A smooth particle mesh Ewald method

Structural basis for TNIP1 binding to FIP200 during mitophagy

TNIP1 has been increasingly recognized as a security check to finely adjust the rate of mitophagy by disrupting the recycling of the Unc-51-like kinase complex during autophagosome formation. Through tank-binding kinase 1-mediated phosphorylation of the TNIP1 FIP200 interacting region (FIR) motif, the binding affinity of TNIP1 for FIP200, a component of the Unc-51-like kinase complex, is enhanced, allowing TNIP1 to outcompete autophagy receptors. Consequently, FIP200 is released from the autophagosome, facilitating further autophagosome expansion. However, the molecular basis by which FIP200 utilizes its claw domain to distinguish the phosphorylation status of residues in the TNIP1 FIR motif for recognition is not well understood. Here, we elucidated multiple crystal structures of the complex formed by the FIP200 claw domain and various phosphorylated TNIP1 FIR peptides. Structural and isothermal titration calorimetry analyses identified the crucial residues in the FIP200 claw domain responsible for the specific recognition of phosphorylated TNIP1 FIR peptides. Additionally, utilizing structural comparison and molecular dynamics simulation data, we demonstrated that the C-terminal tail of TNIP1 peptide affected its binding to the FIP200 claw domain. Moreover, the phosphorylation of TNIP1 Ser123 enabled the peptide to effectively compete with the peptide p-CCPG1 (the FIR motif of the autophagy receptor CCPG1) for binding with the FIP200 claw domain. Overall, our work provides a comprehensive understanding of the specific recognition of phosphorylated TNIP1 by the FIP200 claw domain, marking an initial step toward fully understanding the molecular mechanism underlying the TNIP1-dependent inhibition of mitophagy.

Methodological and force field effects in the molecular dynamics-based prediction of binding free energies of host-guest systems

As a contribution to the understanding and rationalization of methodological and modeling effects in recent host-guest SAMPL challenges, using an alchemical molecular dynamics technique we have examined the impact of force field parameterization and ionic strength in connection with guest charge neutralization on computed dissociation free energies in two typical SAMPL heavily charged macrocyclic hosts encapsulating small protonated amines with disparate binding affinities. We have shown that the methodological treatment for host neutralization, with explicit ions or with the background neutralizing plasma in the context of alchemical calculations under periodic boundary conditions, has a moderate effect on the calculated affinities. On the other hand, we have shown that seemingly small differences in the force field parameterization in highly symmetric hosts can produce systematic effects on the structural features that can have a significant impact on the predicted binding affinities.

Identification of small-molecule ligand-binding sites on and in the ARNT PAS-B domain

Transcription factors are challenging to target with small-molecule inhibitors due to their structural plasticity and lack of catalytic sites. Notable exceptions include naturally ligand-regulated transcription factors, including our prior work with the hypoxia-inducible factor (HIF)-2 transcription factor, showing that small-molecule binding within an internal pocket of the HIF-2α Per-Aryl hydrocarbon Receptor Nuclear Translocator (ARNT)-Sim (PAS)-B domain can disrupt its interactions with its dimerization partner, ARNT. Here, we explore the feasibility of targeting small molecules to the analogous ARNT PAS-B domain itself, potentially opening a promising route to modulate several ARNT-mediated signaling pathways. Using solution NMR fragment screening, we previously identified several compounds that bind ARNT PAS-B and, in certain cases, antagonize ARNT association with the transforming acidic coiled-coil containing protein 3 transcriptional coactivator. However, these ligands have only modest binding affinities, complicating characterization of their binding sites. We address this challenge by combining NMR, molecular dynamics simulations, and ensemble docking to identify ligand-binding "hotspots" on and within the ARNT PAS-B domain. Our data indicate that the two ARNT/transforming acidic coiled-coil containing protein 3 inhibitors, KG-548 and KG-655, bind to a β-sheet surface implicated in both HIF-2 dimerization and coactivator recruitment. Furthermore, while KG-548 binds exclusively to the β-sheet surface, KG-655 can additionally bind within a water-accessible internal cavity in ARNT PAS-B. Finally, KG-279, while not a coactivator inhibitor, exemplifies ligands that preferentially bind only to the internal cavity. All three ligands promoted ARNT PAS-B homodimerization, albeit to varying degrees. Taken together, our findings provide a comprehensive overview of ARNT PAS-B ligand-binding sites and may guide the development of more potent coactivator inhibitors for cellular and functional studies.

A Study on the Regioselective Acetylation of Flavonoid Aglycons Catalyzed by Immobilized Lipases

This study aimed to explore the capacity of immobilized lipases on the acetylation of six aglycon flavonoids, namely myricetin, quercetin, luteolin, naringenin, fisetin and morin. For this purpose, lipase B from Candida antarctica (CaLB) and lipase from Thermomyces lanuginosus (TLL) were immobilized onto the surface of ZnOFe nanoparticles derived from an aqueous olive leaf extract. Various factors affecting the conversion of substrates and the formation of monoesterified and diesterified products, such as the amount of biocatalyst and the molar ratio of the substrates and reaction solvents were investigated. Both CaLB and TLL-ZnOFe achieved 100% conversion yield of naringenin to naringenin acetate after 72 h of reaction time, while TLL-ZnOFe achieved higher conversion yields of quercetin, morin and fisetin (73, 85 and 72% respectively). Notably, CaLB-ZnOFe displayed significantly lower conversion yields for morin compared with TLL-ZnOFe. Molecular docking analysis was used to elucidate this discrepancy, and it was revealed that the position of the hydroxyl groups of the B ring on morin introduced hindrances on the active site of CaLB. Finally, selected flavonoid esters showed significantly higher antimicrobial activity compared with the original compound. This work indicated that these lipase-based nanobiocatalysts can be successfully applied to produce lipophilic derivatives of aglycon flavonoids with improved antimicrobial activity.

Computational Assessment of the Phytochemicals of Panax ginseng C.A. Meyer Against Dopamine Receptor D1 for Early Huntington's Disease Prophylactics

A herb, Panax ginseng C.A. Meyer has been used traditionally for the treatment of various diseases. In this work, its chemical components have been explored by computational methods for the possibility of therapeutic potential against early Huntington's disease. The molecular docking calculations against dopamine receptor D1 (PDB ID: 7X2F) involved in pathogenesis of early Huntington's disease gave the binding affinities (kcal/mol) of schizandrin (-10.530), ergosterol (-10.124), protopanaxadiol (-9.650), panaxydol (-9.399), diphenhydramine (-9.358), and panasenoside (-9.358). The values for native ligand (-7.748) and some selected drugs, Nefazodone (-9.880), Risperidone (-9.752), and Haloperidol (-9.712) were higher revealing weaker interactions. The stability assessment of top protein-ligand adducts in terms of various geometrical and thermodynamical parameters extracted from 200 ns molecular dynamics simulations pointed to schizandrin, protopanaxadiol, and panasenoside as hit molecules. The minimal translational and rotational motion of the docked ligands at orthosteric pocket of the receptor at near physiological conditions hinted at the probability of it restricting or inhibiting over-activation of DRD1. The sustained thermodynamic spontaneity of complex formation reaction augmented the inferences derived from spatial results. The phytochemicals from Panax ginseng could be used in the prophylactics of early Huntington's disease and recommendation is made for further evaluation by experimental work.

How to Sample Dozens of Substitutions per Site with λ Dynamics

Alchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.

