All-atom Simulations Research Articles

Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA.

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

CHARMM36 All-Atom Gas Model for Lipid Nanobubble Simulation.

Lipid nanobubbles with different gas cores may integrate the biocompatibility of lipids, powerful physicochemical properties of nanobubbles, and therapeutic effects of gas molecules, which thus promote enormous biomedical applications such as ultrasound molecular imaging, gene/drug delivery, and gas therapy. In order for further more precise applications, the exact molecular mechanisms for the interactions between lipid nanobubbles and biological systems should be studied. Molecular dynamics (MD) simulation provides a powerful computational tool for this purpose. However, previous state-of-the-art MD simulations of free gas nanobubble/lipid nanobubble employed the vacuum as their gas cores, which is not suitable for studying the interactions between functional lipid nanobubbles and biological systems and revealing the biological roles of gas molecules. Hence, in this work, we developed and optimized the CHARMM36 all-atom gas parameters for six gases including N2, O2, H2, CO, CO2, and SO2, which accurately reproduced the gas density at different pressures as well as the spontaneous formation of gas nanobubbles. Subsequent applications of these gas parameters for lipid nanobubble simulations also reproduced the self-assembly process of the lipid nanobubble. We further developed a Python script to generate all-atom lipid nanobubble simulation systems, which was proven to be efficient for all-atom MD simulations of lipid nanobubbles and to be able to capture the exact dynamics of gas molecules at the gas-lipid and lipid-water interfaces of the lipid nanobubble. In summary, the all-atom gas models proposed in this work are suitable for simulating free gas nanobubbles and lipid nanobubbles, which are supposed to overcome the shortcomings of previous state-of-the-art MD simulations with the vacuum replacing the gas core and play key roles in revealing the molecular-level interactions between lipid nanobubbles and biological systems.

Free Energy Analysis of Peptide-Induced Pore Formation in Lipid Membranes by Bridging Atomistic and Coarse-Grained Simulations.

Antimicrobial peptides (AMPs) are attractive materials for combating the antimicrobial resistance crisis because they can kill target microbes by directly disrupting cell membranes. Although thousands of AMPs have been discovered, their molecular mechanisms of action are still poorly understood. One broad mechanism for membrane disruption is the formation of membrane-spanning hydrophilic pores which can be stabilized by AMPs. In this study, we use molecular dynamics simulations to investigate the thermodynamics of pore formation in model single-component lipid membranes in the presence of one of three AMPs: aurein 1.2, melittin and magainin 2. To overcome the general challenge of modeling long time scale membrane-related behaviors, including AMP binding, clustering, and pore formation, we develop a generalizable methodology for sampling AMP-induced pore formation. This approach involves the long equilibration of peptides around a pore created with a nucleation collective variable by performing coarse-grained simulations, then backmapping equilibrated AMP-membrane configurations to all-atom resolution. We then perform all-atom simulations to resolve free energy profiles for pore formation while accurately modeling the interplay of lipid-peptide-solvent interactions that dictate pore formation free energies. Using this approach, we quantify free energy barriers for pore formation without direct biases on peptides or whole lipids, allowing us to investigate mechanisms of pore formation for these 3 AMPs that are a consequence of unbiased peptide diffusion and clustering. Further analysis of simulation trajectories then relates variations in pore lining by AMPs, AMP-induced lipid disruptions, and salt bridges between AMPs to the observed pore formation free energies and corresponding mechanisms. This methodology and mechanistic analysis have the potential to generalize beyond the AMPs in this study to improve our understanding of pore formation by AMPs and related antimicrobial materials.

