Protein-membrane interactions are increasingly recognized as key factors in amyloid-related pathologies, where amyloid proteins associate with lipid bilayers in ways that influence aggregation pathways and cytotoxic effects. Molecular dynamics (MD) simulations have become essential tools for investigating the molecular mechanisms underlying these interactions. This chapter presents advanced methodologies for simulating amyloid-membrane systems, using a hexameric amyloid-β peptide barrel inserted into a neuronal membrane as an example, and provides practical guidance for system setup, simulation execution, and trajectory analysis.

Protein-membrane interactions are vital to Alzheimer's pathogenesis, as lipid environments modulate both the aggregation and toxicity of amyloid-β (Aβ) peptides. Using atomistic molecular dynamics simulations, this study investigates the stability of an NMR-derived hexameric Aβ42 β-barrel in aqueous solution, a fluid 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer, and a complex neuronal membrane. While the hexamer is unstable and conformationally heterogeneous in water, lipid environments provide essential structural reinforcement. Notably, the multicomponent neuronal membrane offers superior stabilization compared to POPC, supporting the β-barrel in a stable transmembrane conformation. This superior stability is driven by the rigid scaffolding of the liquid-ordered phase alongside specific electrostatic anchoring between the ethanolamine headgroups of the POPE component and the acidic Aβ42 residues E22/D23. We characterize a reciprocal relationship where the membrane stabilizes the β-barrel architecture, while the peptide induces localized lipid disorder and flip-flop translocation. Our findings demonstrate how membrane complexity and phase behavior dictate the stability of toxic Aβ42 oligomers, offering key insights into the membrane-mediated mechanisms of neurotoxicity.

Platinum-based (Pt-based) compounds remain a cornerstone of chemotherapy, yet their clinical use is limited mainly due to poor tumor specificity and systemic toxicities. Fatty acid conjugation has emerged as a promising strategy to overcome the limitations of conventional platinum drugs by enhancing lipophilicity, improving cellular uptake, and potentially acting as prodrugs with altered physicochemical properties and binding kinetics to biomolecular targets. The covalent conjugation of lipophilic fatty acids also improves the compatibility of Pt-based compounds with lipid-based delivery systems, facilitating their incorporation. In this study, we employed atomistic molecular dynamics (MD) simulations to investigate the interactions between a series of Pt-based compounds, including cisplatin and fatty acid-conjugated analogs (CapryP, ArP, SteariP, ElaidP, and OleP), and biologically relevant phospholipids (DOPC, DSPE, and DPPG). Simulations revealed the spontaneous self-assembly of lipid-drug mixtures into micelle-like aggregates, driven by hydrophobic interactions and modulated by the chemical structure of the conjugated moieties. Cluster analysis demonstrated variation in aggregation dynamics among the compounds, with hydrophobic chain length and unsaturation degree influencing the rate and stability of complex formation. These findings provide insights at the molecular-level, shedding light into the molecular attributes that govern the incorporation of fatty acid-Pt-based conjugates into lipid assemblies, highlighting the potential of structural modifications to enhance their delivery within lipid-based systems.

The human guanylate-binding proteins 1 and 5 (hGBP1/5) are key players in innate immunity, vital for defending against intracellular pathogens and mediating membrane-associated immune responses. These protein functions are closely linked to their structural dynamics, making a detailed understanding of the conformational behavior imperative. The closed conformation of hGBP1 is well-characterized through crystallography, but the structural basis for its transition to an active, open state remains less understood. This study uses all-atom and coarse-grained molecular dynamics simulations to investigate the stability and motions of extended monomeric and dimeric hGBP1 and hGBP5 with and without GTP bound. Results reveal that dimers exhibit greater stability than monomers, primarily due to extensive stalk interactions that facilitate a structural crossing of the protomers at the interface of the GTPase and middle domains. This arrangement aligns the middle and effector domains parallel to one another, further stabilizing the dimeric state through the formation of coiled-coil structures supported by salt bridges and hydrophobic contacts. Notably, monomers of both hGBP1 and hGBP5 can revert to a closed state stabilized by a network of salt bridges between the effector domain and the surrounding domains. In hGBP5, this transition is further facilitated by the geranylgeranyl group, which more effectively reaches and buries itself within a hydrophobic pocket of the GTPase domain compared to hGBP1. These findings highlight key factors affecting the stability of hGBP1/5 monomers and dimers, providing insights into their activation mechanisms that are relevant for their role in innate immunity.