High Antifouling Performance of Weakly Hydrophilic Polymer Brushes: A Molecular Dynamics Study

The antifouling performance of polymer brushes usually improves with increasing hydrophilicity of the grafted polymer. However, in some cases, less hydrophilic polymers show comparable or better antifouling performance than do more hydrophilic polymers. We investigate the mechanism of this anomalous behavior using molecular dynamics (MD) simulations of coarse-grained (CG) models of weakly and strongly hydrophilic polymers. The antifouling performance is evaluated from the potential of mean force of a model protein. The strongly hydrophilic polymer exhibits a better antifouling performance than the weakly hydrophilic polymer when the substrate of the polymer brush is repulsive. However, when the substrate is sufficiently attractive, the weakly hydrophilic polymer brush becomes more effective than the strongly hydrophilic brush in a certain range of grafting density. This is because the weakly hydrophilic polymer chains form a tightly packed layer that prevents the adsorbate molecule from contacting the substrate. We also perform all-atom (AA) MD simulations for several standard polymers to examine the correspondence with the CG polymer models. The weakly hydrophilic CG polymer is found to be similar to poly[N-(2-hydroxypropyl)methacrylamide] and poly(2-hydroxyethyl methacrylate), both of which have a hydroxyl group in a monomer unit. The strongly hydrophilic CG polymer resembles zwitterionic poly(carboxybetaine methacrylate). A discussion referring to the adsorption free energies of proteins on surfaces calculated in previous AA MD studies suggests that the higher antifouling performance of less hydrophilic polymer brushes can be realized for various combinations of protein and surface.

Effect of Nanoparticle Curvature on Its Interaction with Serum Proteins

The size or the curvature of nanoparticles (NPs) plays an important role in regulating the composition of the protein corona. However, the molecular mechanisms of how curvature affects the interaction of NPs with serum proteins still remain elusive. In this study, we employ all-atom molecular dynamics simulations to investigate the interactions between two typical serum proteins and PEGylated Au NPs with three different surface curvatures (0, 0.1, and 0.5 nm-1, respectively). The results show that for proteins with a regular shape, the binding strength between the serum protein and Au NPs decreases with increasing curvature. For irregularly shaped proteins with noticeable grooves, the binding strength between the protein and Au NPs does not change obviously with increasing curvature in the cases of smaller curvature. However, as the curvature continues to increase, Au NPs may act as ligands firmly adsorbed in the protein grooves, significantly enhancing the binding strength. Overall, our findings suggest that the impact of NP curvature on protein adsorption may be nonmonotonic, which may provide useful guidelines for better design of functionalized NPs in biomedical applications.

Incorporating Noncovalent Interactions in Transfer Learning Gaussian Process Regression Models for Molecular Simulations

FFLUX is a quantum chemical topology-based multipolar force field that uses Gaussian process regression machine learning models to predict atomic energies and multipole moments on the fly for fast and accurate molecular dynamics simulations. These models have previously been trained on monomers, meaning that many-body effects, for example, intermolecular charge transfer, are missed in simulations. Moreover, dispersion and repulsion have been modeled using Lennard-Jones potentials, necessitating careful parametrization. In this work, we take an important step toward addressing these shortcomings and show that models trained on clusters, in this case, a dimer, can be used in FFLUX simulations by preparing and benchmarking a formamide dimer model. To mitigate the computational costs associated with training higher-dimensional models, we rely on the transfer of hyperparameters from a smaller source model to a larger target model, enabling an order of magnitude faster training than with a direct learning approach. The dimer model allows for simulations that account for two-body effects, including intermolecular polarization and charge penetration, and that do not require nonbonded potentials. We show that addressing these limitations allows for simulations that are closer to quantum mechanics than previously possible with the monomeric models.

Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability

Generalized ensemble methods such as Hamiltonian replica exchange (HREX) and expanded ensemble (EE) have been shown effective in free energy calculations for various contexts, given their ability to circumvent free energy barriers via nonphysical pathways defined by states with different modified Hamiltonians. However, both HREX and EE methods come with drawbacks, such as limited flexibility in parameter specification or the lack of parallelizability for more complicated applications. To address this challenge, we present the method of replica exchange of expanded ensembles (REXEE), which integrates the principles of HREX and EE methods by periodically exchanging coordinates of EE replicas sampling different yet overlapping sets of alchemical states. With the solvation free energy calculation of anthracene and binding free energy calculation of the CB7-10 binding complex, we show that the REXEE method achieves the same level of accuracy in free energy calculations as the HREX and EE methods, while offering enhanced flexibility and parallelizability. Additionally, we examined REXEE simulations with various setups to understand how different exchange frequencies and replica configurations influence the sampling efficiency in the fixed-weight phase and the weight convergence in the weight-updating phase. The REXEE approach can be further extended to support asynchronous parallelization schemes, allowing looser communications between larger numbers of loosely coupled processors such as cloud computing and therefore promising much more scalable and adaptive executions of alchemical free energy calculations. All algorithms for the REXEE method are available in the Python package ensemble_md, which offers an interface for REXEE simulation management without modifying the source code in GROMACS.

Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path?

Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.

Study of Correlated Motions to Detect the Conformational Transitions of the Intrinsically Disordered Sheep Prion Peptide

Intrinsically disordered proteins (IDPs) are known for their random structural changes throughout their sequence based on the environment. The mechanism underlying these structural changes is difficult to explain. All biological processes are known to follow the direction through which they act. A study of the correlated motion can help to understand the direction of the change. Herein, we introduced the multivariate statistical analysis (MSA) technique to study the correlated motion of the peptide. The correlated motion of the sheep prion peptide was studied with the change in the temperature and solvent. These techniques helped to identify the contributing residual motions that helped to form the different secondary structures of the protein and also the triggering factors that drive these sorts of residual motions. The structural details match the experimentally reported data. It was found that the direction of the change of the secondary structure for this peptide shifted from the C-terminal to the N-terminal with an increase in the temperature. It was found that the involvement of the hydrophobic residues present at the C-terminal and the middle residues (residues 12-17) is responsible for forming a β-sheet at the normal temperature. Hydration water was found to play an important role in this change. Insights gained from this study can be used to design strategies for desirable structural changes in the IDPs.

Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods

Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.

Illuminating Protein Allostery by Chemically Accurate Contact Response Analysis (ChACRA)

Decoding allostery at the atomic level is essential for understanding the relationship between a protein's sequence, structure, and dynamics. Recently, we have shown that decomposing temperature responses of inter-residue contacts can reveal allosteric couplings and provide useful insight into the functional dynamics of proteins. The details of this Chemically Accurate Contact Response Analysis (ChACRA) are presented here along with its application to two well-known allosteric proteins. The first protein, IGPS, is a model of ensemble allostery that lacks clear structural differences between the active and inactive states. We show that the application of ChACRA reveals the experimentally identified allosteric coupling between effector and active sites of IGPS. The second protein, ATCase, is a classic example of allostery with distinct active and inactive structural states. Using ChACRA, we directly identify the most significant residue level interactions underlying the enzyme's cooperative behavior. Both test cases demonstrate the utility of ChACRA's unsupervised machine learning approach for dissecting allostery at the residue level.