Polymyxin B1 in the Escherichia coli inner membrane: A complex story of protein and lipopolysaccharide-mediated insertion

The rise in multi-drug resistant Gram-negative bacterial infections has led to an increased need for 'last-resort' antibiotics such as polymyxins. However, the emergence of polymyxin-resistant strains threatens to bring about a post-antibiotic era. Thus, there is a need to develop new polymyxin-based antibiotics, but a lack of knowledge of the mechanism of action of polymyxins hinders such efforts. It has recently been suggested that polymyxins induce cell lysis of the Gram-negative bacterial inner membrane (IM) by targeting trace amounts of lipopolysaccharide (LPS) localized there. We use multiscale molecular dynamics (MD) including long-timescale coarse-grained (CG) and all-atom (AA) simulations to investigate the interactions of polymyxin B1 (PMB1) with bacterial IM models containing phospholipids (PLs), small quantities of LPS, and IM proteins. LPS was observed to (transiently) phase separate from PLs at multiple LPS concentrations, and associate with proteins in the IM. PMB1 spontaneously inserted into the IM and localized at the LPS-PL interface, where it cross-linked lipid headgroups via hydrogen bonds, sampling a wide range of interfacial environments. In the presence of membrane proteins, a small number of PMB1 molecules formed interactions with them, in a manner that was modulated by local LPS molecules. Electroporation-driven translocation of PMB1 via water-filled pores was favored at the protein-PL interface, supporting the 'destabilizing' role proteins may have within the IM. Overall, this in-depth characterization of PMB1 modes of interaction reveals how small amounts of mislocalized LPS may play a role in pre-lytic targeting and provides insights that may facilitate rational improvement of polymyxin-based antibiotics.

Graph neural network coarse-grain force field for the molecular crystal RDX

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

Molecular Mechanism of Ciprofloxacin Translocation Through the Major Diffusion Channels of the ESKAPE Pathogens Klebsiella pneumoniae and Enterobacter cloacae.

Experimental studies on the translocation and accumulation of antibiotics in Gram-negative bacteria have revealed details of the properties that allow efficient permeation through bacterial outer membrane porins. Among the major outer membrane diffusion channels, OmpF has been extensively studied to understand the antibiotic translocation process. In a few cases, this knowledge has also helped to improve the efficacy of existing antibacterial molecules. However, the extension of these strategies to enhance the efficacy of other existing and novel drugs require comprehensive molecular insight into the permeation process and an understanding of how antibiotic and channel properties influence the effective permeation rates. Previous studies have investigated how differences in antibiotic charge distribution can influence the observed permeation pathways through the OmpF channel, and have shown that the dynamics of the L3 loop can play a dominant role in the permeation process. Here, we perform all-atom simulations of the OmpF orthologs, OmpE35 from Enterobacter cloacae and OmpK35 from Klebsiella pneumoniae. Unbiased simulations of the porins and biased simulations of the ciprofloxacin permeation processes through these channels provide insight into the differences in the permeation pathway and energetics. In addition, we show that similar to the OmpF channel, antibiotic-induced dynamics of the L3 loop are also operative in the orthologs. However, the sequence and structural differences, influence the extent of the L3 loop fluctuations with OmpK35 showing greater stability in unbiased runs and subdued fluctuations in simulations with ciprofloxacin.

Coarse-Grained Simulation of mRNA-Loaded Lipid Nanoparticle Self-Assembly.

Ionizable lipid-containing lipid nanoparticles (LNPs) have enabled the delivery of RNA for a range of therapeutic applications. In order to optimize safe, targeted, and effective LNP-based RNA delivery platforms, an understanding of the role of composition and pH in their structural properties and self-assembly is crucial, yet there have been few computational studies of such phenomena. Here we present a coarse-grained model of ionizable lipid and mRNA-containing LNPs. Our model allows access to the large length- and time-scales necessary for LNP self-assembly and is mapped and parametrized with reference to all-atom structures and simulations of the corresponding components at compositions typical of LNPs used for mRNA delivery. Our simulations reveal insights into the dynamics of self-assembly of such mRNA-encapsulating LNPs, as well as the subsequent pH change-driven LNP morphology and release of mRNA.

Non-specific yet selective interactions contribute to small molecule condensate partitioning behavior.

