All-atom empirical potential for molecular modeling and dynamics studies of proteins

A putative binding model of nitazene derivatives at the μ-opioid receptor

Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of μOR-opioid receptor (μOR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the μOR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of μOR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for μOR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln1242.60. Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a π-hole interaction with the conserved Tyr751.39. Our computational analysis provides new insights into the mechanism of μOR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes.

A mechanistic experimental and computational exploration of aldose reductase inhibition by coumarins from Ruta chalepensis

Aldose reductase (AR), a key enzyme in the polyol pathway, contributes to diabetic complications through oxidative stress and cellular damage. We investigated the inhibitory potential of coumarins from Ruta chalepensis as AR inhibitors, combining experimental and computational approaches. Among studied coumarins, aegelinol demonstrated the strongest AR inhibition with a mixed inhibition mode (IC50 = 3.75 ± 0.11 μM, Kᵢ = 3.04 ± 0.82 μM), approaching the potency of the positive control quercetin (IC50 = 2.22 ± 0.27 μM). Marmesinin and gelseminic acid also showed notable inhibition (IC50 = 5.32 ± 0.15 μM, 5.82 ± 0.61 μM, respectively), with noncompetitive binding mechanisms (Kᵢ = 4.86 ± 0.35 μM and 4.46 ± 0.52 μM), suggesting allosteric binding. Docking highlighted key interactions with critical AR residues, while molecular dynamics simulations provided insights into the stability and dynamics of AR-ligand complexes. Various MD parameters confirmed structural integrity, with the aegelinol-AR complex exhibiting the highest stability. MM/PBSA calculations supported these findings, showing the most favorable binding free energy for aegelinol, followed by marmesinin and gelseminic acid. Potential energy landscape analyses showed that aegelinol forms the most stable AR complex with the deepest energy basin. ADMET analysis confirmed aegelinol as a promising lead due to its high gastrointestinal absorption, BBB permeability, and balanced lipophilicity. Thus, Aegelinol, marmesinin, and gelseminic acid show strong potential as AR inhibitors, with aegelinol as the leading candidate for therapeutic development. These findings highlight Ruta chalepensis as a promising source of bioactive coumarins for targeted therapies against diabetic complications.

Molecular dynamics study of monomeric chorismate mutase shows large reduction in conformational diversity of loops upon binding of the transition state analog

In this in silico study, we investigated the structure and dynamics of the molten globule enzyme, monomeric chorismate mutase, which catalyzes the conversion of chorismate to prephenate despite its molten globule state. The primary aim was to understand how the enzyme stabilizes the transition state of the reaction while maintaining its molten globule characteristics. Using the transition state analog (TSA) from the NMR structure (PDB code 2GTV), molecular dynamics simulations revealed multiple hydrogen bonds between three of the enzyme's helices and the TSA. Specific residues that formed stable hydrogen bonds with the TSA were identified as potential mutation targets. Furthermore, the binding of the TSA significantly reduced the entropy of the enzyme and led to the rigidification of the backbone dihedrals across all helices. The flexibility of the loop connecting helices 1 and 2, was also analyzed, showing reduced conformational diversity upon TSA binding. Structural differences between the apo and TSA-bound forms were noted, with helices 3 and 4 exhibiting altered helicity, including a kink in helix 3 and unravelling in helix 4. Despite its molten globule nature, monomeric chorismate mutase can stabilize the TSA through hydrogen bonds involving charged residues, which are essential for maintaining the helix bundle structure. This study highlights the importance of local structural dynamics and entropy changes in enzyme catalysis, offering insights into how molten globule states can support efficient enzymatic activity.

The Role of Asparagine as a Gatekeeper Residue in the Selective Binding of Rare Earth Elements by Lanthanide-Binding Peptides

Lanthanide-binding tag (LBT) peptides selectively complex lanthanide cations (Ln3+) in their binding pockets and are promising for lanthanide separation. However, designing LBTs that selectively target specific Ln3+ cations remains a challenge due to limited molecular-level understanding and control of interactions within the lanthanide-binding pocket. In this study, we reveal that the N5 asparagine residue acts as a gatekeeper in the binding pocket, resulting in a 100-fold selectivity for smaller Lu3+ over larger La3+ cations. Nuclear magnetic resonance spectroscopy and molecular dynamics simulations show that the N5 residue weakly binds to the larger La3+ cation, permitting H2O molecules inside the pocket. For the smaller Lu3+ cations, the N5 residue forms an inter-arm hydrogen bond with the E14 glutamic acid residue, locking the Lu3+ cation in the pocket and preventing H2O infiltration. Mutating the N5 asparagine to a D5 aspartic acid prevents such a hydrogen bond, eliminating the gatekeeping mechanism and precipitously reducing selectivity. The resulting binding affinity to Ln3+ cations is non-monotonic but generally increases with cation size. These results suggest a molecular design paradigm: the reduced affinity for larger lanthanides is due to open pocket conformations, while the selectivity of smaller Ln3+ cations over larger ones is due to the gatekeeping hydrogen bond.

Cation Effects on CO2 Delivery to Cu Electrode in Reactive Capture of CO2

The direct electrochemical conversion of captured CO2, known as reactive capture of CO2 (RCC), remains a formidable challenge in heterogeneous catalysis. Given that amines are one of the most widely used capture agents for CO2, it would be desirable to electrochemically reduce the resultant adducts, such as carbamate, directly in RCC. However, current understanding suggests that the primary species undergoing reduction in RCC with amines is the CO2 dissociated from the sorbent. Herein, we employ ab initio molecular dynamics (AIMD) with DFT to analyze how the nature of alkali metal cations in the electrolyte affects carbamate at the Cu surface, thereby assessing the possibility of promoting RCC by cation effects. The simulations show that the carbamate's orientation with respect to the electrode is governed by the optimal distance between the carbamate and the cation, specifically how this distance aligns with the cation's hydration spheres. Moreover, the slow-growth AIMD results indicate that the CO2 dissociation barrier correlates with the orientation of carbamate at the interface. When the carbamate resides beyond the cation's first hydration sphere, it adopts a flat orientation with respect to the surface that promotes the release of CO2 from the capture agent. In contrast, when the carbamate disrupts the first hydration sphere and exhibits a strong cation-π interaction, it adopts an upright orientation that is less conducive to CO2 release. These findings reveal a nontrivial cation effect in RCC, suggesting that it should be possible to optimize RCC via the choice of the electrolyte.

Magnetosensitivity of Model Flavin-Tryptophan Radical Pairs in a Dynamic Protein Environment

Light-induced radical pairs in cryptochrome proteins located in the retina are thought to be the receptors at the heart of the magnetic compass sense of migratory songbirds. Reliable simulations of the performance of such sensors face several fundamental challenges. The quantum spin dynamics of large spin systems must be modeled for periods in excess of a microsecond including realistic local magnetic interactions that fluctuate on a picosecond to microsecond time scale as a result of thermal motion. Here we employ newly developed computational methods that combine explicitly time-dependent internal magnetic interactions, obtained from molecular dynamics simulations and electronic structure calculations, with efficiently and accurately modeled spin dynamics of multinuclear electron-nuclear spin systems. We identify the range of frequencies of molecular motions that are expected to have the greatest effects on the sensitivity of the proposed compass to the direction of an Earth-strength magnetic field and obtain new insights into the potential enhancements in detection sensitivity afforded by thermal modulations of electron-nuclear hyperfine interactions.

Cholesterol Concentration in Cell Membranes and its Impact on Receptor-Ligand Interaction: A Computational Study of ATP-Sensitive Potassium Channels and ATP Binding

This work describes a computer study that looks at how different amounts of cholesterol (0%, 25%, and 50%) in cell membranes change the relationship between ATP and the KATP channel. This could explain why pancreatic beta-cells secrete insulin differently. We use computer simulations of molecular dynamics, calculations of binding free energy, and an integrated oscillator model to look at the electrical activity of beta-cells. There is a need for this kind of multiscale approach right now because cholesterol plays a part in metabolic syndrome and early type 2 diabetes. Our results showed that the increase in cholesterol concentration in the cell membrane affects the electrostatic interactions between ATP and the KATP channel, especially with charged residues in the binding site. Cholesterol can influence the properties of a membrane, including its local charge distribution near the channel. This affects the electrostatic environment around the ATP-binding site, increasing  the affinity of ATP for the channel as our results indicated from 0 to 25 and 50% cholesterol (- 141 to - 113 kJ/mol, respectively). Simulating this change in the affinity to ATP of the KATP channels in a model of the electrical activity of the pancreatic beta-cell indicates that even a minimal increase could produce hyperinsulism. The study answers an important research question about how the structure of the membrane affects the function of KATP and, in turn, insulin releases a common feature of metabolic syndrome and early stages of type 2 diabetes.