A λ-Dynamics Investigation of Insulin Wakayama and Other A3 Variant Binding Affinities to the Insulin Receptor

Insulin Wakayama is a clinical insulin variant where a conserved valine at the third residue on insulin's A chain (ValA3) is replaced with a leucine (LeuA3), weakening insulin receptor (IR) binding by 140-500-fold. This severe impact on binding from a subtle modification has posed an intriguing problem for decades. Although experimental investigations of natural and unnatural A3 mutations have highlighted the sensitivity of insulin-IR binding at this site, atomistic explanations of these binding trends have remained elusive. We investigate this problem computationally using λ-dynamics free energy calculations to model structural changes in response to perturbations of the ValA3 side chain and to calculate associated relative changes in binding free energy (ΔΔGbind). The Wakayama LeuA3 mutation and seven other A3 substitutions were studied in this work. The calculated ΔΔGbind results showed high agreement compared to experimental binding potencies with a Pearson correlation of 0.88 and a mean unsigned error of 0.68 kcal/mol. Extensive structural analyses of λ-dynamics trajectories revealed that critical interactions were disrupted between insulin and the insulin receptor as a result of the A3 mutations. This investigation also quantifies the effect that adding an A3 Cδ atom or losing an A3 Cγ atom has on insulin's binding affinity to the IR. Thus, λ-dynamics was able to successfully model the effects of mutations to insulin's A3 side chain on its protein-protein interactions with the IR and shed new light on a decades-old mystery: the exquisite sensitivity of hormone-receptor binding to a subtle modification of an invariant insulin residue.

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

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

STORMM: Structure and topology replica molecular mechanics for chemical simulations

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle-mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license.

Structure-Activity Relationship of Pyrrolidine Pentamine Derivatives as Inhibitors of the Aminoglycoside 6'-N-Acetyltransferase Type Ib

Resistance to amikacin and other major aminoglycosides is commonly due to enzymatic acetylation by the aminoglycoside 6'-N-acetyltransferase type I enzyme, of which type Ib [AAC(6')-Ib] is the most widespread among Gram-negative pathogens. Finding enzymatic inhibitors could be an effective way to overcome resistance and extend the useful life of amikacin. Small molecules possess multiple properties that make them attractive for drug development. Mixture-based combinatorial libraries and positional scanning strategy have led to the identification of a chemical scaffold, pyrrolidine pentamine, that, when substituted with the appropriate functionalities at five locations (R1-R5), inhibits AAC(6')-Ib-mediated inactivation of amikacin. Structure-activity relationship studies have shown that while truncations to the molecule result in loss of inhibitory activity, modifications of functionalities and stereochemistry have different effects on the inhibitory properties. In this study, we show that alterations at position R1 of the two most active compounds, 2700.001 and 2700.003, reduced inhibition levels, demonstrating the essential nature not only of the presence of an S-phenyl moiety at this location but also the distance to the scaffold. On the other hand, modifications on the R3, R4, and R5 positions had varied effects, demonstrating the potential for optimization. A correlation analysis between molecular docking values (ΔG) and the dose required for two-fold potentiation of the compounds described in this and the previous studies showed a significant correlation between ΔG values and inhibitory activity.

Molecular dynamics-guided optimization of BGM0504 enhances dual-target agonism for combating diabetes and obesity

The dual activation of glucagon-like peptide-1 receptor (GLP-1R) and glucose-dependent insulinotropic polypeptide receptor (GIPR) has emerged as a promising therapeutic strategy for managing type 2 diabetes and obesity. Tirzepatide, a dual agonist peptide, has exhibited superior clinical efficacy in glycemic and weight control compared to selective GLP-1R agonists. Nevertheless, the structural basis of Tirzepatide's extended half-life, attributed to an acylation side chain on the parent peptide, raises questions regarding its partial agonistic activity. Employing molecular dynamics simulations, we explored the dynamic processes of peptide-receptor interactions. We uncovered a crucial salt bridge between parent peptide and GLP-1R/GIPR at K20, a feature not discernible in cryo-electron microscopy structures. Building upon these insights, we developed an optimization strategy based on the parent peptide which involved repositioning the acylation side chain. The results of both in vitro and in vivo experiments demonstrated that the optimized peptide has twofold to threefold increase in agonistic activity compared to Tirzepatide while maintaining its extended half-life in plasma. This led to the design of BGM0504, which proved to be more effective than its predecessor, Tirzepatide, in both laboratory and animal studies.

Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and binding free energy calculations were combined together to probe the binding mechanism of non-peptide inhibitors K23 and 0Y7 and peptide ones PDI6W and PDI to MDM2. The GaMD trajectory-based DL approach successfully identified significant functional domains, predominantly located at the helixes α2 and α2', as well as the β-strands and loops between α2 and α2'. The post-processing analysis of the GaMD simulations indicated that inhibitor binding highly influences the structural flexibility and collective motions of MDM2. Calculations of molecular mechanics-generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) not only suggest that the ranking of the calculated binding free energies is in agreement with that of the experimental results, but also verify that van der Walls interactions are the primary forces responsible for inhibitor-MDM2 binding. Our findings also indicate that peptide inhibitors yield more interaction contacts with MDM2 compared to non-peptide inhibitors. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated that the piperidinone inhibitor 0Y7 shows the most pronounced impact on the free energy profiles of MDM2, with the piperidinone inhibitor demonstrating higher fluctuation amplitudes along primary eigenvectors. The hot spots of MDM2 revealed by residue-based free energy estimation provide target sites for drug design toward MDM2. This study is expected to provide useful theoretical aid for the development of selective inhibitors of MDM2 family members.

In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy

Due to considerable global prevalence and high recurrence rate, the pursuit of effective new medication for epilepsy treatment remains an urgent and significant challenge. Drug repurposing emerges as a cost-effective and efficient strategy to combat this disorder. This study leverages the transformer-based deep learning methods coupled with molecular binding affinity calculation to develop a novel in-silico drug repurposing pipeline for epilepsy. The number of candidate inhibitors against 24 target proteins encoded by gain-of-function genes implicated in epileptogenesis ranged from zero to several hundreds. Our pipeline has repurposed the medications with most anti-epileptic drugs and nearly half psychiatric medications, highlighting the effectiveness of our pipeline. Furthermore, Lomitapide, a cholesterol-lowering drug, first emerged as particularly noteworthy, exhibiting high binding affinity for 10 targets and verified by molecular dynamics simulation and mechanism analysis. These findings provided a novel perspective on therapeutic strategies for other central nervous system disease.

