Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family

Tuning the Dimensionality of Protein-Peptide Coassemblies to Build 2D Conductive Nanomaterials

The natural self-assembly tendency of proteins to build complex structural architectures has kindled inspiration in developing supramolecular structures through the rational design of biomacromolecules. While there has been significant progress in achieving precise control over the morphology of self-assembled structures, combining different molecules within assemblies enables the design of materials with increased complexity, sophisticated structures, and a broad spectrum of functionalities. Here, the development of 1D and 2D peptide-protein coassembled systems based on the design of amphiphilic peptides and engineered proteins is described. The peptide was optimized to form stable self-assembled fibers by evaluating, computationally and experimentally, the assembling tendencies and the supramolecular features of peptides with different lengths and negative charges. A superhelical repeat protein was engineered by fusing one or two amphiphilic peptides into one or both termini. This modification drove the coassembly between the self-assembled fibers and the protein with one or two peptides, resulting in 1D or 2D coassembled systems. The protein films and the 2D coassembled system exhibited high ionic conductivity for a biomolecular system, attributed to their high content of charged residues, positioning these materials as promising candidates for developing bioelectronic devices. Thus, this work provides a versatile framework for developing coassembled materials with tunable dimensionality by using biocompatible building blocks without any additional chemical moieties, highlighting the potential for their use in biocompatible electronics.

Interplay of Hydrophobicity, Charge, and Sequence Length in Oligopeptide Coassembly

Peptide coassembly offers novel opportunities for designing advanced nanomaterials. This study used coarse-grained molecular dynamics simulations to examine the coassembly of charge-complementary peptides, assessing various ratios and the role of charge and hydrophobicity in their aggregation. We discovered that peptide length, charge, and hydrophobicity significantly influence coassembly behavior, with more hydrophobic peptides exhibiting greater aggregation despite electrostatic repulsion. Beyond the coassembly of two peptides, we also observed that the coassembly of more than two peptides will likely lead to new assembly structures and properties. Our findings underscore the importance of peptide composition and length in tuning the coassembly and the resulting properties, thus facilitating the design of complex peptide nanoparticles for biomedical and biotechnological applications.

Protein-membrane interactions with a twist

Within a framework of elasticity theory and geometry, the twister mechanism has been proposed some years ago for describing the interaction between a biofilament containing a twisted hydrophobic strip and a lipid membrane: this mechanism is capable of inducing deformations of the membrane, which can lead to its opening. The present work intends to extend this model to the interactions between a membrane and protein regions conserving their folds using coarse-grained molecular dynamics simulations. The protein region is modeled as a cylinder stabilized by a tensegrity scheme, leading to an elasticity similar to that observed in real proteins. Recording molecular dynamics trajectories of this cylinder in the presence of a fluid lipid bilayer membrane allows investigation of the effect of the positions of the hydrophobic parts on the interaction with the membrane. The entire configuration space is explored by systematically varying the hydrophobic strip width, the twisting of the strip as well as the range of hydrophobic interactions between the cylinder and the membrane. Three different states are observed: no interaction between the cylinder and membrane, the cylinder in contact with the membrane surface and the cylinder inserted into the membrane with a variable tilt angle. The variations of the tilt angle are explained using a qualitative model based on the total hydrophobic moment of the cylinder. A deformation pattern of the membrane, previously predicted for the filament-membrane interaction by the twister model, is observed for the state when the cylinder is in contact with the membrane surface, which allows estimation of the applied torques.

Benchmark of Coacervate Formation and Mechanism Exploration Using the Martini Force Field

Peptide-based coacervates are crucial for drug delivery due to their biocompatibility, versatility, high drug loading capacity, and cell penetration rates; however, their stability mechanism and phase behavior are not fully understood. Additionally, although Martini is one of the most famous force fields capable of describing coacervate formation with molecular details, a comprehensive benchmark of its accuracy has not been conducted. This research utilized the Martini 3.0 force field and machine learning algorithms to explore representative peptide-based coacervates, including those composed of polyaspartate (PAsp)/polyarginine (PArg), rmfp-1, sticker-and-spacer small molecules, and HBpep molecules. We identified key coacervate formation driving forces such as Coulomb, cation-π, and π-π interactions and established three criteria for determining coacervate formation in simulations. The results also indicate that while Martini 3.0 accurately captures coacervate formation trends, it tends to underestimate Coulomb interactions and overestimate π-π interactions. What is more, our study on drug encapsulation of HBpep and its derivative coacervates suggested that most loaded drugs were distributed on surfaces of HBpep clusters, awaiting experimental validation. This study employs simulation to enhance understanding of peptide-based coacervate phase behavior and stability mechanisms while also benchmarking Martini 3.0, thereby providing fundamental insights for future experimental and simulation investigations.

