorter than the process of interest. For the system of interest here, the binding of modulators of Hepatitis B virus capsid self-assembly, the binding site is at a flexible protein-protein interface. Characterization of the conformational landscape and how it is altered upon ligand binding is thus a prerequisite for a complete mechanistic description of capsid assembly modulation. However, such a description can be difficult due to the aforementioned sampling issues of standard MD, and enhanced sampling strategies are required. Here we employ the Weighted Ensemble methodology to characterize the free-energy landscape of our earlier determined functionally relevant progress coordinates. It is shown that this approach provides conformations outside those sampled by standard MD, as well as an increased number of structures with correspondingly enlarged binding pockets conducive to ligand binding, illustrating the utility of Weighted Ensemble for computational drug development.

Mechanistic Insight on Ethanol Driven Swelling and Disruption of Cholesterol Containing Biomimetic Vesicles From Coarse-Grained Molecular Dynamics

We have performed coarse-grained (CG) molecular dynamics (MD) simulations to delineate the impact of ethanol (EtOH) on cholesterol (CHOL) containing biomimetic bilayer and vesicle composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipids. We have first deduced the missing interaction parameters for the POPC-CHOL-EtOH-water system within the SPICA/SDK CG force-field (CG-FF). By monitoring the electron density profiles, the orientational order parameter, and reproducing the all-atom MD-derived free energy for the insertion of ethanol from the bulk aqueous phase to the hydrophobic core of the POPC-CHOL lipid bilayer, we successfully determined all the missing non-bonding interaction parameters for the POPC-CHOL-EtOH-water system. The proposed force field was applied to investigate the effect of ethanol at various concentrations on unilamellar vesicles composed of POPC and cholesterol. It was found that 40 mol% or more concentration of ethanol is required to disintegrate or rupture the POPC-CHOL vesicle membranes. While cholesterol offers some resilience against the detrimental effects of ethanol, we still observe an increase in vesicle size (swelling) and a contraction in the bilayer thickness (thinning) as ethanol concentration rises from 0 to 30 mol%. At ethanol concentrations exceeding 30 mol%, the vesicles become increasingly susceptible to disintegration due to enhanced penetration of ethanol and water molecules into the hydrophobic core of the membranes.

Integrated virtual screening and compound generation targeting H275Y mutation in the neuraminidase gene of oseltamivir-resistant influenza strains

Neuraminidase (NA) is an essential enzyme located at the outer layer of the influenza virus and plays a key role in the release of virions from infected cells. The rising incidence of global epidemics has made the urgent need for effective antiviral medications an urgent public health priority. Furthermore, the emergence of resistance caused by specific mutations in the influenza viral genome exacerbates the challenges of antiviral therapy. In view of this, this study aims to identify and analyse possible inhibitors of NA from different subtypes of influenza viruses. Initially, a thorough search was conducted in the Protein Data Bank (PDB) to gather structures of NA proteins that were attached with oseltamivir, a widely recognized inhibitor of NA. Here, 36 PDB entries were found with NA-oseltamivir complexes which were studied to evaluate the diversity and mutations present in various subtypes. Finally, N1(H1N1) protein was selected that demonstrated low IC50 value of oseltamivir with mutation H275Y. In addition, the study utilized BiMODAL generative model to generate 1000 novel molecules with comparable structures to oseltamivir. A QSAR model, based on machine learning (ML), was built utilizing the ChEMBL database to improve the selection process of candidate inhibitors. These inhibitors were subsequently analysed by molecular docking and further the best hits compounds (compound_375, compound_106 and compound_597) were appended to make a bigger molecule (compound_106-375, compound_106-597, and compound_375-597) to fit into the binding pocket of protein. Further, triplicate molecular dynamics simulations lasting 100 ns to assess their effectiveness and binding stability showed that compound_106-375 had the most stable binding with the protein. Key residues, including Asn146, Ala138, and Tyr155, form critical interactions with the ligand, contributing to its stability. The investigation was enhanced by employing principal component analysis (PCA), free energy landscape (FEL), and binding free energy calculations. The total binding free energy (GTOTAL) of - 169.62 kcal/mol suggests that the contact between compound_106-375 and the mutant N1 (H1N1) protein is thermodynamically favourable. This approach allowed for a thorough comprehension of the binding interactions and possible effectiveness of the discovered inhibitors. Overall, these findings demonstrate that compound_106-375 exhibits favourable binding characteristics and stability. Further experimental validation is required to confirm its efficacy against the H275Y mutant neuraminidase protein and its potential to overcome influenza drug resistance. However, compound_106-375 is suggested as a promising candidate for further development as a therapeutic agent against the mutant N1 (H1N1) protein. This finding will assist in drug development and to overcome the challenges associated with drug resistance in influenza strains.

Exploiting the Achilles' heel of cancer through a structure-based drug-repurposing approach and experimental validation of top drugs using the TRAP assay

Telomerase, a reverse transcriptase implicated in replicative immortality of cancers, remains a challenging target for therapeutic intervention due to its structural complexity and the absence of clinically approved small-molecule inhibitors. In this study, we explored drug repurposing as a pragmatic approach to address this gap, leveraging FDA-approved drugs to accelerate the identification of potential telomerase inhibitors. Using a structure-based drug discovery framework, we screened the DrugBank database through a previously validated pharmacophore model for the FVYL pocket in the hTERT thumb domain, the established binding site of BIBR1532. This was followed by molecular docking, pharmacokinetic filtering, and molecular dynamics (MD) simulations to evaluate the stability of protein-ligand complexes. Binding free energy calculations (MM-PBSA and MM-GBSA) were employed for cross-validation, identifying five promising candidates. Experimental validation using the Telomerase Repeat Amplification Protocol (TRAP) assay confirmed the inhibitory potential of Raltitrexed, showing significant inhibition with IC50 8.899 µM in comparison to control. Decomposition analysis and Structure-Activity Relationship (SAR) studies further offered insights into the binding mechanism, reinforcing the utility of the FVYL pocket as a druggable site. Raltitrexed's dual mechanism of action, targeting both telomerase and thymidylate synthase, underscores its potential as a versatile anticancer agent, suitable for combination therapies or standalone treatment. As the top lead, Raltitrexed demonstrates the potential of repurposed drugs in telomerase-targeted therapies, offering a time and cost-effective strategy for advancing its clinical development. The study also provides a robust framework for future drug development, addressing challenges in targeting telomerase for anticancer therapy.