Phytochemical inhibitors of squalene epoxidase: Integrated In silico and In vitro mechanistic insights for targeting cholesterol biosynthesis

Squalene epoxidase (SQLE) is a critical enzyme in the sterol biosynthesis pathway and a promising therapeutic target for various diseases. This study investigates the inhibitory potential of six phytochemicals, amentoflavone, dihydromyricetin, withaferin A, ursolic acid, paeonol, and maslinic acid, against SQLE through an integrated approach combining in silico predictions and experimental validation. Computational analyses, including molecular docking, molecular dynamics (MD) simulations, Potential energy landscape (PEL), MM/PBSA analysis, and ADMET profiling, identified amentoflavone, dihydromyricetin, and withaferin A as the most promising inhibitors, with high binding affinities and stable interactions within the SQLE binding site. Among these, amentoflavone exhibited the strongest binding affinity (-10.4 kcal/mol), binding free energy (-42.01 ± 2.78 kcal/mol), and stability during a 300 ns MD simulation, supported by favorable MD trajectory and interaction energy profiles. Experimental in vitro assays further validated these findings, showing that all tested compounds exhibited inhibitory activity against SQLE, with amentoflavone demonstrating the lowest IC50 (1.92 ± 0.28 μM), confirming its role as a potent inhibitor. Enzyme kinetics studies revealed that maslinic acid and ursolic acid exhibited noncompetitive inhibition, while withaferin A, paeonol, and amentoflavone acted as competitive inhibitors. Dihydromyricetin demonstrated a mixed inhibition mode due to its dual interaction with the enzyme's active and allosteric sites. The pharmacokinetic analysis indicated that all compounds exhibited drug-like properties, with varying ADMET profiles influencing their potential as therapeutic candidates. This study highlights the therapeutic potential of amentoflavone, withaferin A, and dihydromyricetin as potent SQLE inhibitors, underscoring the value of integrated in silico and in vitro approaches in drug discovery.

Quantifying the Cooperativity of Backbone Hydrogen Bonding

The hydrogen bonds (H-bonds) between backbone amide and carbonyl groups are fundamental to the stability, structure, and dynamics of proteins. A key feature of such hydrogen bonding interactions is that multiple H-bonds can enhance each other when aligned, as such in theα $$ \alpha $$ -helix orβ $$ \beta $$ -sheet secondary structures. To better understand this cooperative effect, we propose a new physical quantity to evaluate the cooperativity of intermolecular interactions. Using H-bond aligned N-methylacetamide molecules as the model system, we assess the cooperativity of protein backbone hydrogen bonds using quantum chemistry (QM) calculations at the MP2/aug-cc-pVTZ level, revealing cooperative energies ranging from 2 to 4.3 kcal/mol. A set of protein force fields was benchmarked against QM results. While the additive force field failed to reproduce cooperativity, polarizable force fields, including the Drude and AMOEBA protein force fields, have been found to reproduce the trend of QM results, albeit with smaller magnitude. This work demonstrates the theoretical utility of the proposed formula for quantifying cooperativity and its relevance in force field parameterization. Incorporating cooperative energy into polarizable models presents a pathway to achieving more accurate simulations of biomolecular systems.

Identification of a rare variant in TNNT3 responsible for familial dilated cardiomyopathy through whole-exome sequencing and in silico analysis

BACKGROUND

Dilated cardiomyopathy (DCM) is a prevalent etiology of heart failure, distinguished by the gradual and frequently irreversible myocardial muscle impairment. Roughly 50% of DCM occurrences stem from hereditary rare variants. In this study, our aim was to identify the genetic cause of DCM in a pedigree with several affected individuals across four generations.

METHODS

Whole exome sequencing was performed on the proband, with variants filtered and analyzed using in silico tools. Co-segregation analysis was conducted using Sanger sequencing. Protein structure modeling and protein-protein interaction evaluations were performed using AlphaFold3 and HADDOCK2.4, respectively.

RESULTS

We identified a missense rare variant in the TNNT3 gene, leading to the p.Glu125Gly alteration in the Troponin T3 (TNNT3). This rare variant is strongly implicated as the causative factor for DCM in the pedigree. Several key factors underscore its significance: the rare variant co-segregates with the disease in the pedigree, is absent in 850 control samples, alters a conserved amino acid, is predicted to detrimentally affect protein function, and results in structural changes.

CONCLUSIONS

Our findings suggest that TNNT3 rare variants can induce DCM by weakening the binding energy between TNNT3 and Tropomyosin (TPM), leading to functional deficiencies in muscle contraction, as demonstrated by our structural modeling and docking studies. Troponin T is essential for the proper contraction of striated muscles and is related to cardiac development. Bioinformatics investigations have elucidated the involvement of TNNT3-related pathways, notably the Striated Muscle Contraction pathway and Cardiac Conduction. TNNT3 resides within loci previously implicated in cardiomyopathy. Given its crucial role in muscle contractile function, rare variants in TNNT3 hold the potential to be a significant contributing factor in the pathogenesis of DCM. A wealth of literature substantiates the correlation between troponin T and cardiac disorders. Our findings further corroborate this association.

Computer-Aided Drug Discovery for Undruggable Targets

Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.

Influence of mutations at different distances from the active center on the activity and stability of laccase 13B22

Laccases with high catalytic efficiency and high thermostability can drive a broader application scope. However, the structural distribution of key amino acids capable of significantly influencing the performance of laccases has not been explored in depth. Thirty laccase 13B22 mutants with changes in amino acids at distances of 5 Å (first shell), 5-8 Å (second shell), and 8-12 Å (third shell) from the active center were validated experimentally with 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) as substrate. Twelve of these mutants (first shell, 1; second shell, 4; third shell, 7) showed higher catalytic efficiency than the wild-type enzyme. Mutants D511E and I88L-D511E showed 5.36- and 10.58-fold increases in kcat/Km, respectively, with increases in optimal temperature of 15 °C and optimal pH from 7.0 to 8.0. Furthermore, both mutants exhibited greater thermostability compared to the wild-type, with increases of 3.33 °C and 5.06 °C in Tm and decreases of 0.39 J and 0.59 J in total structure energy, respectively. The D511E mutation resides in the third shell, while I88L is in the second shell, and their performance enhancements were attributed to alterations in the rigidity or flexibility of specific protein structural domains. Both mutants showed enhanced degradation efficiency for benzo[a]pyrene and zearalenone. These findings highlight the importance of the residues located far from the active center in the function of laccase (second shell and third shell), suggesting broader implications for enzyme optimization and biotechnological applications.

Effective polarization in potassium channel simulations: Ion conductance, occupancy, voltage response, and selectivity

Potassium (K+) channels are widely distributed in many types of organisms. They combine high efficiency (~100 pS) and K+/Na+ selectivity by a conserved selectivity filter (SF). Molecular Dynamics (MD) simulations can provide detailed, atomistic mechanisms of this sophisticated ion permeation. However, currently there are clear inconsistencies between computational predictions and experimental results. First, the ion occupancy of the SF in simulations is lower than expected (~2.5 in MD compared to ~4 in X-ray crystallography). Second, in many reported MD simulations of K+ channels, K+ conductance is typically an order of magnitude lower than experimental values. This discrepancy is in part because the force fields used in MD simulations of potassium channels do not account for polarization. One of the proposed solutions is the Electronic Continuum Correction (ECC), a force field modification that scales down formal charges, to introduce the polarization in a mean-field way. When the ECC is used in conjunction with the Charmm36m force field, the simulated K+ conductance increases 13-fold. Following the analysis of ion occupancy states using Hamiltonian Replica Exchange simulations, we propose a parameter set for Amber14sb, that also leads to a similar increase in conductance. These two force fields are then used to compute the full current-voltage (I-V) curves from MD simulations, approaching quantitative agreement with experiments at all voltages. In general, the ECC-enabled simulations are in excellent agreement with experiment, in terms of ion occupancy, conductance, current-voltage response, and K+/Na+ selectivity.