Deciphering the shape selective conformational equilibrium of E- and Z-locked azobenzene-tetraethylammonium ion in regulating photo-switchable K+-ion channel blocking

The search for photo-switchable optopharmacological agents that can block ion channels has been a prevalent area owing to its prime advantages of reversibility and specificity over the traditional blockers. However, the quest for a higher blocking ability shown by a less stable photo-isomer to perfectly suit the requirement of the optopharmacological agents is still ongoing. To date, only a marginal improvement in terms of blocking ability is observed by the less stable E-isomer of para-substituted locked azobenzene with TEA (LAB-TEA) for the K+-ion channel. Thus, rationalization of the limitation for achieving high activity by the E-isomer is rather essential to aid the improvement of the efficiency of photoswitchable blocker drugs. Herein, we report a molecular-level analysis on the mechanism of blocking by E- and Z-LAB-TEA with the bacterial KcsA K+-ion channel using Molecular Dynamics (MD) simulation and Quantum Mechanical (QM) calculations. The positively charged TEA fragment engages in stronger electrostatic interactions, while the neutral LAB fragment engages in weaker dispersive interactions. The binding free energy calculated by Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) for E-LAB-TEA (-22.3 kcal mol-1) shows less thermodynamic preference for binding with K+-ion channels than Z-LAB-TEA (-21.6 kcal mol-1) corroborating the experimental observation. The correlation between the structure and the binding ability of E- and Z-isomers of LAB-TEA indicates that the channel gate is narrow and acts as a bottleneck for the entry of the binder molecule inside the large cavity. Upon irradiation, the Z-isomer converts into a less stable but long and planar E-isomer (ΔE of photoisomerism = 7.0 kcal mol-1, at SA2-CASPT2(6,4)/6-31+G(d)//CASSCF(6,4)/6-31+G(d)), which is structurally more suitable to fit into the narrow channel gate rather than the curved and non-planar Z-LAB-TEA. Thus, a reduction in the ionic current is observed owing to the preferential entry and subsequent blocking by E-LAB-TEA. Discontinuing the irradiation leads to conversion to the Z-isomer, the curved nature of which hinders its spontaneous release outside the cavity, thereby contributing only a small increase in the ionic current.

Lateral diffusion of ions near membrane surface

Biological membranes isolate living cells from their environment, while allowing selective molecular transport between the inner and outer realms. For example, Na+ and K+ permeability through ionic channels contributes to neural conduction. Whether the ionic currents arise directly from cations in the bulk, or from the interface, is currently unclear. There are only scant results concerning lateral diffusion of ions on aquated membrane surfaces (and strong belief that this occurs through binding to a diffusing lipid). We performed classical molecular dynamics (MD) simulations of monovalent ions, Na+, K+, and Cl-, near the surface of the zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) membrane. Realistic force-fields for lipids (Amber's Lipid17 and Lipid21) and water (TIP4P-Ew) are tested for the mass and charge densities and the electrostatic potential across the membrane. These calculations reveal that the chloride can bind to the choline moiety through an intervening water molecule by forming a CH⋯OH hydrogen bond, while cations bind to both the phosphatic and carbonyl oxygens of phosphatidylcholine moieties. Upon transitioning from the bulk to the interface, a cation sheds some of its hydration water, which are replaced by headgroup atoms. Notably, an interfacial cation can bind 1-4 headgroup atoms, which is a key to understanding its surface hopping mechanism. We find that cation binding to three headgroup atoms immobilizes it, while binding to four energizes it. Consequently, the lateral cation diffusion rate is only 15-25 times slower than in the bulk, and 4-5 times faster than lipid self-diffusion. K+ diffusion is notably more anomalous than Na+, switching from sub- to super-diffusion after about 2 ns.

Structural Dynamics of the Methyl-Coenzyme M Reductase Active Site Are Influenced by Coenzyme F430 Modifications

Methyl-coenzyme M reductase (MCR) is a central player in methane biogeochemistry, governing methanogenesis and the anaerobic oxidation of methane (AOM) in methanogens and anaerobic methanotrophs (ANME), respectively. The prosthetic group of MCR is coenzyme F430, a nickel-containing tetrahydrocorphin. Several modified versions of F430 have been discovered, including the 172-methylthio-F430 (mtF430) used by ANME-1 MCR. Here, we employ molecular dynamics (MD) simulations to investigate the active site dynamics of MCR from Methanosarcina acetivorans and ANME-1 when bound to the canonical F430 compared to 172-thioether coenzyme F430 variants and substrates (methyl-coenzyme M and coenzyme B) for methane formation. Our simulations highlight the importance of the Gln to Val substitution in accommodating the 172 methylthio modification in ANME-1 MCR. Modifications at the 172 position disrupt the canonical substrate positioning in M. acetivorans MCR. However, in some replicates, active site reorganization to maintain substrate positioning suggests that the modified F430 variants could be accommodated in a methanogenic MCR. We additionally report the first quantitative estimate of MCR intrinsic electric fields that are pivotal in driving methane formation. Our results suggest that the electric field aligned along the CH3-S-CoM thioether bond facilitates homolytic bond cleavage, coinciding with the proposed catalytic mechanism. Structural perturbations, however, weaken and misalign these electric fields, emphasizing the importance of the active site structure in maintaining their integrity. In conclusion, our results deepen the understanding of MCR active site dynamics, the enzyme's organizational role in intrinsic electric fields for catalysis, and the interplay between active site structure and electrostatics.

Uncovering potential CDK9 inhibitors from natural compound databases through docking-based virtual screening and MD simulations

CONTEXT

Cyclin-dependent kinase 9 (CDK9) plays a significant role in gene regulation and RNA polymerase II transcription under basal and stimulated conditions. The upregulation of transcriptional homeostasis by CDK9 leads to various malignant tumors and therefore acts as a valuable drug target in addressing cancer incidences. Ongoing drug development endeavors targeting CDK9 have yielded numerous clinical candidate molecules currently undergoing investigation as potential CDK9 modulators, though none have yet received Food and Drug Administration (FDA) approval.

METHODS

In this study, we employ in silico approaches including the molecular docking and molecular dynamics simulations for the virtual screening over the natural compounds library to identify novel promising selective CDK9 inhibitors. The compounds derived from the initial virtual screening were subsequently employed for molecular dynamics simulations and binding free energy calculations to study the compound's stability under virtual physiological conditions. The first-generation CDK inhibitor Flavopiridol was used as a reference to compare with our novel hit compound as a CDK9 antagonist. The 500-ns molecular dynamics simulation and binding free energy calculation showed that two natural compounds showed better binding affinity and interaction mode with CDK9 receptors over the reference Flavopiridol. They also showed reasonable figures in the predicted absorption, distribution, metabolism, excretion, and toxicity (ADMET) calculations as well as in computational cytotoxicity predictions. Therefore, we anticipate that the proposed scaffolds could contribute to developing potential and selective CDK9 inhibitors subjected to further validations.

Fast, approximation-free molecular simulation of the SPC/Fw water model using non-reversible Markov chains

In a world made of atoms, computer simulations of molecular systems such as proteins in water play an enormous role in science. Software packages for molecular simulation have been developed for decades. They all discretize Hamilton's equations of motion and treat long-range potentials through cutoffs or discretization of reciprocal space. This introduces severe approximations and artifacts that must be controlled algorithmically. Here, we bring to fruition a paradigm for molecular simulation that relies on modern concepts in statistics to explore the thermodynamic equilibrium with an exact and efficient non-reversible Markov process. It is free of all discretizations, approximations, and cutoffs. We explicitly demonstrate that this approach reaches a break-even point with traditional molecular simulation performed at high precision, but without any of its approximations. We stress the potential of our paradigm for crucial applications in biophysics and other fields, and as a practical approach to molecular simulation. We set out a strategy to reach our goal of rigorous molecular simulation.

Dual mechanisms contribute to enhanced voltage dependence of an electric fish potassium channel

The voltage dependence of different voltage-gated potassium channels, described by the voltage at which half of the channels are open (V1/2), varies over a range of 80 mV and is influenced by factors such as the number of positive gating charges and the identity of the hydrophobic amino acids in the channel's voltage sensor (S4). Here we explore by experimental manipulations and molecular dynamics simulation the contributions of two derived features of an electric fish potassium channel (Kv1.7a) that is among the most voltage-sensitive Shaker family potassium channels known. These are a patch of four contiguous negatively charged glutamates in the S3-S4 extracellular loop and a glutamate in the S3b helix. We find that these negative charges affect V1/2 by separate, complementary mechanisms. In the closed state, the S3-S4 linker negative patch reduces the membrane surface charge biasing the channel to enter the open state while, upon opening, the negative amino acid in the S3b helix faces the second (R2) gating charge of the voltage sensor electrostatically biasing the channel to remain in the open state. This work highlights two evolutionary novelties that illustrate the potential influence of negatively charged amino acids in extracellular loops and adjacent helices to voltage dependence.