Structure-guided design of partial agonists at an opioid receptor

Chronic pain and opioid overdose deaths highlight the need for non-addictive analgesics with novel mechanisms. The δ opioid receptor (δOR) is a promising target, as it lacks the respiratory depression associated with µ opioid receptor (µOR) agonists. However, early δOR full agonists caused seizures, limiting their clinical use. Partial δOR agonists may offer more controlled receptor activation than full agonists, but their development has been hindered by uncertainty regarding the molecular mechanism of partial agonism. Here we show that C6-Quino, a bitopic ligand developed through structure-based design, acts as a selective δOR partial agonist. Functional studies reveal that C6-Quino shows differential activity at G-protein and arrestin pathways and interacts with the sodium binding pocket, confirmed through cryo-EM analysis. C6-Quino demonstrates oral activity, analgesic activity in chronic pain models without causing δOR-related seizures and µOR-related adverse effects which have limited opioid usage in recent times. This discovery outlines a new strategy for developing δOR-targeted analgesics and provides a framework for optimizing signaling profiles of other Class A GPCRs.

Phospholipid Bilayer Properties in pH-Responsive Hemoglobin-Based Oxygen Carriers

Hemoglobin (Hb)-based oxygen carriers (HBOCs) are a potential solution to the growing shortage in the worldwide blood supply. Recent developments in HBOC design have shown that Polyethylene glycol surface-conjugated liposome-encapsulated Hb (PEG-LEH) has shown promising results in mimicking the oxygen uptake and release of human red blood cells. This study aims to use atomistic simulations to investigate the mechanical properties, gas-exchange properties, and pH responsiveness of a novel HBOC which introduces a pH-sensitive molecule (KC1003) to the phospholipid membrane to regulate the uptake and release of oxygen based on pH. Mechanical properties of KC1003 in a phospholipid membrane show that it is a stable phospholipid membrane, with slight structural differences from increasing the concentration of KC1003, where an increased concentration slightly increases lipid disorder. Gas diffusion through the membrane was not limited by the addition of KC1003, and the gas diffusion values were similar to those of red blood cells. Furthermore, the membrane proved to be pH responsive, allowing for the binding and release of 2,3-DPG (2,3-Diphosphoglyceric Acid) at high and low pHs, respectively. These results collectively show that the membrane is mechanically stable at physiological conditions at a molecular scale, allows for proper gas diffusion through the phospholipid membrane, and can act as a pH-sensitive lipid membrane that the concentration of KC1003 can modify. Collectively, these results can be used for tuning of the membrane of an HBOC to mimic the physiological oxygen intake and release of a red blood cell.

Opportunities and challenges in modelling ligand adsorption on semiconductor nanocrystals

Semiconductor nanocrystals, including their superstructures and hybridized systems, have opened up a new realm to design next-generation functional materials creatively. Their great success and unlimited potential should be largely attributed to surface-adsorbed ligands. However, due to a lack of means to probe and understand their roles in experiments, only a handful of effective ligands have been identified through trial-and-error processes. Alternatively, computational and theoretical methods are ideal for providing physical insights and further guidance. Still, their applications in ligand-coated semiconductor nanocrystals are relatively scarce compared to those of other systems, such as biological chemistry. In this perspective, we first highlight the success of ab initio methods in modeling ligand adsorption. Then, we discuss the opportunities of molecular dynamics and theory in accommodating complex colloidal nature, where we unfold the challenges therein. Finally, we emphasize the need for high-quality force fields to resolve these challenges and look forward to simulation-guided inverse design.

Development and Evaluation of Indole-Based Phospholipase D Inhibitors for Lung Cancer Immunotherapy

This study explored novel immunomodulatory approaches for cancer treatment, with a specific focus on lung cancer, the leading cause of cancer-related deaths worldwide. We synthesized indole-based phospholipase D (PLD) inhibitors with various substituents to improve anticancer efficacy. Through structure-activity relationship studies, the key compound was identified that significantly inhibiting PLD, suppressing cell growth, viability, and migration in vitro, while inducing apoptosis of lung cancer cells. In silico docking studies confirmed its binding to the PLD1 active site, highlighting the role of specific residues in inhibiting PLD1 activity. The inhibitor modulated oncogenic pathways and immune evasion in lung cancer cells, showing potential for immunotherapy. In vivo experiments in a mouse model showed tumor reduction and immune response alteration. Combining these inhibitors with gemcitabine, an anticancer drug, synergistically enhanced inhibition of lung cancer cell apoptosis and proliferation. This research offers new insights into PLD inhibitor as potential cancer therapeutics.

Functional Design of Peptide Materials Based on Supramolecular Cohesion

Peptide materials offer a broad platform to design biomimetic soft matter, and filamentous networks that emulate those in extracellular matrices and the cytoskeleton are among the important targets. Given the vast sequence space, a combination of computational approaches and readily accessible experimental techniques is required to design peptide materials efficiently. We report here on a strategy that utilizes this combination to predict supramolecular cohesion within filaments of peptide amphiphiles, a property recently linked to supramolecular dynamics and consequently bioactivity. Using established coarse-grained simulations on 10,000 randomly generated peptide sequences, we identified 3500 likely to self-assemble in water into nanoscale filaments. Atomistic simulations of small clusters were used to further analyze this subset of sequences and identify mathematical descriptors that are predictive of intermolecular cohesion, which was the main purpose of this work. We arbitrarily selected a small cohort of these sequences for chemical synthesis and verified their fiber morphology. With further characterization, we were able to link the latent heat associated with fiber to micelle transitions, an indicator of cohesion and potential supramolecular dynamicity within the filaments, to calculated hydrogen bond densities in the simulation clusters. Based on validation from in situ synchrotron X-ray scattering and differential scanning calorimetry, we conclude that the phase transitions can be easily observed by very simple polarized light microscopy experiments. We are encouraged by the methodology explored here as a relatively low-cost and fast way to design potential functions of peptide materials.

TinkerModeller: An Efficient Tool for Building Biological Systems in Tinker Simulations

Polarizable force fields advance our understanding of electrostatic interactions in molecular systems; however, their widespread application is limited by the complexity of required molecular modeling. We here present TinkerModeller (TKM), a versatile software package designed to streamline the construction of biological systems in the Tinker molecular simulation software. The core functionality of TKM lies in its capacity to generate input files for complex molecular systems and facilitate the conversion from classical to polarizable force fields. With a user-friendly, standalone script, TKM provides an intuitive interface that supports users from molecular modeling through to postanalysis, creating a comprehensive platform for molecular dynamics simulations within Tinker. Furthermore, TKM includes an electric field (EF) postanalysis module, introducing a novel approach that employs charge methods and point charge approximations for efficient internal EF estimation. This module offers a computationally low-demand solution for high-throughput EF estimation. Our work paves the way for broader, more accessible use of polarizable force fields within Tinker and introduces a new method for EF estimation, advancing our capacity to explore electrostatic effects in biological and materials science applications.