Fundamental invariant-neural network as a correction to the intramolecular force field illustrated for the full-dimensional potential energy surface of propane

As a highly effective approach for constructing potential energy surfaces (PESs) with both precision and efficiency, Δ-machine learning has been widely used in PES development. Inspired by the Δ-machine learning framework, we develop a combined model of fundamental invariant-neural network (FI-NN) and force field. Fitting the difference between the force field and ab initio energy by the FI-NN method is able to improve the accuracy of the force field. We demonstrate this enhanced methodology through the development of an intramolecular force field for propane, where CCSD(T)-F12a/AVTZ energies are initially approximated by the force field and subsequently refined using the FI-NN approach. Compared to the PES fitted by FI-NN, this combined method reduces the root mean square error (RMSE) by 50%.

Mechanistic Insights into Polyphenols-mediated Squalene Epoxidase Inhibition: Computational Models and Experimental Validation for Targeting Cholesterol Biosynthesis

Squalene epoxidase is a key enzyme in sterol biosynthesis, particularly in cholesterol metabolism. Its inhibition has emerged as a promising therapeutic strategy for metabolic disorders, hypercholesterolemia, and certain infections. Herein, we investigated the SQLE inhibitory potential of six polyphenolic compounds, identified through in silico virtual screening of a large natural phenolic library and selected for high predicted binding affinity and structural diversity. Molecular docking demonstrated strong interactions between these candidates and SQLE, with curcumin exhibiting the highest binding affinity (-10.1 kcal/mol). Molecular dynamics simulations confirmed stable interactions for all compounds, highlighting curcumin, piceatannol, and pterostilbene as particularly favorable. Their strong binding free energies were further supported by MM/PBSA calculations (-36.62 ± 4.17, -31.32 ± 3.77, and -32.01 ± 1.34 kcal/mol, respectively), corroborated by free energy landscape analysis. ADMET profiling revealed diverse pharmacokinetic properties among the six polyphenolics. In vitro testing confirmed curcumin as the most potent inhibitor (IC50 = 1.88 ± 0.21 µM), with piceatannol (2.55 ± 0.30 µM) and pterostilbene (2.69 ± 0.11 µM) following closely. Enzyme kinetics demonstrated that these three compounds act as competitive inhibitors targeting the enzyme's active site. Collectively, these findings highlight the combined power of computational and experimental approaches for identifying novel SQLE inhibitors.

Effects of structural parameters on the desalination performance of a multilayer stacked graphene oxide membrane: insights from molecular dynamics simulation

With the rapid development of nanotechnology, functionalized graphene membranes have shown great advantages, attracting increasing attention in recent years. However, the effects of different groups on the desalination performance of layered graphene membranes, as well as the transport and separation mechanisms of water molecules and ions within two-dimensional nanochannels, remain insufficiently understood. In this study, molecular dynamics simulations are employed to investigate the influence of multiple factors on seawater desalination performance, focusing on transport behavior and intermolecular interactions, to elucidate the structure-performance relationship between the membrane architecture and separation efficiency. Our findings indicate that the desalination performance of a multilayer stacked graphene oxide membrane (MGOM) is affected by key structural parameters, including interlayer spacing (H), offset (O), and slit width (dG). Optimal performance is achieved when H = 0.8 nm, dG = 1.1 nm, and O = 1.26 nm. Interestingly, the rejection rate of Na+ is slightly lower than that of Cl-, mainly due to the differences in the interaction between different ions and functional groups in the sheets and the hydration radius.

Designing Ultraporous Mesostructured Silica Nanoparticles for the Remediation of Per- and Polyfluoroalkyl Substances

Concerns about per- and polyfluoroalkyl substances (PFAS) have been raised globally as they are bioaccumulative, highly persistent, and invoke a range of health risks. Although phytoremediation is a sustainable PFAS remediation strategy, its efficiency is highly dependent on the PFAS analyte chain length, with limited uptake and removal of longer-chain contaminants. This study aims to develop surface-modified ultraporous mesostructured silica nanoparticles (UMNs) to facilitate PFAS phytoremediation. UMNs were synthesized and functionalized to tune their hydrophobicity and surface charge to enhance UMN affinity for PFAS. Dynamic light scattering, ς-potential, and nitrogen physisorption show that the modified UMNs had similar physical characteristics. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis shows that positively charged UMNs have a higher affinity for PFAS than negatively charged UMNs (with 20% of perfluorooctanoic acid, or PFOA, remaining in solution vs 100% of PFOA remaining in solution, respectively). When incubated with multiple PFAS, UMNs show greater removal efficiency for longer-chain and more hydrophobic PFAS. Preliminary plant studies in soil show an increased PFOA bioconcentration when positively charged UMNs are present. Molecular dynamics simulations, which focused on interactions between the different functional groups on the silica surface and PFAS molecules, were completed and show the importance of the combination of hydrophobic and electrostatic interactions to drive PFAS uptake. Overall, this study highlights the potential of surface-modified UMNs to enhance the uptake of PFAS from the environmental matrix and promote phytoremediation.

Iodine recombination in xenon solvent: Clusters in the gas to liquid-like state transition

Supercritical fluids (SCFs) have attracted significant attention as solvents for chemical reactions due to their unique properties, such as high diffusivity, low viscosity, and tunable solvation properties. These properties profoundly influence reaction kinetics and are often attributed to the formation of molecular clusters within SCFs. To study the effect of supercritical solvent on chemical reactivity and dynamics of reactions, one needs to understand the dynamics of clusters in supercritical fluid. Extensive experiments on the photodissociation and recombination of iodine in supercritical fluids served as a model system for understanding these effects. Experimental studies have been complemented by theoretical and computational investigations, which mostly employ Monte Carlo or empirical molecular dynamics simulations. However, computational studies using non-reactive force fields and ab initio approaches present challenges in capturing reactive processes at larger scales within supercritical fluids. In this work, we developed the ReaxFF parameters by training against quantum mechanics data. ReaxFF reactive force field based molecular dynamics simulations were performed, studying the dynamics of a xenon solvent and cage effect at different thermodynamic conditions for the iodine recombination reaction. We show that the conditions near the critical point are the optimal conditions to study the cage effect. We show that the average lifetime of xenon clusters ranging between 5 and 11 ps is comparable to iodine geminate recombination. Our simulation results of iodine recombination in xenon solvent demonstrate the higher probability of iodine molecule formation in the presence of xenon clusters. Finally, we show that the supercritical condition exhibits the highest recombination rate for iodine atoms.

Insights into the solution structure of the actin-binding tail domain of metavinculin by small angle X-ray scattering and molecular dynamics simulations

Vinculin is a ubiquitously expressed focal adhesion protein that plays an important role in cell-matrix and cell-to-cell junctions. Metavinculin, a muscle-specific splice variant of vinculin, contains a 68-amino acid disordered insert region in its actin binding tail domain (MVt). Mutations in this insert are linked to cardiomyopathies. This study investigates the solution structures and structural ensembles of wild-type (WT) and two mutant MVts, ΔLeu954 and R975W, which have been associated with cardiomyopathies, using small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations. SAXS analyses revealed subtle differences in the estimated maximum dimensions and corroborated the elongated shape of the MVts. Quantitative comparisons of SAXS profiles indicated similarity between the WT and ΔLeu954, whereas R975W exhibited differences in the small-angle region. MD simulations demonstrated reduced conformational flexibility and greater packing of the insert in WT compared to mutants. Notably, a salt-bridge observed between R975 and D928 in a WT simulation provides a structural basis for the destabilization caused by the R975W mutation. These findings provide insights into the structure and dynamics of WT and mutant MVt, reflecting the promise of SAXS combined with MD simulations to elucidate the structural properties of proteins with structural disorder.