Biomolecular condensates are essential in various cellular processes, and their mis-regulation has been demonstrated to be underly disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by non-specific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity towards specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

Bridging the Gap: Unraveling the Mechanisms of Nanobubble Detachment from Electrode Surfaces

Gas evolution reactions play a fundamental role in the functionality of various electrochemical devices. Recent experimental studies have provided valuable insights into the behavior of gas bubble formation on nanoelectrodes; however, the experiments have limited spatial resolution to fully understand the underlying mechanisms governing the formation of nanobubbles and their stability on the surface as well as the effects of nanoscopic surface defects on their behavior. A comprehensive understanding of how bubbles behave on electrode surfaces and in the electrolyte is essential for optimizing and designing electrochemical processes. In this work, we employ all-atom molecular simulations to explore the effects of surface chemistry and defect geometry on detachment behavior of hydrogen gas bubbles on nickel electrodes at molecular level. Analyzing the dynamics of bubble detachment on the surfaces with different chemistries reveals that the rate of bubble detachment is higher on the strongly hydrophilic surface compared to the surface exhibiting weak hydrophilicity. In addition, our findings indicate that the defect geometry has significant effects on the detachment of bubbles from the surface. Specifically, the width and depth of canonical-shaped defects are demonstrated to be critical parameters affecting the behavior of the hydrogen bubbles on the nickel surface. Overall, our results show there is a complex balance between the degree of surface wettability and surface defects, determining the stability of nanobubble formation on the electrode surfaces. The results of this study could enhance our understanding of bubble detachment dynamics on the electrode surfaces, providing valuable insights for the future design of practical applications.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC5207NA27344. Release: LLNL-ABS-858292

Registry alteration in Dynein's microtubule-binding domain: A AAA domain-guided event

Dynein, a motor protein, harnesses chemical energy from ATP hydrolysis to generate mechanical output as it travels along microtubular tracks. Essential to this process is the microtubule-binding domain (MTBD), which facilitates the interaction with and detachment from microtubules. Previous studies have proposed that the mechanism governing this interaction is primarily driven by the coiled-coil stalk attached to the MTBD. However, conflicting arguments suggest the presence of two-way communications, where the binding and unbinding mechanisms may also influence the nucleotide state of dynein. In this study, we employed all-atom explicit solvent simulations, enhanced sampling techniques, and coarse-grained methodologies to systematically investigate the effects of stalk and MT on the structural stabilities of different conformations of MTBD and their sequence of events during the transition from one to another. We found that the globular MTBD domain without stalk and MT predominantly resides in its weak binding configuration. Upon introduction of a limited length of stalk and interaction with MT, a balance between the strong and weak binding configurations is restored. Further introduction of the full-length stalk and interaction with MT strengthen the kinetic stability of these two configurations. We have also explored the sequence of events of the relevant transition between these two states using coarse-grained simulation protocols both in the presence and absence of interaction with MT. In the absence of MT interactions, we found a mixed conformational state of the system that corresponds to the already published crystal structure of Dynein. In the presence of MT, the conformational change of the stalk precedes the conformational change of the MTBD globular domain. Our findings support the view that the nucleotide state of the AAA+ ring determines the state of the MTBD domain through stalk, shedding light on the intricate mechanisms governing dynein's interaction with microtubules.

Multiscale Modeling of Vinyl-Addition Polynorbornenes: The Effect of Stereochemistry.

Vinyl-addition polynorbornenes are candidates for designing high-performance polymers due to unique characteristics, which include a high glass transition temperature associated with a rigid backbone. Recent studies have established that the processability and properties of these polymers can be fine-tuned by using targeted substitutions. However, synthesis with different catalysts results in materials with distinct properties, potentially due to the presence of various stereoisomers that are difficult to quantify experimentally. Herein, we develop all-atom models of polynorbornene oligomers based on classical force fields and density functional theory. To establish the relationship between chemical architecture, chain conformations, and melt structure, we perform detailed molecular dynamics simulations with the fine-tuned atomistic force field and propose simpler coarse-grained descriptions to address the high molecular weight limit. All-atom simulations of oligomers suggest high glass transition temperatures in the range of 550-600 K. In the melt state (800 K), meso chains form highly rigid extended coils (C∞≈11) with amorphous structural characteristics similar to the X-ray diffraction data observed in the literature. In contrast, simulations with racemo chains predict highly helical tubular chain conformations that could promote assembly into crystalline structures.

Hydrodynamics and Aggregation of Nanoparticles with Protein Corona: Effects of Protein Concentration and Ionic Strength.