Computational Tool for Determining Local Dielectric Constants in Heterogeneous Nanoscale Systems from Molecular Dynamics Trajectories

In this work, we describe a computational tool designed to determine the local dielectric constants (ε) of charge-neutral heterogeneous systems by analyzing dipole moment fluctuations from molecular dynamics (MD) trajectories. Unlike conventional methods, our tool can calculate dielectric constants for dynamically evolving selections of molecules within a defined region of space, rather than for fixed sets of molecules. We validated our approach by computing the dielectric constants of TIP3P water nanospheres, achieving results consistent with literature values for bulk water. We then applied our tool to more complex systems, the water slabs around solvated phospholipid bilayers, where we observed a lower dielectric constant of water near the bilayer headgroups (ε = 20-50) compared to nanospheres of bulk water (ε = 58-62) with the same number of molecules. Our tool also enabled us to compute the dielectric constants of water in more heterogeneous systems, where water surrounding asymmetrically distributed phospholipids on single-walled carbon nanotubes also exhibited lower dielectric constants than in bulk water nanospheres. Addition of positively charged peptides that bind to phospholipid-nanotube conjugates further lowered the dielectric constants of water in the immediate vicinity of these conjugates. Moreover, we estimated dielectric constants for lipids in symmetric bilayers, where values are well-documented, and for asymmetric phospholipid-wrapped nanotube systems, which were previously unexplored, and found that dielectric constants of phospholipids depend on their arrangement in the assembled aggregate. The results align with the literature for bilayers and provide new insights for phospholipid-nanotube systems. The ability of our tool to provide local dielectric constants for both well-studied and novel systems advances our understanding of molecular environments and interactions.

Exploring the Interaction of Human α-Synuclein with Polyethylene Nanoplastics: Insights from Computational Modeling and Experimental Corroboration

Plastics, particularly microplastics (MPs) and nanoplastics (NP), have become major environmental and health concerns due to their high chemical stability. The highly hydrophobic plastics enter living organisms through reversible interactions with biomolecules, forming biocoronas. Following recent reports on plastics breaching the blood-brain barrier, the binding behavior of human α-synuclein (hαSn) with polyethylene-based (PE) plastics was evaluated by using molecular dynamics simulations and experimental methods. The results provided three important findings: (i) hαSn transitions from an open helical to a compact conformation, enhancing intramolecular interactions, (ii) nonoxidized PE NPs (NPnonox) rapidly adsorb hαSn, as supported by experimental data from dynamic light scattering and adsorption isotherms, altering its structure, and (iii) the oxidized NP (NPox) failed to capture hαSn. These interactions were dominated by the N-terminal domain of hαSn, with major contributions from hydrophobic amino acids. These findings raise concerns about the potential pharmacological effects of NP-protein interactions on human health.

In Silico Investigation of Mineralocorticoid Receptor Antagonists: Insights into Binding Mechanisms and Structural Dynamics

The mineralocorticoid receptor (MR) is a steroid hormone receptor that plays a key role in regulating sodium and water homeostasis and blood pressure. MR antagonists are a guideline recommended for therapy for the treatment of hypertension and cardiovascular disease but can cause hyperkalaemia. Modelling was performed for binding of the endogenous ligands aldosterone and cortisol and MR antagonist spironolactone to the ligand binding domain (LBD) of the MR. A molecular docking screen of compounds that were structurally similar to known antagonists was performed, leading to the identification of two novel compounds, C79 and E67. Molecular dynamics (MD) assessed the dynamic interactions with C79, E76, endogenous ligands, and spironolactone with the MR ligand binding domain (LBD). Analysis of the protein backbone showed modest changes in the overall structure of the MR LBD in response to binding of antagonists, with movement in helix 12 consistent with previous observations. All ligands tested maintained stable binding within the MR LBD throughout the simulations. Hydrogen bond formation played a more prominent role in the binding of endogenous ligands compared to antagonists. MM-PBSA binding free energy calculations showed that all ligands had similar binding affinities, with binding facilitated by key residues within the binding site. The novel antagonists demonstrated similar binding properties to spironolactone, warranting further evaluation. This study provides insights into the molecular mechanisms of MR activation and inhibition, which can aid in the development of novel therapeutic strategies for cardiovascular diseases.

Alkaloids from Caliphruria subedentata (Amaryllidaceae) as Regulators of AChE, BuChE, NMDA and GSK3 Activity: An In Vitro and In Silico Approach for Mimicking Alzheimer´s Disease

Patients with Alzheimer's disease (AD) have two types of abnormal protein buildups: amyloid plaques and neurofibrillary tangles, in addition to the early synaptic dysfunction associated with the enzymes acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). Impairment of the glutamatergic system is also crucial for neuronal survival, as it can cause synaptic dysfunction that overstimulates glutamate receptors, especially N-methyl-d-aspartate receptors (NMDARs). Another protein affecting neuronal health is glycogen synthase kinase-3 (GSK3), a widely preserved serine/threonine protein kinase linked to neuronal disorders, including AD. In recent years, alkaloids from the Amaryllidaceae have received great attention for their known anticholinergic activity, as well as their antioxidant, antigenotoxic, and neuroprotective properties. In this context, the identification of compounds capable of interacting with different targets involved in AD provides a possible new therapeutic strategy. In this study, we conducted a combination of in vitro and in silico approaches to identify the potential of C. subedentata in regulating key proteins involved in AD. Viability and neuroprotection assays were performed to evaluate the neuroprotection exerted by C. subedentata extract against neurotoxicity induced by Aβ (1-42) peptide and Okadaic acid in SH-SY5Y cells. Computational methods such as docking and molecular dynamic and viability therapeutic analysis were conducted to explore the interaction of alkaloids from C. subedentata with target proteins (AChE, BuChE, NMDA, and GSK-3) involved in AD. Our findings show that C. subedentata extract exerts neuroprotective effects against neurotoxic stimuli induced by Aβ (1-42) peptide and Okadaic acid. In addition, in silico approaches provide insight into how C. subedentata extract alkaloids interact with key proteins involved in AD. These findings provide insights into the potential therapeutic effects and action mechanisms of these alkaloids. We hope these rapid findings can contribute as a bridge to the identification of new molecules with the potential to counteract the effects of AD.

Histidine 73 methylation coordinates β-actin plasticity in response to key environmental factors

The functional importance of the methylation of histidine 73 (H73) in actin remains unclear. Focusing on cytoplasmic β-actin, present in all mammalian cells, we use molecular dynamics simulations with a polarizable force field and adaptive sampling to examine the effects of H73 methylation. Our results show that methylation enhances nucleotide binding cleft opening, alters allosteric pathways connecting subdomains 2 and 4 (SD2 and SD4) in G-actin, and affects backdoor openings and inorganic phosphate release in F-actin, as validated by biochemical assays. These effects depend on the nucleotide and ions interacting with the actin. Together, our findings reveal how H73 methylation regulates β-actin plasticity and integrates environmental cues.