Water-Mediated Interactions between Glycans Are Weakly Repulsive and Unexpectedly Long-Ranged

Glycans on the cell surface play an essential role in mediating cell-cell interactions and immune response. Despite their importance, the interactions between them have not been fully characterized. Here, we reveal, using all-atom molecular dynamics simulations and free energy calculations, that water-mediated interactions between a pair of N-glycans without a net charge are weakly repulsive with a range that exceeds their sizes. Unexpectedly, the effective glycan-glycan interactions decay logarithmically as the separation between them increases. Strikingly, this finding coincides exactly with the predicted interaction, which is entropic in origin, between two star polymers consisting of long flexible polymers grafted onto colloidal particles. The weak repulsive interaction, which extends beyond the size of a glycan, is sensitive to the relative orientation of the glycans. The effective long-range repulsive interaction vanishes if the charges on water are turned off, thus establishing that electrostatic interactions, arising in part due to the persistent hydrogen bonds between water and the glycans, are responsible for the interglycan repulsion.

Molecular insights into human phosphatidylserine synthase 2 and its regulation of SREBP pathways

Homologous proteins share similar sequences, enabling them to work together in cells to support normal physiological functions. Phosphatidylserine synthases 1 and 2 (PSS1 and PSS2) are homologous enzymes that catalyze the synthesis of phosphatidylserine (PS) from different substrates. PSS2 shows a preference for phosphatidylethanolamine (PE) as its substrate, whereas PSS1 can utilize either PE or phosphatidylcholine. Previous studies showed that inhibiting PSS1 promotes SREBP-2 cleavage. Interestingly, despite their homology, our findings reveal that PSS2 exerts an opposing effect on the cleavage of both SREBP-1 and SREBP-2. We resolved the cryo-electron microscopy (cryo-EM) structure of human PSS2 at 3.3 Å resolution. Structural comparison of the catalytic cavities between PSS1 and PSS2 along with molecular dynamics simulations uncovers the molecular details behind the substrate preference of PSS2 for PE. The lipidomic analysis showed that PSS2 deficiency leads to PE accumulation in the endoplasmic reticulum, which has been shown to inhibit the cleavage of sterol regulatory element-binding proteins (SREBPs) in mice. Thus, our findings reveal the intricate network of intracellular phospholipid metabolism and underscore the distinct regulatory roles of homologous proteins in cellular activities.

Template-Based Docking Using Automated Maximum Common Substructure Identification with EnzyDock: Mechanistic and Inhibitor Docking

EnzyDock is a multistate, multiscale CHARMM-based docking program which enables mechanistic docking, i.e., modeling enzyme reactions by docking multiple reaction states, like substrates, intermediates, transition states, and products to the enzyme, in addition to standard protein-ligand docking. To achieve docking of multiple reaction states with similar poses (i.e., consensus docking), EnzyDock employs consensus pose restraints of the docked ligand states relative to a docking template. In the current work, we present an implementation of a Maximum Common Substructure (MCS)-guided docking strategy using EnzyDock, enabling the automatic detection of similarity among query ligands. Specifically, the MCS multistate approach is employed to efficiently dock ligands along enzyme reaction coordinates, including reactants, intermediates, and products, which allows efficient and robust mechanistic docking. To demonstrate the effectiveness of the MCS strategy in modeling enzymes, it is first applied to two highly complex enzyme reaction cascades catalyzed by the diterpene synthase CotB2 and the Diels-Alderase LepI. In addition, the MCS strategy is applied to dock enzyme inhibitors using cocrystallized inhibitors or substrates to guide the docking in the enzymes dihydrofolate reductase and the SARS-CoV-2 enzyme Mpro. The latter case exemplifies the use of MCS with EnzyDock's covalent docking capabilities and QM/MM scoring option. We show that different protocols of the implemented MCS algorithm are needed to obtain mechanistic consistency (i.e., similar poses) in mechanistic docking or to accurately dock chemically diverse ligands in inhibitor docking. Although the current implementation is specific for EnzyDock, the findings should be general and transferable to additional docking programs.

PDBrestore: A Free Web Interface for Processing and Fixing Protein Chains From Raw PDB Files

We present PDBrestore, a free web interface for repairing protein PDB chains extracted from either a local PDB file or a PDB file downloaded from the Protein Data Bank. PDBrestore performs several key tasks: It adds hydrogen atoms, completes missing atoms in side chains, fills gaps in the sequence, derives the itp parameter file for a ligand according to the GAFF2 force field for GROMACS applications, and generates a reasonably pre-equilibrated solvated simulation box. The interface is designed to streamline the cumbersome preparatory work required to set up an initial protein-ligand coordinates PDB file for use in drug design projects, such as free energy perturbation or thermodynamic integration calculations of ligand binding affinities. Additionally, PDBrestore is available as a command-line application within the open-source ORAC distribution, which can be freely downloaded from the website: www1.chim.unifi.it/orac.

Data-Driven Discovery of the Origins of UV Absorption in the Alpha-3C Protein

Over the past decade, there has been a growing body of experimental work showing that proteins devoid of aromatic and conjugated groups can absorb light in the near-UV beyond 300 nm and emit visible light. Understanding the origins of this phenomenon offers the possibility of designing noninvasive spectroscopic probes for local interactions in biological systems. It was recently found that the synthetic protein α3C displays UV-vis absorption between 250 and 800 nm, which was shown to arise from charge-transfer excitations between charged amino acids. In this work, we use data-driven approach to re-examine the origins of these features using a combination of molecular dynamics and excited-state simulations. Specifically, an unsupervised learning approach beginning with encoding protein environments with local atomic descriptors is employed to automatically detect relevant structural motifs. We identify three main motifs corresponding to different hydrogen-bonding patterns that are subsequently used to perform QM/MM simulations, including the entire protein and solvent bath with the density-functional tight-binding (DFTB) approach. Hydrogen-bonding structures involving arginine and carboxylate groups appear to be the most prone to near-UV absorption. We show that the magnitude of the UV-vis absorption predicted from the simulations is rather sensitive to the size of the QM region employed as well as to the inclusion of explicit solvation.

The molecular mechanisms of visual chromophore release from cellular retinaldehyde-binding protein

Cellular retinaldehyde-binding protein (CRALBP) is an 11-cis-retinoid binding protein operating within the visual cycle. CRALBP serves as the terminal acceptor of 11-cis-retinaldehyde (11cRAL) produced within the retinal pigment epithelium (RPE) and mediates 11cRAL transport to the RPE apical microvilli. Crystallographic structures of CRALBP revealed that the 11cRAL-binding pocket is sealed off from bulk solvent, indicating a necessity for conformational changes to allow ligand egress. Here, we performed long timescale all-atom molecular dynamics simulations of CRALBP to elucidate the mechanisms of ligand release. CRALBP exhibits slower diffusive behavior in the presence of membranes containing negatively charged phospholipids, which bind to an exposed cationic pocket in CRALBP. Umbrella sampling calculations revealed thermodynamically likely pathways for 11cRAL egress. Our data suggest that the CRALBP-acidic phospholipid interaction facilitates 11cRAL release through allosteric, conformational changes that perturb the binding site, lowering ligand affinity. These findings offer insights into the molecular pathology of CRALBP-associated retinopathy.

Ensemble Adaptive Sampling Scheme: Identifying an Optimal Sampling Strategy via Policy Ranking

Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamic behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of the phase space. In this work, we present a framework for identifying the optimal sampling policy through metric-driven ranking. Our approach systematically evaluates the policy ensemble and ranks the policies based on their ability to explore the conformational space effectively. Through a series of biomolecular simulation case studies, we demonstrate that the choice of a different adaptive sampling policy at each round significantly outperforms single policy sampling, leading to faster convergence and improved sampling performance. This approach takes an ensemble of adaptive sampling policies and identifies the optimal policy for the next round based on current data. Beyond presenting this ensemble view of adaptive sampling, we also propose two sampling algorithms that approximate this ranking framework on the fly. The modularity of this framework allows incorporation of any adaptive sampling policy, making it versatile and suitable as a comprehensive adaptive sampling scheme.