Synthesis and characterization of anthocyanin-loaded bovine serum albumin nanoparticles: unveiling the encapsulation mechanisms with computational insights

BACKGROUND

Black rice anthocyanins (BRA) offer significant health benefits but are limited in application due to their low bioavailability. Bovine serum albumin (BSA) nanoparticles (NPs) have been shown to effectively enhance the stability of encapsulated BRA. However, the mechanism of BRA-BSA NP formation and their molecular interactions remain unclear. This study prepared and characterized BRA-BSA NPs and investigated the formation mechanisms using computational simulations.

RESULTS

The optimized BRA-BSA NPs had a particle size of 128.37 ± 4.10 nm, a zeta potential of -18.93 ± 0.32 mV and an encapsulation efficiency of 81.10 ± 0.08%. Characterization showed that the NPs were stabilized through hydrophobic interactions and hydrogen bonds. BRA-BSA NPs exhibited a slow release in the upper gastrointestinal tract. Molecular dynamics simulations, both all-atom and coarse-grained, revealed that anthocyanins bound to four primary sites on the BSA surface through hydrogen bonds and van der Waals forces. Furthermore, ethanol was shown to modulate the dissociation of amino acids, promoting BSA aggregation and self-assembly into NPs.

CONCLUSION

The results demonstrate that BSA NPs loaded with BRA serve as effective carriers with high encapsulation efficiency. Molecular dynamics simulations elucidated the molecular basis of cyanidin-3-O-glucoside-BSA interactions, as well as the self-assembly process of BSA. This study therefore provides valuable insights for developing BSA-based delivery systems for BRA, advancing the fields of bioactive encapsulation of nutraceuticals. © 2025 Society of Chemical Industry.

Recent Advances in Self-Assembling Peptide-Based Nanomaterials for Enhanced Photodynamic Therapy

Self-assembling peptide-based materials with ordered nanostructures possess advantages such as good biocompatibility and biodegradability, superior controllability, and ease of chemical modification. Through covalent conjugation or non-covalent encapsulation, photosensitizers (PSs) can be carried by self-assembling peptide-based nanomaterials for targeted delivery towards tumor tissues. This improves the stability, solubility, and tumor accumulation of PSs, as well as reduces their dark toxicity. More importantly, these nanomaterials can be tailored with responsiveness to tumor microenvironment, which enables smart release of PSs for precise and enhanced photodynamic therapy (PDT). In this review, the self-assembly of peptide from the perspective of driving forces is first described, and various self-assembling peptide materials with zero to 3D nanostructures are subsequently highlighted for PDT of cancers in recent years. Finally, an outlook in this field is provided to motivate fabrication of advanced PDT nanomaterials.

A Neural-Network-Based Mapping and Optimization Framework for High-Precision Coarse-Grained Simulation

The accuracy and efficiency of a coarse-grained (CG) force field are pivotal for high-precision molecular simulations of large systems with complex molecules. We present an automated mapping and optimization framework for molecular simulation (AMOFMS), which is designed to streamline and improve the force field optimization process. It features a neural-network-based mapping function, DSGPM-TP (deep supervised graph partitioning model with type prediction). This model can accurately and efficiently convert atomistic structures to CG mappings, reducing the need for manual intervention. By integrating bottom-up and top-down methodologies, AMOFMS allows users to freely combine these approaches or use them independently as optimization targets. Moreover, users can select and combine different optimizers to meet their specific mission. With its parallel optimizer, AMOFMS significantly accelerates the optimization process, reducing the time required to achieve optimal results. Successful applications of AMOFMS include parameter optimizations for systems such as POPC and PEO, demonstrating its robustness and effectiveness. Overall, AMOFMS provides a general and flexible framework for the automated development of high-precision CG force fields.

Coarse-Grained Simulation Study of the Association of Selected Dipeptides

The association of 55 dipeptides extracted from aggregation-prone regions of selected proteins was studied by means of multiplexed replica-exchange molecular dynamics simulations with the coarse-grained UNRES model of polypeptide chains. Each simulation was carried out with 320 dipeptide molecules in a periodic box at 0.24 mol/dm3 concentration, in the 260-370 K temperature range. The temperature profiles of the degree of association, distributions of dipeptide cluster size, and structures of clusters were examined. It has been found that the dipeptides composed of strongly nonpolar (aromatic or aliphatic) residues associate nearly completely at all temperatures to form tight clusters, while those composed of charged or polar residues exhibited no or residual association. The dipeptides composed of nonpolar and small polar residues and those composed of less hydrophobic residues formed single clusters, gradually dissolving with increasing temperature, while those composed of phenylalanine or tryptophan and polar or charged residues formed multiple irregular clusters with room to accommodate water inside, suggesting the formation of liquid droplets or gels. The logarithms of the average degree of association and the free energy of aggregation per monomer were found to correlate with the dipeptide hydrophobicity.