Slowing Down Peptide Translocation through MoSi2N4 Nanopores for Protein Sequencing

Precise identification and quantification of amino acids are crucial for numerous biological applications. A significant challenge in the development of high-throughput, cost-effective nanopore protein sequencing technology is the rapid translocation of protein through the nanopore, which hinders accurate sequencing. In this study, we explore the potential of nanopore constructed from a novel two-dimensional (2D) material MoSi2N4 in decelerating the velocity of protein translocation using molecular dynamics simulations. The translocation velocity of the peptide through the MoSi2N4 nanopore can be reduced by nearly an order of magnitude compared to the MoS2 nanopore. Systematic analysis reveals that this reduction is due to stronger interaction between the peptide and MoSi2N4 membrane surface, particularly for aromatic residues, as they contain aromatic rings composed of relatively nonpolar C-C and C-H bonds. By adjusting the proportion of aromatic residues in peptides, further control over peptide translocation velocity can be achieved. Additionally, the system validates the feasibility of using an appropriate nanopore diameter for protein sequencing. The theoretical investigations presented herein suggest a potential method for manipulating protein translocation kinetics, promising more effective and economical advancements in nanopore protein sequencing technology.

In silico screening and molecular dynamics analysis of natural DHPS enzyme inhibitors targeting Acinetobacter baumannii

Over time, antimicrobial agents are losing their credibility in curbing infections due to the development of resistant pathogen strains. The resistant strains have proven to invade living beings and cause various diseases, leading to deaths at an alarming rate. Acinetobacter baumannii is one such pathogen, and to target it through enzyme inhibition, Dihydropteroate synthase enzyme's active site is virtually screened for antimicrobial agents against in-house libraries of natural molecules from medicinally important plants and Agaricus spp. fungus. Two ligands (MSID_000725 and CID_291096) are found to be suitable candidate inhibitors after various screening through Lipinski's based drug-like parameters, pharmacokinetic parameters, toxicity parameters and structural parameters which comprised of estimated free energy of binding, ligand efficiency and interaction analysis. DHPS enzyme catalyses the condensation reaction of hydroxymethyl-7, 8-dihydropterin pyrophosphate and para-aminobenzoic acid in the folic acid synthesis pathway in bacterial cells. The Complexes of the DHPS enzyme and ligands are validated through in silico studies, including MD simulations and MM/PBSA based binding free energy studies. The Complex DHPS-MSID_000725 and DHPS-CID_291096 were analysed for global dynamics attributes such as RMSD, RMSF, Rg, SASA and essential dynamics through PCA. The complexes were subjected to MM/PBSA based binding free energy analysis and were found to have binding free energy of -25.18 kcal/mol (DHPS-MSID_000725) and - 4.90 kcal/mol (DHPS-CID_291096).

Molecular Determinants of Optical Modulation in ssDNA-Carbon Nanotube Biosensors

Most traditional optical biosensors operate through molecular recognition, where ligand binding causes conformational changes that lead to optical perturbations in the emitting motif. Optical sensors developed from single-stranded DNA-functionalized single-walled carbon nanotubes (ssDNA-SWCNTs) have started to make useful contributions to biological research. However, the mechanisms underlying their function have remained poorly understood. In this study, we combine experimental and computational approaches to show that ligand binding alone is not sufficient for optical modulation in this class of synthetic biosensors. Instead, the optical response that occurs after ligand binding is highly dependent on the chemical properties of the ligands, resembling mechanisms seen in activity-based biosensors. Specifically, we show that in ssDNA-SWCNT catecholamine sensors, the optical response correlates positively with the electron density on the aryl motif, even among ligands with similar ligand binding affinities. Importantly, despite the strong correlations with electrochemical properties, we find that catechol oxidation itself is not necessary to drive the sensor optical response. We discuss how these findings could serve as a framework for tuning the performance of existing sensors and guiding the development of new biosensors of this class.

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Eco-friendly Synthesis and Molecular Modelling of 2-Phenylimidazo[1,2-b]pyridazine Derivatives: In Vitro and In Vivo Studies for Lead Optimization

7-methyl-2-phenylimidazo[1,2-b]pyridazin-3-carboxylic acid (DM1) and 6-methoxy-2-phenylimidazo[1,2-b]pyridazin-3-carboxylic acid (DM2) have been shown to act as human (h) Cav3.1 voltage-gated calcium channel blockers with promising in vivo anti-absence activity, positioning them as potential antiepileptic drugs. The primary aim of this work was to develop cost-effective and environmentally friendly synthetic procedures for preparing 2-phenylimidazo[1,2-b]pyridazine derivatives. After optimizing the synthesis of this compound class using efficient and green techniques such as microwaves and ultrasound irradiation, we further evaluated the antiepileptic effects of DM1 and DM2 in two animal models: CD-1 ICR mice after pentylenetetrazol administration and DBA/2 mice with seizures induced by audiogenic stimuli. Their neuroprotective effect against oxidative stress were assessed using C6 rat brain glioma cells. DM1 and DM2 exhibited potent anti-seizure effects in both animal models and demonstrated significant in vitro neuroprotective activity by reducing reactive oxygen species release. To lay the groundwork for the future rational optimization of this promising class of compounds, the molecular bases of DM1 and DM2 activity were investigated by modelling their interaction with hCav3.1 channels. The calculated binding modes of DM1 and DM2 to hCav3.1 channels partially mirrored that of the selective Cav3.1 blocker Z944, paving the way for future lead optimization.

Encapsulation of menthol by cyclodextrins-comparison between experiments and molecular simulations

Cyclodextrins (CDs) are a traditional wall material for encapsulating flavor ingredients. Given the substantial structural differences among flavor ingredients, experimentally deriving the selection criteria for CDs suitable for these flavor substances is a time-consuming process. However, most existing literature relies on the embedding efficiency from practical experiments to evaluate the binding effect between flavor ingredients and CDs. This article used menthol as the research subject and investigated the binding effects of three different CDs through experiments and computer simulations. The experimental results revealed that β-CD exhibited the optimal encapsulation efficiency (EE, 36.54%) on menthol, subsequently, γ-CD showed a 33.35% EE value, whereas α-CD was unable to form an inclusion complex (IC) with menthol. Conformation changes, root mean square deviation (RMSD), radius of gyration (Rg), Radial distribution function (RDF), solvent accessible surface area (SASA), and hydrogen bonds, and binding free energy were analyzed through molecular dynamics simulation and compared with the experimental results. The results indicated that the IC formed between β-CD and menthol (menthol/β-CD-IC) is the most stable, with a binding free energy (ΔGbind) of -7.27 kcal/mol. The IC formed between α-CD and menthol (menthol/α-CD-IC) is the least stable one (ΔGbind = 2.59 kcal/mol). The results revealed a high degree of consistency between the experimental outcomes and those of molecular simulation, so molecular simulation can serve as a more efficient screening method, an alternative to practical experiments, to obtain the combination ability between host-guest molecules.