Dissecting the neuroprotective interaction between the BH4 domain of BCL-w and the IP3 receptor

BCL-w is a BCL-2 family protein that promotes cell survival in tissue- and disease-specific contexts. The canonical anti-apoptotic functionality of BCL-w is mediated by a surface groove that traps the BCL-2 homology 3 (BH3) α-helices of pro-apoptotic members, blocking cell death. A distinct N-terminal portion of BCL-w, termed the BCL-2 homology 4 (BH4) domain, selectively protects axons from paclitaxel-induced degeneration by modulating IP3 receptors, a noncanonical BCL-2 family target. Given the potential of BCL-w BH4 mimetics to prevent or mitigate chemotherapy-induced peripheral neuropathy, we sought to characterize the interaction between BCL-w BH4 and the IP3 receptor, combining "staple" and alanine scanning approaches with molecular dynamics simulations. We generated and identified stapled BCL-w BH4 peptides with optimized IP3 receptor binding and neuroprotective activities. Point mutagenesis further revealed the sequence determinants for BCL-w BH4 specificity, providing a blueprint for therapeutic targeting of IP3 receptors to achieve neuroprotection.

A methodology of quantifying membrane permeability based on returning probability theory and molecular dynamics simulation

We propose a theoretical approach to estimate the permeability coefficients of substrates (permeants) for crossing membranes from donor (D) phase to acceptor (A) phase by means of molecular dynamics (MD) simulation. A fundamental aspect of our approach involves reformulating the returning probability (RP) theory, a rigorous bimolecular reaction theory, to describe permeation phenomena. This reformulation relies on the parallelism between permeation and bimolecular reaction processes. In the present method, the permeability coefficient is represented in terms of the thermodynamic and kinetic quantities for the reactive (R) phase that exists within the inner region of a membrane. One can evaluate these quantities using multiple MD trajectories starting from phase R. We apply the RP theory to the permeation of ethanol and methylamine at different concentrations (infinitely dilute and 1 mol % conditions of permeants). Under the 1 mol% condition, the present method yields a larger permeability coefficient for ethanol (0.12 ± 0.01 cm s-1) than for methylamine (0.069 ± 0.006 cm s-1), while the values of the permeability coefficient are satisfactorily close to those obtained from the brute-force MD simulations (0.18 ± 0.03 and 0.052 ± 0.005 cm s-1 for ethanol and methylamine, respectively). Moreover, upon analyzing the thermodynamic and kinetic contributions to the permeability, we clarify that a higher concentration dependency of permeability for ethanol, as compared to methylamine, arises from the sensitive nature of ethanol's free-energy barrier within the inner region of the membrane against ethanol concentration.

Phosphorylation of GCN2 by mTOR confers adaptation to conditions of hyper-mTOR activation under stress

Adaptation to the shortage in free amino acids (AA) is mediated by 2 pathways, the integrated stress response (ISR) and the mechanistic target of rapamycin (mTOR). In response to reduced levels, primarily of leucine or arginine, mTOR in its complex 1 configuration (mTORC1) is suppressed leading to a decrease in translation initiation and elongation. The eIF2α kinase general control nonderepressible 2 (GCN2) is activated by uncharged tRNAs, leading to induction of the ISR in response to a broader range of AA shortage. ISR confers a reduced translation initiation, while promoting the selective synthesis of stress proteins, such as ATF4. To efficiently adapt to AA starvation, the 2 pathways are cross-regulated at multiple levels. Here we identified a new mechanism of ISR/mTORC1 crosstalk that optimizes survival under AA starvation, when mTORC1 is forced to remain active. mTORC1 activation during acute AA shortage, augmented ATF4 expression in a GCN2-dependent manner. Under these conditions, enhanced GCN2 activity was not dependent on tRNA sensing, inferring a different activation mechanism. We identified a labile physical interaction between GCN2 and mTOR that results in a phosphorylation of GCN2 on serine 230 by mTOR, which promotes GCN2 activity. When examined under prolonged AA starvation, GCN2 phosphorylation by mTOR promoted survival. Our data unveils an adaptive mechanism to AA starvation, when mTORC1 evades inhibition.

Dynamics and Activation of Membrane-Bound B Cell Receptor Assembly

B-cell receptor complexes (BCR) are expressed on the surface of a B-cell and are the critical regulators of adaptive immune response. Even though the relevance of antibodies has been known for almost a hundred years, the antigen-dependent activation of antibody-producing B-cells has remained elusive. Several models have been proposed for BCR activation, including cross-linking, conformation-induced oligomerization, and dissociation activation models. Recently, the first cryo-EM structure of the human B-cell antigen receptor of the IgM isotype was published. Given the new asymmetric BCR complex, we have carried out extensive molecular dynamics simulations to probe the conformational changes upon antigen binding and the influence of the membrane. We identified two critical dynamical events that could be associated with antigen-dependent activation of BCR. First, antigen binding caused increased flexibility in regions distal to the antigen binding site. Second, this increased flexibility led to the rearrangement of helices in transmembrane helices, including the relative interaction of Igα/Igβ, which has been responsible for intracellular signaling. Further, these transmembrane rearrangements led to changes in localized lipid composition. Even though the simulations considered only a single BCR complex, our work indirectly supports the dissociation activation model.

Tuning MOF/polymer interfacial pore geometry in mixed matrix membrane for upgrading CO2 separation performance

The current paradigm considers the control of the MOF/polymer interface mostly for achieving a good compatibility between the two components to ensure the fabrication of continuous mixed-matrix metal-organic framework (MMMOF) membranes. Here, we unravel that the interfacial pore shape nanostructure plays a key role for an optimum molecular transport. The prototypical ultrasmall pore AlFFIVE-1-Ni MOF was assembled with the polymer PIM-1 to design a composite with gradually expanding pore from the MOF entrance to the MOF/polymer interfacial region. Concentration gradient-driven molecular dynamics simulations demonstrated that this pore nanostructuring enables an optimum guided path for the gas molecules at the MOF/polymer interface that decisively leads to an acceleration of the molecular transport all along the MMMOF membrane. This numerical prediction resulted in the successful fabrication of a [001]-oriented nanosheets AlFFIVE-1-Ni/PIM-1 MMMOF membrane exhibiting an excellent CO2 permeability, better than many MMMs, and ideally associated with a sufficiently high CO2/CH4 selectivity that makes this membrane very promising for natural gas/biogas purification.