Deep Potential for Interaction between Hydrated Cs+ and Graphene

The influence of hydrated cation-π interaction forces on the adsorption and filtration capabilities of graphene-based membrane materials is significant. However, the lack of interaction potential between hydrated Cs+ and graphene limits the scope of adsorption studies. Here, it is developed that a deep neural network potential function model to predict the interaction force between hydrated Cs+ and graphene. The deep potential has DFT-level accuracy, enabling accurate property prediction. This deep potential is employed to investigate the properties of the graphene surface solution, including the vibrational power spectrum of water density distribution, radial distribution function, and mean square displacement. Furthermore, the adsorption energy and charge between hydrated Cs+ and graphene were calculated for varying amounts of bound water, indicating that the presence of water molecules weakens the interaction between the ions and graphene. The method provides a powerful tool to study the adsorption behavior of hydrated cations on graphene surfaces and offers a new solution for handling radionuclides.

A Thermodynamic Cycle to Predict the Competitive Inhibition Outcomes of an Evolving Enzyme

Understanding competitive inhibition at the molecular level is essential for unraveling the dynamics of enzyme-inhibitor interactions and predicting the evolutionary outcomes of resistance mutations. In this study, we present a framework linking competitive inhibition to alchemical free energy perturbation (FEP) calculations, focusing on Escherichia coli dihydrofolate reductase (DHFR) and its inhibition by trimethoprim (TMP). Using thermodynamic cycles, we relate experimentally measured binding constants (Ki and Km) to free energy differences associated with wild-type and mutant forms of DHFR with a mean error of 0.9 kcal/mol, providing insight into the molecular underpinnings of TMP resistance. Our findings highlight the importance of local conformational dynamics in competitive inhibition. Mutations in DHFR affect substrate and inhibitor binding affinities differently, influencing the fitness landscape under selective pressure from TMP. Our FEP simulations reveal that resistance mutations stabilize inhibitor-bound or substrate-bound states through specific structural and/or dynamical effects. The interplay of these effects showcases significant molecular-level epistasis in certain cases. The ability to separately assess substrate and inhibitor binding provides valuable insights, allowing for a more precise interpretation of mutation effects and epistatic interactions. Furthermore, we identify key challenges in FEP simulations, including convergence issues arising from charge-changing mutations and long-range allosteric effects. By integrating computational and experimental data, we provide an effective approach for predicting the functional impact of resistance mutations and their contributions to evolutionary fitness landscapes. These insights pave the way for constructing robust mutational scanning protocols and designing more effective therapeutic strategies against resistant bacterial strains.

Sequence Similarity Network Guided Discovery of a Dehydrogenase for Asymmetric Carbonyl Dehydrogenation

Carbonyl dehydrogenation is one of the most valuable transformations in modern synthetic chemistry. As compared to traditional chemical synthesis methods, enzymatic dehydrogenation offers a greener and more selective alternative. However, except for a few rare natural dehydrogenases for desaturation, current enzymatic methods predominantly rely on enzyme promiscuity, which often suffers from lower efficiency and limited reaction controllability. Herein, we employed sequence similarity networks to mine natural dehydrogenases from a vast array of sequences with potential dehydrogenation activity. This approach led to the discovery of an uncharacterized FAD-dependent enzyme capable of efficiently performing the desymmetrizing desaturation of cyclohexanones, thereby generating diverse cyclohexenones bearing remote γ-quaternary stereocenters. The current method has enhanced the turnover frequency (TOF) by approximately 178-fold as compared to the best existing biocatalytic strategies and displayed almost no overoxidation reactions. Through a combination of experimental assays and computational studies, we elucidated that this enzyme enhances its dehydrogenation capability through an unconventional proton relay system, absent in previously reported enzyme promiscuity systems. Additionally, this streamlined enzymatic process demonstrated scalability to gram-scale synthesis with maintained efficiency and selectivity, offering robust and sustainable alternatives for the synthesis of chiral cyclohexenones with high optical purity.

Semaglutide Aggregates into Oligomeric Micelles and Short Fibrils in Aqueous Solution

Semaglutide is a lipopeptide with important applications in the treatment of diabetes, obesity, and other conditions. This class of drug (glucagon-like peptide-1 agonists and other lipidated peptides) may be susceptible to aggregation due to the tendency of lipopeptides to self-assemble into various nanostructures. Here, we show using cryogenic-TEM, small-angle X-ray scattering, and molecular dynamics simulations that semaglutide in aqueous solution undergoes slow aggregation into spherical micelles in water at sufficiently high concentration. A small population of needle-shaped fibril aggregates is also observed. At a lower concentration, dimer and trimer structures are formed. The micelles, once formed, are stable toward further aging. The aggregation influences the effect of semaglutide on the permeability of an epithelial gut model membrane of Caco-2 cells. These findings are expected to be important in understanding the long-term stability of semaglutide solutions and the potential effects of aggregation on therapeutic efficacy.

In Silico Approach to Design of New Multi-Targeted Inhibitors Based on Quinoline Ring with Potential Anticancer Properties

Searching for new anticancer drugs is a significant challenge for the medical community due to the current limitations of existing treatments. The primary objective of this study was to design and optimize multi-targeted drug candidates based on a quinoline scaffold. In this paper, we adopt various in silico techniques, including molecular docking, molecular dynamics simulations, and ADMET property modeling, to predict the binding affinity and interactions of 7-ethyl-10-hydroxycamptothecin derivatives with multiple biological targets. The interactions of these compounds with three potential molecular targets, topoisomerase I, bromodomain-containing protein 4, and ATP-binding cassette sub-family G member 2 proteins, were analyzed. It has been previously proved that the inhibition of these molecular targets may have beneficial effects on cancer treatment. The designed chemical compounds can effectively interact with selected proteins, thereby establishing their potential as drug candidates. Molecular docking revealed promising binding affinities, with topoisomerase I docking scores ranging from -9.0 to -10.3 kcal/mol, BRD4 scores from -6.6 to -8.0 kcal/mol, and ABCG2 scores from -8.0 to -10.0 kcal/mol. Furthermore, the ADMET property analysis indicates promising pharmacological profiles, protein binding affinity, selectivity, and bioavailability while minimizing toxicity. For example, satisfactory logP values have been demonstrated in the favorable range for bioavailability after oral administration. Additionally, several compounds exhibited predicted aqueous solubility values greater than -3, suggesting moderate-to-good solubility, which is crucial for oral drug delivery.

Tuning energy transport in helical protein nanotubes through side-chain modifications

Fibrous proteins are widely used as materials due to their biocompatibility, flexibility, and mechanical properties. With advancements in bioelectronics and flexible materials, there is increasing demand for biocompatible materials with tunable thermal conductivity. Understanding the mechanisms of thermal transport in proteins can facilitate the design of biomaterials with tailored thermal properties. In this study, we use non-equilibrium molecular dynamics (NEMD) to investigate how side-chain mass affects thermal transport in α-helix proteins. We analyze four representative residues - glycine (G), alanine (A), leucine (L), and phenylalanine (F) - and demonstrate that variations in side-chain mass significantly influence thermal conductivity. Results show that heavier side chains hinder heat transport, while lighter side chains enhance it. Phonon analysis reveals that side-chain mass primarily affects the properties of low-frequency acoustic and semi-optical phonons, which are critical for energy transfer. These findings provide insights into the design of protein-based biomaterials with customized thermal properties, offering potential applications in bioelectronics, medical devices, and sustainable materials. STATEMENT OF SIGNIFICANCE: This research explores how side chains in α-helix proteins influence their thermal conductivity through the application of molecular dynamics simulations. By analyzing four types of amino acids with differing side-chain masses, the study demonstrates that lighter side chains enhance heat transport, whereas heavier ones diminish it. This work establishes a direct correlation between protein structural features and their thermal properties, providing the groundwork that could enable the engineering of biomaterials with tailored heat conduction capabilities. The findings have implications for applications in bioelectronics, medical devices, and sustainable materials, where precise thermal management is essential, rendering this research highly relevant to scientists and engineers focused on advancing biocompatible materials with specific thermal characteristics.

Collective Vibration Decoupling of Confined Water in Membrane Channels

We previously reported that the asymmetric IR absorption of monolayer water confined within two-dimensional nanochannels is capable of nonthermally inducing a unidirectional flow [Zhang, Q. L. Phys. Rev. Lett. 2024, 132, 184003], while the reason for the difference in the collective vibration IR spectrum between the confined water (CW) and bulk water is still not fully understood. Here, using molecular dynamics simulations, we systematically demonstrated that the CW in narrow graphene membrane channels will appear as a predominant fingerprint-peak and a subpeak in the collective vibration spectrum band. A comparison with the calculated IR spectrum for the CW in the channels with different interlayer spacings revealed that the double-peaked pattern originates from the decoupling of the CW's collective vibration. The highlight spectral intensity of the fingerprint-peak is attributed to the low-cost out-of-plane vibration (wag mode) of the CW molecules. These findings help us understand the physical origins of the unique IR spectra of CW in nanochannels, thereby providing a robust theoretical support for the regulation of the CW's structure and dynamics properties by a remote terahertz stimulation.