Cryogenic Electron Microscopy Informed Molecular Dynamics Simulations to Investigate the Disulfide Hydrogel Self-Assembly

Disulfide hydrogels, derived from cysteine-based redox systems, exhibit active self-assembly properties driven by reversible disulfide bond formation, making them a versatile platform for dynamic material design. Detailed cryogenic electron microscopy (cryo-EM) analysis reveals a consistent fiber diameter of 5.4 nm for individual fibers. Using cryo-EM-informed radial positional restraints, all-atom molecular dynamics (MD) simulations are employed to reproduce fibers with dimensions closely matching experimental observations, validated further through simulated cryo-EM images. The MD simulations reveal that the disulfide gelator (CSSC) predominantly adopts an open conformation, with hydrogen bonds emerging as the key intermolecular force stabilizing the fibers. Notably, intermolecular interactions are found to be higher at 70% conversion to the disulfide gelator compared with 100%, comparable with past unrestrained simulations. Water molecules and solute-water hydrogen bonds are present throughout the fiber, indicating that the fiber remains hydrated. These findings underscore the potential role of the thiol precursor CSH in stabilizing the transient phase and highlight the importance of CSH-CSSC interplay. Herein, it provides novel insights into molecular mechanisms governing active self-assembly and offers strategies for designing tunable materials through controlled assembly conditions.

2Danalysis: A toolbox for analysis of lipid membranes and biopolymers in two-dimensional space

Molecular simulations expand our ability to learn about the interplay of biomolecules. Biological membranes, composed of diverse lipids with varying physicochemical properties, are highly dynamic environments involved in cellular functions. Proteins, nucleic acids, glycans and bio-compatible polymers are the machinery of cellular processes both in the cytosol and at the lipid membrane interface. Lipid species directly modulate membrane properties, and affect the interaction and function of other biomolecules. Natural molecular diffusion results in changes of local lipid distribution, affecting the membrane properties. Projecting biophysical and structural membrane and biopolymer properties to a two-dimensional plane can be beneficial to quantify molecular signatures in a reduced dimensional space to identify relevant interactions at the interface of interest, i.e. the membrane surface or biopolymer-surface interface. Here, we present a toolbox designed to project membrane and biopolymer properties to a two-dimensional plane to characterize patterns of interaction and spatial correlations between lipid-lipid and lipid-biopolymer interfaces. The toolbox contains two hubs implemented using MDAKits architecture, one for membranes and one for biopolymers, that can be used independently or together. Three case studies demonstrate the versatility of the toolbox with detailed tutorials in GitHub. The toolbox and tutorials will be periodically updated with other functionalities and resolutions to expand our understanding of the structure-function relationship of biomolecules in two-dimensions.

Raloxifene Prevents Chemically-Induced Ferroptotic Neuronal Death In Vitro and In Vivo

Ferroptosis, a regulated form of cell death characterized by excessive iron-dependent lipid peroxidation, can be readily induced in cultured cells by chemicals such as erastin and RSL3. Protein disulfide isomerase (PDI) has been identified as an upstream mediator of chemically induced ferroptosis and also a target for ferroptosis protection. In this study, we discovered that raloxifene (RAL), a selective estrogen receptor modulator known for its neuroprotective actions in humans, can effectively inhibit PDI function and provide robust protection against chemically induced ferroptosis in cultured HT22 neuronal cells. Specifically, RAL can bind directly to PDI both in vitro and in intact neuronal cells and inhibit its catalytic activity. Computational modeling analysis reveals that RAL can tightly bind to PDI through forming a hydrogen bond with its His256 residue, and biochemical analysis further shows that when PDI's His256 is mutated to Ala256, RAL loses its inhibition of PDI's catalytic activity. This inhibition of PDI by RAL significantly reduces the dimerization of both the inducible and neuronal nitric oxide synthases and the accumulation of nitric oxide, both of which have recently been shown to play a crucial role in mediating chemically induced ferroptosis through subsequent induction of ROS and lipid-ROS accumulation. In vivo behavioral analysis shows that mice treated with RAL are strongly protected against kainic acid-induced memory deficits and hippocampal neuronal damage. In conclusion, this study demonstrates that RAL is a potent inhibitor of PDI and can effectively prevent chemically induced ferroptosis in hippocampal neurons both in vitro and in vivo. These findings offer a novel estrogen receptor-independent mechanism for RAL's neuroprotective actions in animal models and humans.

The structure and function of the ghrelin receptor coding for drug actions

Drugs targeting the ghrelin receptor hold therapeutic potential in anorexia, obesity and diabetes. However, developing effective drugs is challenging. To tackle this common issue across a broad drug target, this study aims to understand how anamorelin, the only approved drug targeting the ghrelin receptor, operates compared to other synthetic drugs. Our research elucidated the receptor's structure with anamorelin and miniGq, unveiling anamorelin's superagonistic activity. We demonstrated that ligands with distinct chemical structures uniquely bind to the receptor, resulting in diverse conformations and biasing signal transduction. Moreover, our study showcased the utility of structural information in effectively identifying natural genetic variations altering drug action and causing severe functional deficiencies, offering a basis for selecting the right medication on the basis of the individual's genomic sequence. Thus, by building on structural analysis, this study enhances the foundational framework for selecting therapeutic agents targeting the ghrelin receptor, by effectively leveraging signaling bias and genetic variations.

Self-association of cyclodextrin inclusion complexes in a deep eutectic solvent enhances guest solubility

Cyclodextrins are widely used pharmaceutical excipients known to increase the solubility of drug compounds through formation of inclusion complexes. A prominent limitation of common cyclodextrins is their own scarce solubility in water, which renders them unsuitable for many drug formulations. Cyclodextrin solubility can be enhanced in appropriate media such as Deep Eutectic Solvents (DESs). However, DESs can also reduce the equilibrium constant for host-guest complexation, making it challenging to optimize drug solubility using cyclodextrin. To determine the impact and mechanism of cyclodextrin complexation in DES, we tracked changes in the solubility of methyl orange (MO), serving as a hardly soluble model compound, in the presence of β-cyclodextrin (CD) in hydrated urea-choline chloride DES. The highest achievable MO solubility is obtained in concentrated CD-in-DES mixtures at low hydration, resulting from the higher solubility of CD⊃MO complexes in DES compared to water as a solvent. Combining our results with molecular dynamics simulations, we provide evidence that CD⊃MO complexes self-associate into dimers and larger oligomers. This self-association of complexes greatly enhances MO solubilization by CD beyond that expected from the canonical 1:1 binding stoichiometry. This newly unraveled solubilization mechanism via cyclodextrins and its facilitation by DES should aid the design of future drug formulations.