Structure-guided discovery of novel AflG inhibitors for aflatoxin contamination control in aspergillus flavus

Aflatoxins (AFs) are highly carcinogenic metabolites produced by Aspergillus species that can contaminate critical food staples, leading to significant health and economic risks. The cytochrome P450 monooxygenase AflG catalyzes an early step in AF biosynthesis, resulting in the conversion of averantin (AVN) to 5'-hydroxy-averantin. However, the molecular mechanism underlying the AflG-AVN interaction remains unclear. Here, we sought to understand the structural features of AflG in complex with AVN to enable the identification of inhibitors targeting the AflG binding pocket. To achieve this goal, we employed a comprehensive approach combining computational and experimental methods. Structural modeling and microsecond-scale molecular dynamics (MD) simulations yielded new insights into AflG architecture and unveiled unique ligand binding conformations of the AflG-AVN complex. High-throughput virtual screening of more than 1.3 million compounds pinpointed specific subsets with favorable predicted docking scores. The resulting compounds were ranked based on binding free energy calculations and evaluated with MD simulations and in vitro experiments with Aspergillus flavus. Our results revealed two compounds significantly inhibited AF biosynthesis. Comprehensive structural analysis elucidated the binding sites of competitive inhibitors and demonstrated their regulation of AflG dynamics. This structure-guided pipeline successfully enabled the identification of novel AflG inhibitors and provided novel molecular insights that will guide future efforts to develop effective therapeutics that prevent AF contamination.

2-Substituted (N)-Methanocarba A3 Adenosine Receptor Agonists: In Silico, In Vitro, and In Vivo Characterization

2-Arylethynyl (N)-methanocarba adenosine 5'-methylamides are selective A3 adenosine receptor (AR) agonists containing a preestablished receptor-preferred pseudoribose conformation. Here, we compare analogues having bulky 2-substitution, either containing or lacking an ethynyl spacer between adenine and a cyclic group. 2-Aryl compounds 9-11, 13, 14, 19, 22, 23, 27, 29, 31, and 34, lacking a spacer, had human (h) A3AR K i values of 2-30 nM, and others displayed lower affinity. Mouse (m) A3AR affinity varied, with 2-arylethynyl having a higher affinity than 2-aryl analogues (7, 8 > 3c, 3d > 3b). However, 2-aryl-4'-truncated derivatives had greatly reduced hA3AR affinity, even containing affinity-enhancing N 6-dopamine-derived substituents. Molecular modeling, including molecular dynamics simulation, predicted stable poses in the canonical A3AR agonist binding site, but 2-aryl (ECL2 interactions) and 2-arylethynyl (TM2 interactions) substituents have different conformations and environments. In a hA3AR miniGαi recruitment assay, 31 (MRS8062) was (slightly) more potent compared to a β-arrestin2 recruitment assay, both in engineered HEK293T cells, and its maximal efficacy (E max) was much higher (165%) than reference agonist NECA's. Thus, in the 2-aryl series, A3AR affinity and selectivity were variable and generally reduced compared to the 2-arylethynyl series, with a greater dependence on the specific aryl group present. Selected compounds were studied in vivo in an ischemic model of peripheral artery disease (PAD). Rigidified 2-arylethynyl analogues 3a-3c were protective in this model of skeletal muscle ischemia-reperfusion injury/claudication, as previously shown only for moderately A3AR-selective ribosides or (N)-methanocarba derivatives. Thus, we have expanded the A3AR agonist SAR for (N)-methanocarba adenosines.

Elucidating bis-pyrimidines as new and efficient mushroom tyrosinase inhibitors: synthesis, SAR, kinetics and computational studies

In this study, a series of novel bis-pyrimidine derivatives (1P-8P) were designed, synthesized, characterized, and investigated for their in vitro inhibitory activity against mushroom tyrosinase, an enzyme critical in melanin biosynthesis and implicated in various hyperpigmentation disorders. To the best of our knowledge, the bispyrimidine scaffold has been evaluated for the first time for its tyrosinase inhibitory activity. Their inhibitory activities were assessed, revealing inhibition with IC50 values in the micromolar range. Additionally, this series of compounds were found to inhibit tyrosinase activity in a mixed-type manner, with IC50 values ranging from 12.36 ± 1.24 to 86.67 ± 3.08 μM. To further elucidate the binding interactions, molecular docking simulations were performed, identifying key residues in the active site responsible for binding affinity. Furthermore, molecular dynamics (MD) simulations were conducted to assess the dynamic behavior, stability, and binding affinity of the most potent inhibitor, compound 6P. Quantitative Structure-Activity Relationship (QSAR) models were developed to correlate the structural features of the bis-pyrimidines with their inhibitory activity, providing insights into the structure-activity relationships (SAR) that govern their potency. The experimental and theoretical findings demonstrated excellent agreement. These findings pave the way for the development of novel bis-pyrimidine-based therapeutic agents for treating hyperpigmentation and related conditions.

Molecular Modeling of Single- and Double-Hydrocarbon-Stapled Coiled-Coil Inhibitors against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias and a subset of acute lymphoblastic leukemias. As a result of the so-called Philadelphia chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase, which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown that conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation relative to traditional small-molecule therapeutics. Here, we iterate a new generation of CCmut3 inhibitors against Bcr-CC-mediated Bcr-Abl assembly designed to address these constraints through incorporation of all-hydrocarbon staples spanning i and i + 7 positions in α-helix 2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to evaluate single- and double-stapled CCmut3 candidates in silico for dynamics and binding energetics. We further model a truncated system characterized by the deletion of α-helix 1 and the flexible loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems devoid of the CPP, with a cyclized CPP, and with an open-configuration CPP, for a total of six systems that comprise our library. From this library, we present lead-stapled peptide candidates to be synthesized and evaluated experimentally as our next iteration of inhibitors against Bcr-Abl.

Asymmetric ion transport through "Janus" MoSSe sub-nanometer pores

We conduct all-atom molecular dynamics simulations to systematically investigate the underlying mechanisms governing ion transport through a sub-nanometer pore decorated with negative charges in a "Janus" MoSSe membrane. The charge imbalance between S and Se atoms on each side of the membrane induces different types of ion adsorption processes depending on the pore inner charge configuration, and the polarity of external biases, which leads to asymmetry in ionic I-V characteristics. Statistical analysis of the total translocation times including adsorption-desorption processes, and ion dwell times indicates that potassium ions predominantly remain adsorbed during their interaction with the membrane before undertaking a quick translocation through the pore. High applied biases suppress cation adsorption, which results in fast translocation with the current flow boosted by negative inner charges around the pore. We also show that in a membrane consisting of several "Janus" layers, the applied bias necessary to overcome the sub-nm pore barrier increases with the number of layers, providing control over the ionic current.

Temperature-Dependent Dielectric Relaxation Measurements of (Betaine + Urea + Water) Deep Eutectic Solvent in Hz-GHz Frequency Window: Microscopic Insights into Constituent Contributions and Relaxation Mechanisms

A combined experimental and simulation study of dielectric relaxation (DR) of a deep eutectic solvent (DES) composed of betaine, urea, and water with the composition [Betaine:Urea:Water = 11.7:12:1 (weight ratio) and 9:18:5 (molar ratio)] was performed to explore and understand the interaction and dynamics of this system. Temperature-dependent (303 ≤ T/K ≤ 343) measurements were performed over 9 decades of frequency, combining three different measurement setups. Measured DR, comprising four distinct steps with relaxation times spreading over a few picoseconds to several nanoseconds, was found to agree well with simulations. The simulated total DR spectra, upon dissection into three self (intraspecies) and three cross (interspecies) interaction contributions, revealed that the betaine-betaine self-term dominated (∼65%) the relaxation, while the urea-urea and water-water interactions contributed only ∼7% and ∼1%, respectively. The cross-terms (betaine-urea, betaine-water, and urea-water) together accounted for <30% of the total DR. The slowest DR component with a time constant of ∼1-10 ns derived dominant contribution from betaine-betaine interactions, where betaine-water and urea-water interactions also contributed. The subnanosecond (0.1-0.6 ns) time scale originated from all interactions except betaine-water interaction. An extensive interaction of water with betaine and urea severely reduced the average number of water-water H-bonds (∼0.7) and heavily decreased the static dielectric constant of water in this DES (εs ∼ 2). Furthermore, simulated first rank collective single particle reorientational relaxations (C1(t)) and the structural H-bond fluctuation dynamics (CHB (t)) exhibited multiexponential kinetics with time scales that corresponded well with those found both in the simulated and measured DR.