Sequence and structural determinants of RNAPII CTD phase-separation and phosphorylation by CDK7

The intrinsically disordered carboxy-terminal domain (CTD) of the largest subunit of RNA Polymerase II (RNAPII) consists of multiple tandem repeats of the consensus heptapeptide Y1-S2-P3-T4-S5-P6-S7. The CTD promotes liquid-liquid phase-separation (LLPS) of RNAPII in vivo. However, understanding the role of the conserved heptad residues in LLPS is hampered by the lack of direct biochemical characterization of the CTD. Here, we generated a systematic array of CTD variants to unravel the sequence-encoded molecular grammar underlying the LLPS of the human CTD. Using in vitro experiments and molecular dynamics simulations, we report that the aromaticity of tyrosine and cis-trans isomerization of prolines govern CTD phase-separation. The cis conformation of prolines and β-turns in the SPXX motif contribute to a more compact CTD ensemble, enhancing interactions among CTD residues. We further demonstrate that prolines and tyrosine in the CTD consensus sequence are required for phosphorylation by Cyclin-dependent kinase 7 (CDK7). Under phase-separation conditions, CDK7 associates with the surface of the CTD droplets, drastically accelerating phosphorylation and promoting the release of hyperphosphorylated CTD from the droplets. Our results highlight the importance of conformationally restricted local structures within spacer regions, separating uniformly spaced tyrosine stickers of the CTD heptads, which are required for CTD phase-separation.

Tips and Tricks in the Modeling of Supramolecular Peptide Assemblies

Supramolecular peptide assemblies (SPAs) hold promise as materials for nanotechnology and biomedicine. Although their investigation often entails adapting experimental techniques from their protein counterparts, SPAs are fundamentally distinct from proteins, posing unique challenges for their study. Computational methods have emerged as indispensable tools for gaining deeper insights into SPA structures at the molecular level, surpassing the limitations of experimental techniques, and as screening tools to reduce the experimental search space. However, computational studies have grappled with issues stemming from the absence of standardized procedures and relevant crystal structures. Fundamental disparities between SPAs and protein simulations, such as the absence of experimentally validated initial structures and the importance of the simulation size, number of molecules, and concentration, have compounded these challenges. Understanding the roles of various parameters and the capabilities of different models and simulation setups remains an ongoing endeavor. In this review, we aim to provide readers with guidance on the parameters to consider when conducting SPA simulations, elucidating their potential impact on outcomes and validity.

Assessing the Martini 3 protein model: A review of its path and potential

Coarse-grained (CG) protein models have become indispensable tools for studying many biological protein details, from conformational dynamics to the organization of protein macro-complexes, and even the interaction of proteins with other molecules. The Martini force field is one of the most widely used CG models for bio-molecular simulations, partly because of the enormous success of its protein model. With the recent release of a new and improved version of the Martini force field - Martini 3 - a new iteration of its protein model was also made available. The Martini 3 protein force field is an evolution of its Martini 2 counterpart, aimed at improving many of the shortcomings that had been previously identified. In this mini-review, we first provide a general overview of the model and then focus on the successful advances made in the short time since its release, many of which would not have been possible before. Furthermore, we discuss reported limitations, potential directions for model improvement and comment on what the likely future development and application avenues are.

Effect of terahertz waves on the aggregation behavior of neurotransmitters

Understanding the dynamics of neurotransmitters is crucial for unraveling synaptic transmission mechanisms in neuroscience. In this study, we investigated the impact of terahertz (THz) waves on the aggregation of four common neurotransmitters through all-atom molecular dynamics (MD) simulations. The simulations revealed enhanced nicotine (NCT) aggregation under 11.05 and 21.44 THz, with a minimal effect at 42.55 THz. Structural analysis further indicated strengthened intermolecular interactions and weakened hydration effects under specific THz stimulation. In addition, enhanced aggregation was observed at stronger field strengths, particularly at 21.44 THz. Furthermore, similar investigations on epinephrine (EPI), 5-hydroxytryptamine (5-HT), and γ-aminobutyric acid (GABA) corroborated these findings. Notably, EPI showed increased aggregation at 19.05 THz, emphasizing the influence of vibrational modes on aggregation. However, 5-HT and GABA, with charged or hydrophilic functional groups, exhibited minimal aggregation under THz stimulation. The present study sheds some light on neurotransmitter responses to THz waves, offering implications for neuroscience and interdisciplinary applications.