6,7-Dichloro-1H-indole-2,3-dione-3-oxime functions as a superagonist for the intermediate-conductance Ca2+-activated K+ channel KCa3.1

NS309 (6,7-dichloro-1H-indole-2,3-dione-3-oxime) is widely used as a pharmacological tool to increase the activity of small- and intermediate-conductance calcium-activated potassium channels. NS309 is assumed to function as a positive allosteric gating modulator. However, its binding site and the molecular details of its action remain unknown. Here, we show that NS309 has a profound effect on the calcium-dependent gating of the intermediate-conductance Ca2+-activated K+ channel KCa3.1. In inside-out experiments, 10 μM NS309 shifted the calcium EC50 from 430 to 31 nM. In whole-cell experiments, changing free intracellular calcium from 250 nM to 3 μM decreased the EC50 of NS309 from 74 to 8.6 nM. We further observed that NS309 could elicit greater responses than saturating calcium, making it a "superagonist." Molecular modeling suggested 2 possible binding sites for NS309 in KCa3.1, which we probed by mutagenesis and determined that NS309 is binding in the interface between the S45A segment of the intracellular S4-S5 linker and the N-lobe of the channel-associated calmodulin. Molecular dynamic simulations revealed that NS309 pushes several water molecules out of the interface pocket, establishes stable contacts with S181 and L185 in the S45A segment of KCa3.1 and E54 in calmodulin, and promotes longer sustained widening of the inner gate of KCa3.1 at V282 in the S6 segment. Polar substitutions of the hydrophobic-gating residues V282 and A279 resulted in constitutively open channels that could not be further potentiated by NS309, suggesting that NS309 produces its agonistic effects by increasing the open probability of the inner gate of KCa3.1. SIGNIFICANCE STATEMENT: The publication of the full-length cryo-electron microscopy structure of the intermediate-conductance Ca2+-activated K+ channel KCa3.1 suggested that the previously reported binding site of NS309 (6,7-dichloro-1H-indole-2,3-dione-3-oxime) was a crystallization artifact because this structure only included the C-terminus and the channel-associated calmodulin. This study demonstrates that the true binding site of NS309 is located between the S4 and S5 linker of KCa3.1 and the N-lobe of calmodulin. NS309 acts as a stabilizing force within the gating interface and increases the open probability of the inner hydrophobic gate.

Structure and mechanism of vitamin-K-dependent γ-glutamyl carboxylase

γ-Glutamyl carboxylase (GGCX) is the sole identified enzyme that uses vitamin K (VK) as a cofactor in humans. This protein catalyses the oxidation of VK hydroquinone to convert specific glutamate residues to γ-carboxyglutamate residues in VK-dependent proteins (VDPs), which are involved in various essential biological processes and diseases1-3. However, the working mechanism of GGCX remains unclear. Here we report three cryogenic electron microscopy structures of human GGCX: in the apo state, bound to osteocalcin (a VDP) and bound to VK. The propeptide of the VDP binds to the lumenal domain of GGCX, which stabilizes transmembrane helices 6 and 7 of GGCX to create the VK-binding pocket. After binding of VK, residue Lys218 in GGCX mediates the oxidation of VK hydroxyquinone, which leads to the deprotonation of glutamate residues and the construction of γ-carboxyglutamate residues. Our structural observations and results from binding and cell biological assays and molecular dynamics simulations show that a cholesterol molecule interacts with the transmembrane helices of GGCX to regulate its protein levels in cells. Together, these results establish a link between cholesterol metabolism and VK-dependent pathways.

Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions

Machine learning (ML) techniques have been making major impacts on all areas of science and engineering, including biophysics. In this review, we discuss several applications of ML to biophysical problems based on our recent research. The topics include the use of ML techniques to identify hotspot residues in allosteric proteins using deep mutational scanning data and to analyze how mutations of these hotspots perturb co-operativity in the framework of a statistical thermodynamic model, to improve the accuracy of free energy simulations by integrating data from different levels of potential energy functions, and to determine the phase transition temperature of lipid membranes. Through these examples, we illustrate the unique value of ML in extracting patterns or parameters from complex data sets, as well as the remaining limitations. By implementing the ML approaches in the context of physically motivated models or computational frameworks, we are able to gain a deeper mechanistic understanding or better convergence in numerical simulations. We conclude by briefly discussing how the introduced models can be further expanded to tackle more complex problems.

Substrate interactions guide cyclase engineering and lasso peptide diversification

Lasso peptides are a diverse class of naturally occurring, highly stable molecules kinetically trapped in a distinctive [1]rotaxane conformation. How the ATP-dependent lasso cyclase constrains a relatively unstructured substrate peptide into a low entropy product has remained a mystery owing to poor enzyme stability and activity in vitro. In this study, we combined substrate tolerance data with structural predictions, bioinformatic analysis, molecular dynamics simulations and mutational scanning to construct a model for the three-dimensional orientation of the substrate peptide in the lasso cyclase active site. Predicted peptide cyclase molecular contacts were validated by rationally engineering multiple, phylogenetically diverse lasso cyclases to accept substrates rejected by the wild-type enzymes. Finally, we demonstrate the utility of lasso cyclase engineering by robustly producing previously inaccessible variants that tightly bind to integrin αvβ8, which is a primary activator of transforming growth factor β and, thus, an important anti-cancer target.

Molecular dynamics of SARS-CoV-2 omicron variants from Philippine isolates against hesperidin as spike protein inhibitor

SARS-CoV-2 remains a global threat with new sublineages posing challenges, particularly in the Philippines. Hesperidin (HD) is being studied as a potential prophylactic for COVID-19. However, the virus's rapid evolution could alter how HD binds to it, affecting its effectiveness. Here, we study the mutation-induced variabilities of HD dynamics and their effects on molecular energetics in SARS-CoV-2 spike receptor complex systems. We considered eight different point mutations present in the Omicron variant. Root-mean-square deviation and binding energy analysis showed that S477N and Omicron did not eject HD throughout the simulation. Hydrogen bond distribution analysis highlighted the involvement of hydrogen bonding in mutant-HD stabilization, especially for S477N and Omicron. Root-mean-square fluctuation analysis revealed evidence of Y505H destabilization on complex systems, while distal-end loop mutations increased loop flexibility for all models bearing the three mutations. Per-residue energy decomposition demonstrated that Q493R substitution increased HD interaction. Free energy landscape and essential dynamics through principal component analysis provided insights into the conformational subspace distribution of mutant model molecular dynamics trajectories. In conclusion, significant mutations contributed to the HD interaction in different ways. S477N has shown significant binding contributions through favorable ligand interaction, while other mutations contribute via conformational modifications, increased affinity due to sidechain mutations, and increased loop flexibility.