Multiple 10nm-sized anionic nanoparticles complexed with plasma proteins (human serum albumin (SA) or immunoglobulin gamma-1 (IgG)) at different ratios are simulated using all-atom and coarse-grained models. Coarse-grained simulations show much larger hydrodynamic radii of individual particles at a low protein concentration (a protein-to-particle ratio of 1) than at high protein concentrations or without proteins, indicating particle aggregation only at such a low protein concentration, in agreement with experiments. This particle aggregation is attributed to both electrostatic and hydrophobic particle-protein interactions, to an extent dependent on different proteins. In all-atom simulations, IgG proteins induce particle aggregation with and without salt, while SA proteins promote particle aggregation only in the presence of salt that can weaken the electrostatic repulsion between anionic particles closely linked via SA that is smaller than IgG, which also agree well with experiments. Besides charge interactions, hydrophobic interactions between particles and proteins are also important especially at the high salt concentration, leading to the increased particle-protein contact area. These findings help explain experimental observations regarding that the effects of protein concentration and ionic strength on particle aggregation depend on different plasma proteins, which are interpreted by binding free energies, electrostatic, and hydrophobic interactions between particles and proteins.

Martini 3 Coarse-Grained Model for the Cofactors Involved in Photosynthesis.

As a critical step in advancing the simulation of photosynthetic complexes, we present the Martini 3 coarse-grained (CG) models of key cofactors associated with light harvesting (LHCII) proteins and the photosystem II (PSII) core complex. Our work focuses on the parametrization of beta-carotene, plastoquinone/quinol, violaxanthin, lutein, neoxanthin, chlorophyll A, chlorophyll B, and heme. We derived the CG parameters to match the all-atom reference simulations, while structural and thermodynamic properties of the cofactors were compared to experimental values when available. To further assess the reliability of the parameterization, we tested the behavior of these cofactors within their physiological environments, specifically in a lipid bilayer and bound to photosynthetic complexes. The results demonstrate that our CG models maintain the essential features required for realistic simulations. This work lays the groundwork for detailed simulations of the PSII-LHCII super-complex, providing a robust parameter set for future studies.

Neuropilin-1 Protein May Serve as a Receptor for SARS-CoV-2 Infection: Evidence from Molecular Dynamics Simulations.

The binding of the virus to host cells is the first step in viral infection. Human cell angiotensin converting enzyme 2 (ACE2) is the most popular receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while other receptors have recently been observed in experiments. Neuropilin-1 protein (NRP1) is one of them, but the mechanism of its binding to the wild type (WT) and different variants of the virus remain unclear at the atomic level. In this work, all-atom umbrella sampling simulations were performed to clarify the binding mechanism of NRP1 to the spike protein fragments 679-685 of the WT, Delta, and Omicron BA.1 variants. We found that the Delta variant binds most strongly to NRP1, while the affinity for Omicron BA.1 slightly decreases for NRP1 compared to that of WT, and the van der Waals interaction plays a key role in stabilizing the studied complexes. The change in the protonation state of the His amino acid results in different binding free energies between variants. Consistent with the experiment, decreasing the pH was shown to increase the binding affinity of the virus to NRP1. Our results indicate that Delta and Omicron mutations not only affect fusogenicity but also affect NRP1 binding. In addition, we argue that viral evolution does not further improve NRP1 binding affinity which remains in the μM range but may increase immune evasion.

A molecular dynamics investigation into the polymer tackifiers in supercritical CO2 fracturing fluids under wellbore conditions

SC-CO2 fracturing technology is a feasible method for green extraction of unconventional oil and gas reservoirs. However, due to a series of problems such as sand transportation during SC-CO2 fracturing, CO2 thickening has become the focus of current research. Polymer thickeners have been widely used in oilfields. Due to the macroscopic nature and limitations of laboratory experiments, we have not been able to elucidate the microscopic mechanism of polymer thickening CO2 and the viscosity change of the CO2 fracturing fluid system downhole after polymer addition. In this study, we used all-atom modeling and molecular simulation of the COMPASSII force field. We selected nine polymers to study their microscopic properties and the factors affecting their effects. Finally, we simulated the viscosity change of the CO2 system after the addition of polymer tackifiers in the wellbore under the CO2 fracturing construction conditions by combining the data from one well in the oilfield.