Determinants of hydrogen bond distances in proteins

Hydrogen bonds (H-bonds) between oxygen atoms, with the O-H bond donated to the acceptor O atom (Odonor-H⋯Oacceptor), are essential for stabilizing protein structures and facilitating enzymatic reactions. The dielectric and electrostatic environment of proteins, as well as structural constraints imposed by protein folding, influence the nature of H-bonds. In this study, we investigated how these factors affect H-bond distances in proteins. Analysis of 906 high-resolution protein structures (≤1.2 Å) from the Protein Data Bank revealed that H-bond distances for H-bonds with the same donor and acceptor groups are distributed around a value primarily determined by the pKa difference between these groups (ΔpKa) in water, with lower ΔpKa values leading to shorter distances. This correlation arises from enhanced electron redistribution from the H-bond acceptor to the donor in lower ΔpKa H-bonds, which increases the covalent character of the H-bond and decreases the H⋯Oacceptor distance. In contrast, H-bond distances are largely unaffected by whether the H-bond is buried in the protein interior or exposed to bulk water, as the strength of the electrostatic interaction between the donor and acceptor groups plays a minor role in determining distances. Furthermore, analysis of H-bonds in microbial rhodopsins using a quantum mechanical/molecular mechanical approach demonstrates that the protein environment primarily influences H-bond distances electrostatically by altering the ΔpKa of the H-bond, while structural constraints impose a secondary influence by altering Odonor-H⋯Oacceptor angles or H⋯Oacceptor distances without changing ΔpKa.

Cytochrome P450 Mediated Cyclohexane Ring Formation in Forazoline Biosynthesis

Forazoline A, produced by the marine actinomycete Actinomadura sp. WMMB-499, is a unique PK/NRP hybrid macrolactone with promising antifungal in vivo efficacy through a previously unreported mechanism. Although a PKS/NRPS gene cluster was identified as a candidate for forazoline production, the precise biosynthetic pathway and the functions of the tailoring enzymes remain unclear. In this work, the functions of three cytochrome P450 mono-oxygenases (FrazP1P2P3) were characterized. Notably, FrazP2 was found to mediate cyclohexane ring formation from an 1,3,6-triene precursor during forazoline A biosynthesis, as confirmed by genetic and biochemical analysis. To gain structural and mechanistic insight into the activity of FrazP2, the crystal structure of a FrazP2-substrate complex has been solved at 2.3 Å resolution. The molecular dynamics simulations and DFT calculations revealed an unprecedented enzyme-catalyzed oxidative cyclization reaction by FrazP2. These findings expand our understanding of the catalytic diversity of cytochrome P450s, contributing to the diversification of natural products and enabling the creation of unnatural derivatives with increased antifungal potency.

Exploring the binding mode of BBA protein anchored on defective graphene and evaluating the biocompatibility of two types of graphene with λ-repressor protein

Since defects in nanomaterials are inevitable during experimental manipulation, investigating the interactions between defective materials and active biological proteins is crucial for evaluating the biocompatibility and biosafety of nanomaterials. This study employs molecular dynamics simulation techniques to investigate the interaction mechanisms between two types of graphene (ideal graphene and defective graphene) and two model proteins (BBA protein and λ-repressor protein). The simulation results indicate that both types of graphene exhibit superior biocompatibility with the λ-repressor protein compared to the BBA protein. The difference in binding modes of the BBA protein with the two graphenes arises mainly from its initial orientation. Notably, the positively charged Arg residue forces the BBA protein to "anchor" to the surface of defective graphene, significantly restricting its lateral migration. The λ-repressor protein is "anchored" onto the surface of defective graphene through hydrogen bonding interactions involving its Ser residue. Such hydrogen bonding was never reported in similar systems. The distinctive binding modes of these two model proteins with defective graphene are beneficial for the future development of safer and more efficient nanomedicine technologies.

Molecular dynamics study of the helix-to-disorder transition in short antimicrobial peptides from Urodacus yaschenkoi

The bioactivity of the short antimicrobial peptides (ssAMPs) UyCT1, CT2, CT3, CT5, Uy17, Uy192, and Uy234 from the scorpion Urodacus yaschenkoi has been well-characterized. The antagonistic effect reported in those studies on some clinical isolates of pathogenic bacteria, including Staphylococcus aureus, Klebsiella pneumoniae, and Escherichia coli was studied with an in silico approach to contrast their bioactivity in molecular terms. The peptides were modeled by generating high-quality structures with AlphaFold2, properly validated, and subjected to dynamic simulations in aqueous systems with the Gromos 43a1 and Charmm 36 force fields. Our analysis indicates that the degree of helicity of these peptides is closely linked to their composition and several physicochemical factors such as the hydrophobicity index, electrostatic potential, intrinsic flexibility, and dipole moment. We also found interesting parallels between the degree of order mentioned and the potency of each peptide with previously studied bacterial strains, specifically S. aureus. We analyzed in more detail of two specific peptides, UyCT1 and UyCT2, whose sequences are almost identical, except for the presence of a G-cap in the former. This subtle difference has a decisive impact on the conformational dynamics of these peptides, making the UyCT2 peptide more prone to disorder and the UyCT1 peptide more stable through the formation of multiple H-bonds. This analysis, based on an exhaustive characterization of the physicochemical properties of these ssAMPs, together with the determination of their conformational dynamics and the correlation with experimental data, could be the basis for the design and optimization of new drugs based on natural peptides found in scorpion venoms.

Fluorination Induced Inversion of Helicity and Self-Assembly Into Cross-α Like Piezoelectric Amyloids by Minimalistic Designer Peptide

Although initially identified as pathological aggregates, amyloid fibrillar assemblies formed by various proteins and peptides are now known to have crucial physiological roles, carrying out numerous biological functions in almost all organisms. Due to unique features, the common etiology of amyloids' cross-β structure is long posited as a template for designing artificial self-assembling systems. However, the recent discovery of cross-α amyloids indicates additional structural paradigms for self-assembly into ordered nanostructures, turning significant attention toward designing artificial nanostructures based on cross-α assembly. Herein, a minimalistic designer peptide which forms a hydrogen-bonded amyloid-like structure while remaining in the α region of conformation is engineered, to investigate the effect of aromatic, hydrophobic, and steric considerations on amyloidal assemblies. These results demonstrate a significant modulation of helicity and self-assembly, leading to the structure-dependent piezoelectric function of the amyloid-like cross-α fibrils. This study indicates a potential avenue for molecular engineering of functional peptide materials.

Exploring the modulation of phosphorylation and SUMOylation-dependent NPR1 conformational switching on immune regulators TGA3 and WRKY70 through molecular simulation

NPR1 (Nonexpressor pathogenesis-related genes 1) is regulated by multisite phosphorylation and SUMOylation, serving as a master switch for effector-triggered plant immunity through a transcriptional activator (TGA3) and repressor (WRKY70) are experimentally well studied. However, the conformational relationship between the various phosphorylation, un-phosphorylation states, and SUMOylation's role in the functional switch remains unclear. Using deep learning-based molecular modeling, docking, and multi-nanosecond simulations (totaling 2 μs) with end-state free energy calculations, we unveil how different phosphorylation states impact the dynamic stability of NPR1's four phospho-serine residues (Ser11, Ser15, Ser55, & Ser59) and binding of the TGA3-WRKY70 over SUMOylation. Results from our simulations show that the salicylic-acid induced P-Ser11/15NPR1-SUMO3 stabilizes helices and the flexible activation loop (α22Lys423 - α1Arg50 & L35Asp467-Arg51α51, and Gly27L3), thereby switching association with TGA3. The inter-helix salt-bridge formed (L10Arg99-Glu323α9 and α14Glu280-Pro264L6) between the phosphorylated NPR1-SUMO3-TGA3 engage in tight control of conformational regulation were disengaged in the unphosphorylated system. The P-Ser55/59NPR1-SUMO3-WRKY70 reorients itself and forms an electrostatic and hydrogen bond with Lys145α7 - L2Asp26, L6Arg99 - Leu293L18 and Lys262L15 - Glu241L15, α13Val239 (α310), & L17Leu267 keeps complex stable and quiescent compare to unphosphorylated NPR1-WRKY70. Subsequently, the essential dynamic and secondary structural analysis reveals that the phosphorylation inhibits the α516 (long helix) formation and reduces the communication space between the 460α25-βturn3-α30-L42590 (NPR1) and 90L9-L10107 (SUMO3), making the binding more suitable for TGA3 (260βturn-L6270) and WRKY70 (230L15-L16265) via activation loop. These findings, which reveal the atomic and structural details of the NPR1's post-translational modification, will illuminate future investigations into enhancing plant immunity.