Improving the antimicrobial potential of the peptide CIDEM-501 through acylation: A computational approach

Acylation is a common method used to modify antimicrobial peptides to enhance their effectiveness. It increases the interactions between the peptide and the bacterial cell membranes. However, acylation can also reduce the selectivity of the peptides by making them more active on eukaryotic membranes, which can lead to unintended toxicity. This study examines the potential of using in silico tools to evaluate the interaction and selectivity of the antimicrobial peptide CIDEM-501 when acylated with decanoic acid at the N-terminus, compared to the non-acylated counterpart. Circular dichroism, microdilution, and hemolysis assays were used to determine the peptide's secondary structure, antimicrobial activity, and selectivity to validate the theoretical predictions. The acylated peptide showed a more stable interaction with the bacterial membrane by inserting the acyl chain into the membrane's hydrophobic core, which led to tighter adsorption and a greater buried surface area. Additionally, it significantly altered membrane order more than the non-acylated counterpart, suggesting superior antimicrobial potential. Finally, in vitro activity assays confirmed theoretical predictions, showing that the acylated peptide had lower Minimum Inhibitory Concentration (MIC) values than the non-acylated peptide. Neither peptide showed significant hemolytic activity at their MIC. The computational techniques used in this study displayed strong predictive capability and helped to elucidate the interaction between the peptide and the membranes.

Stabilization of a protein by a single halogen-based aromatic amplifier

The utility of halogenation in protein design is investigated by a combination of quantitative atomistic simulations and experiment. Application to insulin is of complementary basic and translational interest. In a singly halogenated aromatic ring, regiospecific inductive effects were predicted to modulate multiple surrounding electrostatic (weakly polar) interactions, thereby amplifying changes in thermodynamic stability. In accordance with the simulations, we demonstrated stabilization of insulin by single halogen atoms at the ortho position of an invariant phenylalanine (2-F-PheB24, 2-Cl-PheB24, and 2-Br-PheB24; ΔΔGu = -0.5 to -1.0 kcal/mol) located at the edge of a protein crevice; corresponding meta and para substitutions had negligible effects. Although receptor-binding affinities were generally decreased (in accordance with packing of the native Phe at the hormone-receptor interface), the ortho-analogs retained biological activity in mammalian cells and in a rat model of diabetes mellitus. Further, the ortho-modified analogs exhibited enhanced resistance to fibrillation above room temperature in two distinct assays of physical stability. Regiospecific halo-aromatic stabilization may thus augment the shelf life of pharmaceutical insulin formulations under real-world conditions. This approach, extending principles of medicinal chemistry, promises to apply to a broad range of therapeutic proteins and vaccines whose biophysical stabilization would enhance accessibility in the developing world.

Investigation of serotonin-receptor interactions, stability and signal transduction pathways via molecular dynamics simulations

Serotonin-receptor binding plays a key role in several neurological and biological processes, including mood, sleep, hunger, cognition, learning, and memory. In this article, we performed molecular dynamics simulation to examine the key residues that play an essential role in the binding of serotonin to the G-protein-coupled 5-HT1B receptor (5HT1BR) via electrostatic interactions. Key residues for electrostatic interactions were identified via bond distance analysis and frustration analysis methods. An end-point free energy calculation method determines the stability of the 5-HT1BR due to serotonin binding. The single-point mutation of the polar/charged amino acid residues (Asp129, Thr134) on the binding sites and the calculation of binding free energy validate the quantitative contribution of these residues to the stability of the serotonin-receptor complex. The principal component analysis reflects that the serotonin-bound 5-HT1BR is more stabilized than the apo-receptor regarding dynamical changes. The difference dynamic cross-correlations map shows the correlation between the transmembranes and mini-Go, which indicates that the signal transduction happens between mini-Go and the receptor. Allosteric pathway analysis reveals the key nodes and key pathways for signal transduction in 5-HT1BR. These results provide useful insights into the study of signal transduction pathways and mutagenesis to regulate the binding and functionality of the complex. The developed protocols can be applied to study local non-covalent interactions and long-range allosteric communications in any protein-ligand system for computer-aided drug design.

Computer-Aided Drug Design in Research on Chinese Materia Medica: Methods, Applications, Advantages, and Challenges

Chinese materia medica (CMM) refers to the medicinal substances used in traditional Chinese medicine. In recent years, CMM has become globally prevalent, and scientific research on CMM has increasingly garnered attention. Computer-aided drug design (CADD) has been employed in Western medicine research for many years, contributing significantly to its progress. However, the role of CADD in CMM research has not been systematically reviewed. This review briefly introduces CADD methods in CMM research from the perspectives of computational chemistry (including quantum chemistry, molecular mechanics, and quantum mechanics/molecular mechanics) and informatics (including cheminformatics, bioinformatics, and data mining). Then, it provides an exhaustive discussion of the applications of these CADD methods in CMM research through rich cases. Finally, the review outlines the advantages and challenges of CADD in CMM research. In conclusion, despite the current challenges, CADD still offers unique advantages over traditional experiments. With the development of the CMM industry and computer science, especially driven by artificial intelligence, CADD is poised to play an increasingly pivotal role in advancing CMM research.

Discovery and mechanistic exploration of promiscuous xylosyltransferase based on protein engineering

Glycosylation is an effective means to alter the structure and properties of plant compounds, influencing the pharmacological activity of natural products (NPs) to obtain highly active NPs. In nature, glucosides are the most widely distributed, while other glycosides such as xylosides are less common and present in lower quantities. This is due to the scarcity of xylosyltransferases with substrate promiscuity in nature, and the modification of their catalytic function is also quite challenging. In this study, we first performed a phylogenetic analysis of reported UDP-glycosyltransferases (UGTs) of plant and microbiological origin and identified a unique motif region from the UGTs of the Bacillus genus, which may be responsible for the broad sugar donor catalytic activity of the UGTs in the Bacillus genus. Then, utilizing protein engineering techniques, we have evolved a xylosyltransferase M3-2, which exhibited high substrate promiscuity, sugar donor promiscuity, and site selectivity, enabling the synthesis of a variety of O-glycosides. In addition, another mutant M3-1 has been engineered to alter the sugar donor specificity of the UGT, enabling the switch from UDP-Glc donor to UDP-Xyl. The improved enzymatic activity is likely attributed to stable hydrophobic interactions and hydrogen bonding interactions between the enzyme and the substrate. In order to synthesize xylosylated products more economically and efficiently, an in vitro synthetic pathway that utilizes NPs and inexpensive glucuronic acid as starting materials was designed. Through this pathway, we successfully synthesized a variety of unnatural xylosylated products belonging to O-glycosides, one of which 10a possesses excellent anti-inflammatory activity. We anticipate that this work will contribute to the future discovery and industrial production of unnatural glycosides.