Martinoid: the peptoid martini force field

Many exciting innovations have been made in the development of assembling peptoid materials. Typically, these have utilised large oligomeric sequences, though elsewhere the study of peptide self-assembly has yielded numerous examples of assemblers below 6-8 residues in length, evidencing that minimal peptoid assemblers are not only feasible but expected. A productive means of discovering such materials is through the application of in silico screening methods, which often benefit from the use of coarse-grained molecular dynamics (CG-MD) simulations. At the current level of development, CG models for peptoids are insufficient and we have been motivated to develop a Martini forcefield compatible peptoid model. A dual bottom-up and top-down parameterisation approach has been adopted, in keeping with the Martini parameterisation methodology, targeting the reproduction of atomistic MD dynamics and trends in experimentally obtained log D7.4 partition coefficients, respectively. This work has yielded valuable insights into the practicalities of parameterising peptoid monomers. Additionally, we demonstrate that our model can reproduce the experimental observations of two very different peptoid assembly systems, namely peptoid nanosheets and minimal tripeptoid assembly. Further we can simulate the peptoid helix secondary structure relevant for antimicrobial sequences. To be of maximum usefulness to the peptoid research community, we have developed freely available code to generate all requisite simulation files for the application of this model with Gromacs MD software.

Martini on the Rocks: Can a Coarse-Grained Force Field Model Crystals?

Computational chemistry is an important tool in numerous scientific disciplines, including drug discovery and structural biology. Coarse-grained models offer simple representations of molecular systems that enable simulations of large-scale systems. Because there has been an increase in the adoption of such models for simulations of biomolecular systems, critical evaluation is warranted. Here, the stability of the amyloid peptide and organic crystals is evaluated using the Martini 3 coarse-grained force field. The crystals change shape drastically during the simulations. Radial distribution functions show that the distance between backbone beads in β-sheets increases by ∼1 Å, breaking the crystals. The melting points of organic compounds are much too low in the Martini force field. This suggests that Martini 3 lacks the specific interactions needed to accurately simulate peptides or organic crystals without imposing artificial restraints. The problems may be exacerbated by the use of the 12-6 potential, suggesting that a softer potential could improve this model for crystal simulations.

Multiscale Simulations to Discover Self-Assembled Oligopeptides: A Benchmarking Study

Peptide self-assembly is critical for biomedical and material discovery and production. While it is costly to experimentally test every possible peptide design, computational assessment provides an affordable solution to evaluate many designs and prioritize synthesis and characterization. Following a theoretical investigation, we present a systematic analysis of all-atom and coarse-grained simulations to predict peptide self-assembly. Benchmarking studies of two model dipeptides allow us to assess the impacts of intrinsic properties (such as amino acids and terminal modifications) and external environment (such as salinity) on the simulated aggregation. Further examination of 20 oligopeptides containing two to five amino acids shows good agreement among our theory, simulations, and prior experimental observations. The success rate of our prediction is 90%. Therefore, our theory, simulation, and analysis can be useful to identify peptide designs that can self-assemble and predict the potential nanostructures. These findings lay the ground for future virtual screening of peptide-assembled nanostructures and computer-aided biologics design.

Assessment of the MARTINI 3 Performance for Short Peptide Self-Assembly

The coarse-grained MARTINI force field, initially developed for membranes, has proven to be an exceptional tool for investigating supramolecular peptide assemblies. Over the years, the force field underwent refinements to enhance accuracy, enabling, for example, the reproduction of protein-ligand interactions and constant pH behavior. However, these protein-focused improvements seem to have compromised its ability to model short peptide self-assembly. In this study, we assess the performance of MARTINI 3 in reproducing peptide self-assembly using the well-established diphenylalanine (FF) as our test case. Unlike its success in version 2.1, FF does not even exhibit aggregation in version 3. By systematically exploring parameters for the aromatic side chains and charged backbone beads, we established a parameter set that effectively reproduces tube formation. Remarkably, these parameter adjustments also replicate the self-assembly of other di- and tripeptides and coassemblies. Furthermore, our analysis uncovers pivotal insights for enhancing the performance of MARTINI in modeling short peptide self-assembly. Specifically, we identify issues stemming from overestimated hydrophilicity arising from charged termini and disruptions in π-stacking interactions due to insufficient planarity in aromatic groups and a discrepancy in intermolecular distances between this and backbone-backbone interactions. This investigation demonstrates that strategic modifications can harness the advancements offered by MARTINI 3 for the realm of short peptide self-assembly.

Efficient prediction of peptide self-assembly through sequential and graphical encoding

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.

Diverging conformations guide dipeptide self-assembly into crystals or hydrogels

The prediction of dipeptide assembly into crystals or gels is challenging. This work reveals the diverging conformational landscape that guides self-organization towards different outcomes. In silico and experimental data enabled deciphering of the electronic circular dichroism (ECD) spectra of self-assembling dipeptides to reveal folded or extended conformers as key players.