Direct mapping of tyrosine sulfation states in native peptides by nanopore

Sulfation is considered the most prevalent post-translational modification (PTM) on tyrosine; however, its importance is frequently undervalued due to difficulties in direct and unambiguous determination from phosphorylation. Here we present a sequence-independent strategy to directly map and quantify the tyrosine sulfation states in universal native peptides using an engineered protein nanopore. Molecular dynamics simulations and nanopore mutations reveal specific interactions between tyrosine sulfation and the engineered nanopore, dominating identification across diverse peptide sequences. We show a nanopore framework to discover tyrosine sulfation in unknown peptide fragments digested from a native protein and determine the sequence of the sulfated fragment based on current blockade enhancement induced by sulfation. Moreover, our method allows direct observation of peptide sulfation in ultra-low abundance, down to 1%, and distinguishes it from isobaric phosphorylation. This sequence-independent strategy suggests the potential of nanopore to explore specific PTMs in real-life samples and at the omics level.

Inhibitory potential of furanocoumarins against cyclin dependent kinase 4 using integrated docking, molecular dynamics and ONIOM methods

Cyclin Dependent Kinase 4 (CDK4) is vital in the process of cell-cycle and serves as a G1 phase checkpoint in cell division. Selective antagonists of CDK4 which are in use as clinical chemotherapeutics cause various side-effects in patients. Furanocoumarins induce anti-cancerous effects in a range of human tumours. Therefore, targeting these compounds against CDK4 is anticipated to enhance therapeutic effectiveness. This work intended to explore the CDK4 inhibitory potential of 50 furanocoumarin molecules, using a comprehensive approach that integrates the processes of docking, drug-likeness, pharmacokinetic analysis, molecular dynamics simulations and ONIOM (Our own N-layered Integrated molecular Orbital and Molecular mechanics) methods. The top five best docked compounds obtained from docking studies were screened for subsequent analysis. The molecules displayed good pharmacokinetic properties and no toxicity. Epoxybergamottin, dihydroxybergamottin and notopterol were found to inhabit the ATP-binding zone of CDK4 with substantial stability and negative binding free energy forming hydrogen bonds with key catalytic residues of the protein. Notopterol exhibiting the highest binding energy was subjected to ONIOM calculations wherein the hydrogen bonding interactions were retained with significant negative interaction energy. Hence, through these series of computerised methods, notopterol was screened as a potent CDK4 inhibitor and can act as a starting point in successive processes of drug design.

Conformational heterogeneity and protonation equilibria shape the photocycle branching in channelrhodopsin-2

Channelrhodopsin-2 is a photoactive membrane protein serving as an ion channel, gathering significant interest for its applications in optogenetics. Despite extensive investigation, several aspects of its photocycle remain elusive and continue to be subjects of ongoing debate. Of particular interest are the localization of the P480 intermediate within the photocycle and the timing of the deprotonation of glutamic acid E90, a critical residue for ChR2 functioning. In this study, we explore the possibility of an early-P480 state, formed directly upon photoillumination of the dark-adapted state, where E90 is deprotonated, as hypothesized in a previous work [Kuhne et al. Proc. Natl. Acad. Sci. 116.19 (2019): 9380]. Employing extended molecular dynamics simulations, deprotonation free energy calculations, and the computation of the infrared band associated with E90, we provide support to the photocycle model proposed by Kuhne et al. Furthermore, our findings show that E90 protonation state is influenced by diverse interconnected variables and provide molecular detail insights that connect E90 interaction pattern with its deprotonation propensity. Our data demonstrate in fact that both protonated and deprotonated E90 are possible in P480 depending on E90 hydrogen bonding pattern and explaining the molecular mechanism at the basis of P480 accumulation under continuous illumination.

Computational modeling of the anti-inflammatory complexes of IL37

Interleukin (IL) 37 is an anti-inflammatory cytokine belonging to the IL1 protein family. Owing to its pivotal role in modulating immune responses, elucidating the IL37 complex structures holds substantial therapeutic promise for various autoimmune disorders and cancers. However, none of the structures of IL37 complexes have been experimentally characterized. This computational study aims to address this gap through molecular modeling and classical molecular dynamics simulations. We modeled all protein-protein complexes of IL37 using a range of methods from homology modeling to AlphaFold2 multimer predictions. Models that successfully recapitulated experimental features underwent further analysis through molecular dynamics simulations. As positive controls, binary and ternary complexes of IL18 from PDB were included for comparison. Several key findings emerged from the comparative analysis of IL37 and IL18 complexes. IL37 complexes exhibited higher mobility than the IL18 complexes. Simulations of the IL37-IL18Rα complex revealed altered receptor conformations capable of accommodating a dimeric IL37, with the N-terminal loop of IL37 contributing significantly to complex mobility. Additionally, the glycosyl chain on N297 of IL18Rα, which contours one edge of the cytokine binding surface, acted as a steric block against the N-terminal loop of IL37. Further, investigations into interactions between IL37 and IL18BP suggested that a binding mode homologous to IL18 was unstable for IL37, indicating an alternative binding mechanism. Altogether, this study accesses to the structure and dynamics of IL37 complexes, revealing the structural underpinnings of the IL37's modulatory effect on the IL18 signaling pathway.

Quantum Mechanics/Molecular Mechanics Studies on the Excited-State Relaxation Mechanisms of Cytidine Analogues: 2'-Deoxy-5-Methylcytidine and 2'-Deoxy-5-Hydroxymethylcytidine in Aqueous Solution

We have used the high-level QM(CASPT2//CASSCF)/MM method to investigate the excited-state properties and decay pathways of two important cytidine analogues, i.e., 2'-deoxy-5-methylcytidine (5mdCyd) and 2'-deoxy-5-hydroxymethylcytidine (5hmdCyd), in aqueous solution. In view of the computed minimum-energy structures, conical intersections, and crossing points, and the relevant excited-state decay paths including the different internal conversion (IC) and intersystem crossing (ISC) routes in and between the S1, T1, T2, and S0 states, we finally provided the feasible excited-state relaxation mechanisms of these two important epigenetic DNA nucleosides. Upon 285 nm photoexcitation, the lowest spectroscopically bright S1(ππ*) state is initially populated in the Franck-Condon (FC) region in both solvated systems and then mainly occurs direct IC to the ground state through the nearby accessible S1/S0 conical intersection, with the QM(CASPT2)/MM computed energy barriers of 9.5 and 1.6 kcal/mol for 5mdCyd and 5hmdCyd, respectively. In addition, the S1(ππ*) state can partially hop to the T1(ππ*) state directly or is mediated by the T2(ππ*) state. In comparison to the favorable singlet-mediated IC channel, the minor S1→T1 and S1→T2→T1 ISCs would take place slowly. Subsequently, the T1 state will further approach the nearby T1/S0 crossing point to slowly deactivate to the S0 state. Due to the T1/S0 crossing point above the T1-MIN as well with the small T1/S0 SOC, i.e., 9.8 kcal/mol and 0.3 cm-1 in 5mdCyd and 8.7 kcal/mol and 1.9 cm-1 in 5hmdCyd, the slow ISC would trap the system in the T1 state for a long time. The present work rationalizes the excited-state dynamics of 5mdCyd and 5hmdCyd in aqueous solution and could provide mechanistic insights into understanding the photophysics and photochemistry of similar epigenetic DNA nucleosides and their derivatives.