A novel anion replaced gemini surfactant: Investigation on the primary interaction between gemini surfactant and BSA

Gemini surfactants (GS) could serve as the drug carrier agents for the delivery of macromolecules due to the excellent properties and tuneable structures. Little attention has been paid to the impact of counterion change on GS and the interaction between GS and protein. In this work, ibuprofen (Ibu) replaced quaternary ammonium ion GS (GS-Ibu) with the hydrophobic chain length of 8, 10, 12, 14 and 16 carbon atoms were prepared for the first-time using extraction technology. The prepared GS-Ibu has stronger electrostatic interaction compared to traditional gemini surfactants with bromide anions (GS-Br). GS were further incubated with the model macromolecule, bovine serum albumin (BSA), to form BSA/GS complexes. The colloid stability of BSA could be affected by the concentration of GS, the length of hydrophobic chain and the type of anion. GS-Ibu exhibited better ability to prevent BSA from aggregating based the result of PAGE test. The molecular level change of BSA after the introduction of GS was first reflected by UV-Visible absorption spectrum. CD spectrum results further revealed that the primary interaction leading to the change in the secondary structure of BSA is electrostatic interaction. Molecular docking and molecular dynamic simulations confirmed the presence of hydrophobic and electrostatic interaction between BSA and GS. In conclusion, the anion replaced GS could be a promising strategy to stabilize the proteins.

Decoding the therapeutic landscape of alpha-linolenic acid: a network pharmacology and bioinformatics investigation against cancer-related epigenetic modifiers

Omega-3 (n - 3) and omega-6 (n - 6) polyunsaturated fatty acids (PUFAs) are vital for human health, but an imbalance between these types is associated with chronic diseases, including cancer. Alpha-linolenic acid (ALA), a n - 3 PUFA, shows promise as an anticancer agent in both laboratory and animal studies. However, the precise molecular mechanisms underlying ALA's actions against cancer-related epigenetic modifiers (CaEpM) remain unclear. To understand this, we employed network pharmacology (NP) and molecular docking techniques. Our study identified 51 potential ALA targets and GO and KEGG pathway analysis revealed possible molecular targets and signaling pathways of ALA against CaEpM. From PPI analysis, EZH2, KAT2B, SIRT1, KAT2A, KDM6B, EHMT2, WDR5, SETD7, SIRT2, and HDAC3 emerged as the top 10 potential targets. Additionally, GeneMANIA functional association (GMFA) network analysis of these top 10 targets was performed to enhance NP insights and explore ALA's multi-target approach. After an exhaustive analysis of the core FGN subnetwork, it became evident that 9 out of the 15 targets-namely EZH2, SUZ12, EED, PARP1, HDAC3, DNMT1, NCOR2, KAT2B, and TRRAP-manifested evidently strong and abundant interconnections among each other. Molecular docking of both top 10 targets and core FGN targets confirmed strong binding affinity between ALA and SIRT2, WDR5, KDM6B, EHMT2, HDAC3, EZH2, PARP1, and KAT2B, underscoring their roles in ALA's anti-CaEpM mechanism. Our findings suggest that ALA may target key signaling pathways related to transcriptional regulation, microRNA involvement, stem cell pluripotency and cellular senescence in cancer epigenetics. These findings illuminate ALA's potential as a multi-target agent against CaEpM.Communicated by Ramaswamy H. Sarma.

Molecular insights into the modulation of the 5HT2A receptor by serotonin, psilocin, and the G protein subunit Gqα

5HT2AR is a G-protein-coupled receptor that drives many neuronal functions and is a target for psychedelic drugs. Understanding ligand interactions and conformational transitions is essential for developing effective pharmaceuticals, but mechanistic details of 5HT2AR activation remain poorly understood. We utilized all-atom molecular dynamics simulations and free-energy calculations to investigate 5HT2AR's conformational dynamics upon binding to serotonin and psilocin. We show that the active state of 5HT2AR collapses to a closed state in the absence of Gqα, underscoring the importance of G-protein coupling. We discover an intermediate "partially-open" receptor conformation. Both ligands have higher binding affinities for the orthosteric than the extended binding pocket. These findings enhance our understanding of 5HT2AR's activation and may aid in developing novel therapeutics. Impact statement This study sheds light on 5HT2AR activation, revealing intermediate conformations and ligand dynamics. These insights could enhance drug development for neurological and psychiatric disorders, benefiting researchers and clinicians in pharmacology and neuroscience.

Comparison between molecular dynamics potentials for simulation of graphene-based nanomaterials for biomedical applications

OBJECTIVE

This article provides a substantial review of recent research and comparison on molecular dynamics potentials to determine which are most suitable for simulating the phenomena in graphene-based nanomaterials (GBNs).

SIGNIFICANCE

GBNs gain significant attention due to their remarkable properties and potential applications, notably in nanomedicine. However, the physical and chemical characteristics toward macromolecules that justify their nanomedical applications are not yet fully understood. The molecular interaction through molecular dynamic simulation offers the benefits for simulating inorganic molecules like GBNs, with necessary adjustments to account for physical and chemical interactions, or thermodynamic conditions.

METHOD

In this review, we explore various molecular dynamics potentials (force fields) used to simulate interactions and phenomena in graphene-based nanomaterials. Additionally, we offer a brief overview of the benefits and drawbacks of each force fields that available for analysis to assess which one is suitable to study the molecular interaction of graphene-based nanomaterials.

RESULT

We identify and compare various molecular dynamics potentials that available for analyzing GBNs, providing insights into their suitability for simulating specific phenomena in graphene-based nanomaterials. The specification of each force fields and its purpose can be used for further application of molecular dynamics simulation on GBNs.

CONCLUSION

GBNs hold significant promise for applications like nanomedicine, but their physical and chemical properties must be thoroughly studied for safe clinical use. Molecular dynamics simulations, using either reactive or non-reactive MD potentials depending on the expected chemical changes, are essential for accurately modeling these properties, requiring careful selection based on the specific application.