Rationalization of a Streptavidin Based Enantioselective Artificial Suzukiase: An Integrative Computational Approach

An Artificial Metalloenzyme (ArM) built employing the streptavidin-biotin technology has been used for the enantioselective synthesis of binaphthyls by means of asymmetric Suzuki-Miyaura cross-coupling reactions. Despite its success, it remains a challenge to understand how the length of the biotin cofactors or the introduction of mutations to streptavidin leads the preferential synthesis of one atropisomer over the other. In this study, we apply an integrated computational modeling approach, including DFT calculations, protein-ligand dockings and molecular dynamics to rationalize the impact of mutations and length of the biotion cofactor on the enantioselectivities of the biaryl product. The results unravel that the enantiomeric differences found experimentally can be rationalized by the disposition of the first intermediate, coming from the oxidative addition step, and the entrance of the second substrate. The work also showcases the difficulties facing to control the enantioselection when engineering ArM to catalyze enantioselective Suzuki-Miyaura couplings and how the combination of DFT calculations, molecular dockings and MD simulations can be used to rationalize artificial metalloenzymes.

Structural basis of binding the unique N-terminal domain of microtubule-associated protein 2c to proteins regulating kinases of signaling pathways

Isoforms of microtubule-associated protein 2 (MAP2) differ from their homolog Tau in the sequence and interactions of the N-terminal region. Binding of the N-terminal region of MAP2c (N-MAP2c) to the dimerization/docking domains of the regulatory subunit RIIα of cAMP-dependent protein kinase (RIIDD2) and to the Src-homology domain 2 (SH2) of growth factor receptor-bound protein 2 (Grb2) have been described long time ago. However, the structural features of the complexes remained unknown due to the disordered nature of MAP2. Here, we provide structural description of the complexes. We have solved solution structure of N-MAP2c in complex with RIIDD2, confirming formation of an amphiphilic α-helix of MAP2c upon binding, defining orientation of the α-helix in the complex and showing that its binding register differs from previous predictions. Using chemical shift mapping, we characterized the binding interface of SH2-Grb2 and rat MAP2c phosphorylated by the tyrosine kinase Fyn in their complex and proposed a model explaining differences between SH2-Grb2 complexes with rat MAP2c and phosphopeptides with a Grb2-specific sequence. The results provide the structural basis of a potential role of MAP2 in regulating cAMP-dependent phosphorylation cascade via interactions with RIIDD2 and Ras signaling pathway via interactions with SH2-Grb2.

Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation

Dual-target drug design has gained significant attention in the treatment of complex diseases, such as cancers and autoimmune disorders. A widely employed design strategy is combining pharmacophores to leverage the knowledge of structure-activity relationships of both targets. Unfortunately, pharmacophore combination often struggles with long and expensive trial and error, because the protein pockets of the two targets impose complex structural constraints. In this study, we propose AIxFuse, a structure-aware dual-target drug design method that learns pharmacophore fusion patterns to satisfy the dual-target structural constraints simulated by molecular docking. AIxFuse employs two self-play reinforcement learning (RL) agents to learn pharmacophore selection and fusion by comprehensive feedback including dual-target molecular docking scores. Collaboratively, the molecular docking scores are learned by active learning (AL). Through collaborative RL and AL, AIxFuse learns to generate molecules with multiple desired properties. AIxFuse is shown to outperform state-of-the-art methods in generating dual-target drugs against glycogen synthase kinase-3 beta (GSK3β) and c-Jun N-terminal kinase 3 (JNK3). When applied to another task against retinoic acid receptor-related orphan receptor γ-t (RORγt) and dihydroorotate dehydrogenase (DHODH), AIxFuse exhibits consistent performance while compared methods suffer from performance drops, leading to a 5 times higher performance in success rate. Docking studies demonstrate that AIxFuse can generate molecules concurrently satisfying the binding mode required by both targets. Further free energy perturbation calculation indicates that the generated candidates have promising binding free energies against both targets.

Kinetic Network in Milestoning: Clustering, Reduction, and Transition Path Analysis

We present a reduction of the Milestoning (ReM) algorithm to analyze the high-dimensional Milestoning kinetic network. The algorithm reduces the Milestoning network to low dimensions but preserves essential kinetic information, such as local residence time, exit time, and mean first passage time between any two states. This is achieved in three steps. First, nodes (milestones) in the high-dimensional Milestoning network are grouped into clusters based on the metastability identified by an auxiliary continuous-time Markov chain. Our clustering method is applicable not only to time-reversible networks but also to nonreversible networks generated from practical simulations with statistical fluctuations. Second, a reduced network is established via network transformation, containing only the core sets of clusters as nodes. Finally, transition pathways are analyzed in the reduced network based on the transition path theory. The algorithm is illustrated using a toy model and a solvated alanine dipeptide in two and four dihedral angles.

Leveraging Machine Learning-Guided Molecular Simulations Coupled with Experimental Data to Decipher Membrane Binding Mechanisms of Aminosterols

Understanding the molecular mechanisms of the interactions between specific compounds and cellular membranes is essential for numerous biotechnological applications, including targeted drug delivery, elucidation of the drug mechanism of action, pathogen identification, and novel antibiotic development. However, estimation of the free energy landscape associated with solute binding to realistic biological systems is still a challenging task. In this work, we leverage the Time-lagged Independent Component Analysis (TICA) in combination with neural networks (NN) through the Deep-TICA approach for determining the free energy associated with the membrane insertion processes of two natural aminosterol compounds, trodusquemine (TRO), and squalamine (SQ). These compounds are particularly noteworthy because they interact with the outer layer of neuron membranes, protecting them from the toxic action of misfolded proteins involved in neurodegenerative disorders, in both their monomeric and oligomeric forms. We demonstrate how this strategy could be used to generate an effective collective variable for describing solute absorption in the membrane and for estimating free energy landscape of translocation via on-the-fly probability enhanced sampling (OPES) method. In this context, the computational protocol allowed an exhaustive characterization of the aminosterol entry pathway into a neuron-like lipid bilayer. Furthermore, it provided accurate prediction of membrane binding affinities, in close agreement with the experimental binding data obtained by using fluorescently labeled aminosterols and large unilamellar vesicles (LUVs). The findings contribute significantly to our understanding of aminosterol entry pathways and aminosterol-lipid membrane interactions. Finally, the computational methods deployed in this study further demonstrate considerable potential for investigating membrane binding processes.