Role of Mutual Information Profile Shifts in Assessing the Pathogenicity of Mutations on Protein Functions: The Case of Pyrin Variants Associated With Familial Mediterranean Fever

This study presents a novel method to assess the pathogenicity of pyrin protein mutations by using mutual information (MI) as a measure to quantify the correlation between residue motions or fluctuations and associated changes affecting the phenotype. The concept of MI profile shift is presented to quantify changes in MI upon mutation, revealing insights into residue-residue interactions at critical positions. We apply this method to the pyrin protein variants, which are associated with an autosomal recessively inherited disease called familial Mediterranean fever (FMF) since the available tools do not help predict the pathogenicity of the most penetrant variants. We demonstrate the utility of MI profile shifts in assessing the effects of mutations on protein stability, function, and disease phenotype. The importance of MI shifts, particularly the negative shifts observed in the pyrin example, as indicators of severe functional effects is emphasized. Additionally, the exploration of potential compensatory mechanisms suggested by positive MI shifts, which are otherwise random and inconsequential, is highlighted. The study also discusses challenges in relating MI profile changes to disease severity and advocates for comprehensive analysis considering genetic, environmental, and stochastic factors. Overall, this study provides insights into the molecular mechanisms underlying the pathogenesis of FMF and offers a framework for identifying potential therapeutic targets based on MI profile changes induced by mutations.

Consequences of the failure of equipartition for the p-V behavior of liquid water and the hydration free energy components of a small protein

Earlier we showed that in the molecular dynamics simulation of a rigid model of water it is necessary to use an integration time-step δ t ≤ 0.5 fs to ensure equipartition between translational and rotational modes. Here we extend that study in the NVT ensemble to NpT conditions and to an aqueous protein. We study neat liquid water with the rigid, SPC/E model and the protein BBA (PDB ID: 1FME) solvated in the rigid, TIP3P model. We examine integration time-steps ranging from 0.5 fs to 4.0 fs for various thermostat plus barostat combinations. We find that a small δ t is necessary to ensure consistent prediction of the simulation volume. Hydrogen mass repartitioning alleviates the problem somewhat, but is ineffective for the typical time-step used with this approach. The compressibility, a measure of volume fluctuations, and the dielectric constant, a measure of dipole moment fluctuations, are also seen to be sensitive to δ t. Using the mean volume estimated from the NpT simulation, we examine the electrostatic and van der Waals contribution to the hydration free energy of the protein in the NVT ensemble. These contributions are also sensitive to δ t. In going from δ t = 2 fs to δ t = 0.5 fs, the change in the net electrostatic plus van der Waals contribution to the hydration of BBA is already in excess of the folding free energy reported for this protein.

Modulation of Bacterial Iron Homeostasis to Enhance Cuproptosis-like Death for the Treatment of Infected Diabetic Wound

Cuproptosis, an emerging cell death pathway, offers an alternative approach for antimicrobial therapy, but it suffers from deficiencies and health risks. Here, we design hollow Cu-enriched Prussian blue-based nanostructures (Cu-HMPBs) and find that the infected microenvironment facilitates the release of Cu ions from Cu-HMPBs, leading to Cu overload in bacterial cells. Meanwhile, Fe ions in bacterial cells are highly selectively chelated, triggering iron starvation. As a result, the proteotoxic stress and redox imbalance induced by Cu overload are aggravated upon iron starvation, thus remarkably enhancing cuproptosis-like bacterial cell death at extremely low-dose (noncytotoxic) Cu ions. Moreover, we demonstrate the effectiveness of this iron starvation-augmented antimicrobial strategy, and its efficacy is further validated in a methicillin-resistant Staphylococcus aureus (MRSA)-infected diabetic mouse wound model. Collectively, these findings provide a promising and universal strategy on iron starvation sensitizing cuproptosis-like bacterial cell death for combating drug resistance.

Influence of Alcohols on the Bending Rigidity and the Thickness of Phospholipid Membranes: The Role of Chain Length Mismatch

Alcohols influence the shape of the cells. To elucidate this phenomenon and understand the influence of alcohols on the mechanical properties of cell membranes such as bending rigidity, it is essential to investigate their effects on lipid bilayers. In this study, we explored the impact of short- and medium-chain alcohols on the bending rigidity and thickness of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) bilayers in the fluid phase. We employed various techniques, including vesicle fluctuation analysis, small-angle X-ray scattering, and differential scanning calorimetry. Experimental observations were further validated and interpreted using atomistic molecular dynamics simulations. Our results demonstrate that alcohols ranging from ethanol to octanol reduce the main phase transition temperature (Tm), bending rigidity (κ), and thickness of the bilayer (DHH). Decanol and dodecanol, on the other hand, increase Tm without significantly affecting κ and the bilayer thickness. Our study conclusively shows that alcohols shorter than decanol induce a negative chain length mismatch condition, leading to disorder and enhanced interdigitation in DMPC membranes, resulting in membrane thinning and softening. In contrast, decanol, whose chain length matches that of the lipid, enhances lipid chain order and reduces their interdigitation, resulting in no alteration in the bending rigidity and membrane thickness.

Computational analysis of zoanthamine alkaloids from Zoanthus sp. as potential DKK1 and GSK-3β inhibitors for osteoporosis therapy via Wnt signaling

Marine invertebrates are a rich source of structurally diverse secondary metabolites with broad biological activities, making them valuable for drug discovery. The genus Zoanthus is particularly noteworthy, producing numerous bioactive alkaloids, including the zoanthamines, which show promise in treating osteoporosis. Osteoporosis, a debilitating bone disease characterized by reduced bone mineral density and increased fracture risk, is linked to Wnt signaling pathway dysregulation. This highly conserved pathway maintains tissue homeostasis and is crucial for neurogenesis, synapse formation, and bone development. Dickkopf-1 (DKK1) and glycogen synthase kinase-3β (GSK-3β), key Wnt pathway regulators, are established therapeutic targets for osteoporosis. This study employed an integrated computational approach-combining molecular docking, extensive molecular dynamics (MD) simulations, and density functional theory (DFT) calculations-to assess the inhibitory potential of 69 zoanthamine-type alkaloids against DKK1 and GSK-3β. MD simulations, analyzing root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration, and free energy landscape, provided insights into protein-ligand complex stability and key interactions. Binding free energies were calculated using the MM-PBSA method combined with interaction entropy. DFT calculations further elucidated the electronic structure and reactivity of the most promising inhibitors (3α-hydroxyzoanthenamine, epioxyzoanthamine, 7α-hydroxykuroshine E, and norzoanthamine), which exhibited favorable binding interactions with key residues in target proteins. This integrative approach demonstrates the power of computational methods in drug discovery, highlighting the potential of zoanthamine alkaloids as lead compounds for innovative osteoporosis therapies.

In Silico Discovery of Potential Inhibitors Targeting the MEIG1-PACRG Complex for Male Contraceptive Development

The interaction between meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) is a critical determinant of spermiogenesis, the process by which round spermatids mature into functional spermatozoa. Disruption of the MEIG1-PACRG complex can impair sperm development, highlighting its potential as a therapeutic target for addressing male infertility or for the development of non-hormonal contraceptive methods. This study used virtual screening, molecular docking, and molecular dynamics (MD) simulations to identify small molecule inhibitors targeting the MEIG1-PACRG interface. MD simulations provided representative protein conformations, which were used to virtually screen a library of 821 438 compounds, resulting in 48 high-ranking candidates for each protein. PACRG emerged as a favorable target due to its flexible binding pockets and better docking scores compared to MEIG1. Key binding residues with compounds included W50, Y68, N70, and E74 on MEIG1, and K93, W96, E101, and H137 on PACRG. MD simulations revealed that compound stability in MEIG1 complexes is primarily maintained by hydrogen bonding with E74 and π-π stacking interactions with W50 and Y68. In PACRG complexes, compound stabilization is facilitated by hydrogen bonding with E101 and π-π interactions involving W96 and H137. These findings highlight distinct molecular determinants of ligand binding for each protein. Our work provides mechanistic insights and identifies promising compounds for further experimental validation, establishing a foundation for developing MEIG1-PACRG interaction inhibitors as male contraceptives.