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.

Fracture of Epoxy Networks Using Atomistic Simulations.

Predicting fracture properties through all-atomistic simulations poses challenges due to classical force field limitations in breaking covalent bonds and the computational demands of reactive force fields like ReaxFF. In addressing this, we propose a scale-bridging method for forecasting the fracture behavior of highly cross-linked epoxy combining classical force fields, the LAMMPS package REACTER, and for bond breaking a parameter based on experimental distance criterion. In our analysis, we anticipate the macroscopic fracture energy GC of the epoxy network through the application of a continuum fracture mechanics model developed for fibrils. In addition, we extract the value of the stress intensity factor KI. This modeling approach is specifically implemented for a frequently used epoxy system that consists of bisphenol F and DETDA hardener. Notably, our results demonstrate a robust correlation with existing literature and experimental studies. Moreover, our approach boasts a substantial computational time advantage, facilitating calculations that are significantly faster compared to those performed using reactive force fields.

Atomistic Modeling of Quaternized Chitosan Head Groups: Insights into Chemical Stability and Ion Transport for Anion Exchange Membrane Applications.

The chemical stability and ion transport properties of quaternized chitosan (QCS)-based anion exchange membranes (AEMs) were explored using Density Functional Theory (DFT) calculations and all-atom molecular dynamics (MD) simulations. DFT calculations of LUMO energies, reaction energies, and activation energies revealed an increasing stability trend among the head groups: propyl trimethyl ammonium chitosan (C) < oxy propyl trimethyl ammonium chitosan (B) < 2-hydroxy propyl trimethyl ammonium chitosan (A) at hydration levels (HLs) of 0 and 3. Subsequently, all-atom MD simulations evaluated the diffusion of hydroxide ions (OH-) through mean square displacement (MSD) versus time curves. The diffusion coefficients of OH- ions for the three types of QCS (A, B, and C) were observed to increase monotonically with HLs ranging from 3 to 15 and temperatures from 298 K to 350 K. Across different HLs and temperatures, the three QCS variants exhibited comparable diffusion coefficients, underlining their effectiveness in vehicular transport of OH- ions.

Multilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction.

The herpesvirus entry mediator (HVEM) and its ligand LIGHT play crucial roles in immune system regulation, including T-cell proliferation, B-cell differentiation, and immunoglobulin secretion. However, excessive T-cell activation can lead to chronic inflammation and autoimmune diseases. Thus, inhibiting the HVEM-LIGHT interaction emerges as a promising therapeutic strategy for these conditions and in preventing adverse reactions in organ transplantation. This study focused on designing peptide inhibitors, targeting the HVEM-LIGHT interaction, using molecular dynamics (MD) simulations of 65 peptides derived from HVEM. These peptides varied in length and disulfide-bond configurations, crucial for their interaction with the LIGHT trimer. By simulating 31 HVEM domain variants, including the full-length protein, we assessed conformational changes upon LIGHT binding to understand the influence of HVEM segments and disulfide bonds on the binding mechanism. Employing multitrajectory microsecond-scale, all-atom MD simulations and molecular mechanics with generalized Born and surface area (MM-GBSA) binding energy estimation, we identified promising CRD2 domain variants with high LIGHT affinity. Notably, point mutations in these variants led to a peptide with a single disulfide bond (C58-C73) and a K54E substitution, exhibiting the highest binding affinity. The importance of the CRD2 domain and Cys58-Cys73 disulfide bond for interrupting HVEM-LIGHT interaction was further supported by analyzing truncated CRD2 variants, demonstrating similar binding strengths and mechanisms. Further investigations into the binding mechanism utilized steered MD simulations at various pulling speeds and umbrella sampling to estimate the energy profile of HVEM-based inhibitors with LIGHT. These comprehensive analyses revealed key interactions and different binding mechanisms, highlighting the increased binding affinity of selected peptide variants. Experimental circular dichroism techniques confirmed the structural properties of these variants. This study not only advances our understanding of the molecular basis of HVEM-LIGHT interactions but also provides a foundation for developing novel therapeutic strategies for immune-related disorders. Furthermore, it sets a gold standard for peptide inhibitor design in drug development due to its systematic approach.