Nanostructured lipopeptide-based membranomimetics for stabilizing bacteriorhodopsin

Nanostructured 7-9-residue cyclic and unstructured lipopeptide-based facial detergents have been engineered to stabilize the model integral membrane protein, bacteriorhodopsin. Formation of a cylindrical-type micelle assembly induced by facial amphipathic lipopeptides resembles a biological membrane more effectively than conventional micelles. The hydrophobic face of this cylindrical-type micelle provides extended stability to the membrane protein and the hydrophilic surface interacts with an aqueous environment. In our present study, we have demonstrated experimentally and computationally that lipopeptide-based facial detergents having an unstructured or β-turn conformation can stabilize membrane proteins. However, constrained peptide detergents can provide enhanced stability to bacteriorhodopsin. In this study, we have computationally examined the structural stability of bacteriorhodopsin in the presence of helical, beta-strand, and cyclic unstructured peptide detergents, and conventional detergent-like peptides. Our study demonstrates that optimal membranomimetics (detergents) for stabilizing a specific membrane protein can be screened based on the following criteria: (i) hydrodynamic radii of the self-assembled peptide detergents, (ii) stability assay of detergent-encased membrane proteins, (iii) percentage covered area of detergent-encased membrane proteins obtained computationally and (iv) protein-detergent interaction energy.

POTRA domains of the TamA insertase interact with the outer membrane and modulate membrane properties

The outer membrane (OM) of gram-negative bacteria serves as a vital organelle that is densely populated with OM proteins (OMPs) and plays pivotal roles in cellular functions and virulence. The assembly and insertion of these OMPs into the OM represent a fundamental process requiring specialized molecular chaperones. One example is the translocation and assembly module (TAM), which functions as a transenvelope chaperone promoting the folding of specific autotransporters, adhesins, and secretion systems. The catalytic unit of TAM, TamA, comprises a catalytic β-barrel domain anchored within the OM and three periplasmic polypeptide-transport-associated (POTRA) domains that recruit the TamB subunit. The latter acts as a periplasmic ladder that facilitates the transport of unfolded OMPs across the periplasm. In addition to their role in recruiting the auxiliary protein TamB, our data demonstrate that the POTRA domains mediate interactions with the inner surface of the OM, ultimately modulating the membrane properties. Through the integration of X-ray crystallography, molecular dynamic simulations, and biomolecular interaction methodologies, we located the membrane-binding site on the first and second POTRA domains. Our data highlight a binding preference for phosphatidylglycerol, a minor lipid constituent present in the OM, which has been previously reported to facilitate OMP assembly. In the context of the densely OMP-populated membrane, this association may serve as a mechanism to secure lipid accessibility for nascent OMPs through steric interactions with existing OMPs, in addition to creating favorable conditions for OMP biogenesis.

Markov State Models Reveal How Folding Kinetics Influence Absorption Spectra of Foldamers

Self-assembly of platinum(II) complex foldamers is an essential approach to fabricate advanced luminescent materials. However, a comprehensive understanding of folding kinetics and their absorption spectra remains elusive. By constructing Markov state models (MSMs) from large-scale molecular dynamics simulations, we reveal that two largely similar dinuclear alknylplatinum(II) terpyridine foldamers, Pt-PEG and Pt-PE with slightly different bridges, exhibit distinctive folding kinetics. Particularly, Pt-PEG bears bridge-dominant, plane-dominant, and cooperative pathways, while Pt-PE only prefers the plane-dominant pathway. Such preference originates from their difference in intrabridge electrostatic interactions, leading to contrastive distributions of metastable states. We also found that the bridge-dominant pathway for Pt-PEG becomes more favorable when lowering the temperature. Interestingly, based on the comprehensive conformation ensembles from our MSMs, we reveal the conformation-dependent absorption spectra of Pt-PEG and Pt-PE. Our theoretical spectra not only align with experimental results but also reveal the contributions of diverse conformations to the overall absorption bands explicitly, facilitating the rational design of stimuli-responsive smart luminescent molecules.

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

Prebound State Discovered in the Unbinding Pathway of Fluorinated Variants of the Trypsin-BPTI Complex Using Random Acceleration Molecular Dynamics Simulations

The serine protease trypsin forms a tightly bound inhibitor complex with the bovine pancreatic trypsin inhibitor (BPTI). The complex is stabilized by the P1 residue Lys15, which interacts with negatively charged amino acids at the bottom of the S1 pocket. Truncating the P1 residue of wildtype BPTI to α-aminobutyric acid (Abu) leaves a complex with moderate inhibitor strength, which is held in place by additional hydrogen bonds at the protein-protein interface. Fluorination of the Abu residue partially restores the inhibitor strength. The mechanism with which fluorination can restore the inhibitor strength is unknown, and accurate computational investigation requires knowledge of the binding and unbinding pathways. The preferred unbinding pathway is likely to be complex, as encounter states have been described before, and unrestrained umbrella sampling simulations of these complexes suggest additional energetic minima. Here, we use random acceleration molecular dynamics to find a new metastable state in the unbinding pathway of Abu-BPTI variants and wildtype BPTI from trypsin, which we call the prebound state. The prebound state and the fully bound state differ by a substantial shift in the position, a slight shift in the orientation of the BPTI variants, and changes in the interaction pattern. Particularly important is the breaking of three hydrogen bonds around Arg17. Fluorination of the P1 residue lowers the energy barrier of the transition between the fully bound state and prebound state and also lowers the energy minimum of the prebound state. While the effect of fluorination is in general difficult to quantify, here, it is in part caused by favorable stabilization of a hydrogen bond between Gln194 and Cys14. The interaction pattern of the prebound state offers insights into the inhibitory mechanism of BPTI and might add valuable information for the design of serine protease inhibitors.

Simulating the Skin Permeation Process of Ionizable Molecules

It is commonly assumed that ionizable molecules, such as drugs, permeate through the skin barrier in their neutral form. By using molecular dynamics simulations of the charged and neutral states separately, we can study the dynamic protonation behavior during the permeation process. We have studied three weak acids and three weak bases and conclude that the acids are ionized to a larger extent than the bases, when passing through the headgroup region of the lipid barrier structure, at pH values close to their pKa. It can also be observed that even if these dynamic protonation simulations are informative, in the cases studied herein they are not necessary for the calculation of permeability coefficients. It is sufficient to base the calculations only on the neutral form, as is commonly done.

In-Depth Understanding of the Effect of the Distribution of Substituents on the Morphology and Physical Properties of Ethylcellulose: Molecular Dynamics Simulations Insights

Ethylcellulose (EC) is a crucial cellulose derivative with widespread applications, particularly in the pharmaceutical industry, where precise property adjustments through chemical modification are imperative. The degree of substitution (DS) and the localization of substituents along the cellulose chains are pivotal factors in this process. However, the impact of the substituent location within the repeating unit of EC remains unexplored. To address this gap, we conducted molecular dynamics simulations on amorphous EC, comparing randomly and uniformly substituted ethyl groups in the repeating units. This comprehensive study of pairwise interactions revealed significant differences in intramolecular and intermolecular hydrogen-bonding capabilities, depending on whether the hydroxyl groups were substituted at C2, C3, or C6. While our simulations demonstrated that substituent localization in the repeating unit influenced the density, number of hydrogen bonds, and conformations, the DS emerged as the dominant determinant. This insight led us to propose and validate a hypothesis: a straightforward linear function using the properties of uniform models and molar fractions can predict the properties of randomly substituted EC with a given DS. This innovative approach is anticipated to contribute to the selection of cellulose derivatives with desirable properties for the pharmaceutical industry and new applications in other fields.