Abstract Glycosaminoglycans (GAGs) are polyanionic polysaccharides that co-localize with amyloid-β (Aβ) deposits in Alzheimer’s disease, yet their mechanistic contribution to Aβ aggregation remains unclear. Here, we show that GAGs function as pH-responsive electrostatic scaffolds that selectively accelerate Aβ(1–42) aggregation under mildly acidic, endosomal conditions but not at neutral extracellular pH. Combining experimental and computational approaches, we identify protonated N-terminal histidines as key determinants of GAG binding. Weak interactions between GAGs and the charged Nterminal region of Aβ promote conformational rearrangements that bring peptides into proximity and expose adjacent hydrophobic aggregation-prone segments, thereby facilitating peptide clustering. Kinetic analyses reveal that aggregation is enhanced in a way consistent with an apparent increase in effective peptide concentration, accelerating nucleation without altering the dominant aggregation pathway. Systematic variation of GAG chain length and sulfation level further demonstrates that aggregation enhancement requires a threshold degree of multivalency, consistent with a clustering-driven mechanism. Together, these findings establish a framework in which pH-dependent electrostatic interactions with GAGs act as molecular triggers of amyloid nucleation, providing insight into how cellular microenvironments regulate the earliest stages of Alzheimer’s disease pathology.

The amyloid-β peptide (Aβ), implicated in Alzheimer's disease, exhibits significant polymorphism. At the monomer level, Aβ can adopt disordered, helical, and β-hairpin structures, influenced by environmental conditions. Both oligomeric and fibrillar states, characterized by the prevalence of β-sheets, are polymorphic in the arrangement of β-strands. This chameleon-like behavior arises from Aβ's unique sequence and relatively flat energy landscape, which facilitates aggregation and may contribute to the prevalence of Alzheimer's disease, while also enabling disaggregation, thus slowing disease progression. In contrast, Creutzfeldt-Jakob disease, which is much rarer, progresses far more rapidly, likely due to the steeper energy landscape of the prion protein.

Understanding the conformational dynamics of biomolecules requires methods that go beyond structural sampling and provide a quantitative description of thermodynamics and kinetics. For intrinsically disordered proteins (IDPs), energy landscape characterization is particularly crucial to unravel their complex conformational behavior. Here, we present a comprehensive protocol for analyzing molecular dynamics (MD) simulations in terms of energy landscapes, metastable states, and transition pathways. Our approach is based on the distribution of reciprocal interatomic distances (DRID) for dimensionality reduction, followed by clustering and kinetic modeling. Free energy surfaces and transition state barriers are computed directly from the simulation data and visualized using disconnectivity graphs. The method integrates two Python packages, DRIDmetric and freenet, with standard energy landscape tools based on kinetic transition networks, including PATHSAMPLE and disconnectionDPS. We demonstrate this workflow for simulations of the intrinsically disordered, aggregation-prone Alzheimer's amyloid-β peptide in physiologically relevant environments. This modular framework offers a robust and interpretable way to extract thermodynamic and kinetic insights from MD data and is especially valuable for characterizing the diverse conformational states of IDPs.

The outbreak of the COVID-19 pandemic has resulted in not <7.1million deaths globally as of December 2024. Many new variants of concern have continued to emerge since the initial outbreak of the original SARS-CoV-2 virus traceable to the Wuhan strain (Wuhan-Hu-1). In this work, the therapeutic potentials of four new polyherbal dietary preparations – VIVE (five plants), FORTE1(fortified VIVE), COMBI-5 (five spices) and MOK (Moringa seed) as well as four individual ethnomedicinal plants were investigated. Computational screening revealed chemical structures capable of establishing moderate to strong interaction with SARS-CoV-2′s main protease enzyme, while in vitro screening against the viral protease clearly established inhibitory potencies. The individual plant extracts making up VIVE and FORTE1 showed mild (494.9 ± 19.6 µg/ml) to moderate (21.5 ± 1.1 µg/ml) inhibitory activity against the viral enzyme in vitro; highest activity was obtained in the polyherbal VIVE preparation (17.3 ± 1.4 µg/ml). The MOK exerted total inhibition – 100 % (IC50 -3.6 ± 0.9) of the viral enzyme while COMBI-5 produced an inhibition of 95 %(IC50 - 0.9 ± 0.1). These results revealed the potential of specialized metabolites within these widely consumed dietary herbal products for the management of COVID-19 and related viral threats.

Cannabidiol (CBD), a non-psychoactive phytocannabinoid from Cannabis sativa, has gained significant attention due to its diverse therapeutic properties, including anti-inflammatory, antioxidant, and anxiolytic effects. However, its clinical application is hindered by poor water solubility, which limits its bioavailability. The aim of this study is to deepen our understanding of the conformational properties of CBD, and investigate how these properties affect its solubility. Using Density Functional Theory (DFT) calculations, we analyzed the axial and equatorial positions of substituents on the limonene ring and the arrangement of both hydroxyl groups. Our findings indicate that the most stable conformation of CBD involves diequatorial substitution on the limonene ring, stabilized by specific –OH⋯π hydrogen bonding interactions. All-atom Molecular Dynamics (MD) simulations in an aqueous environment revealed that while single CBD molecules maintain their conformation, multiple CBD molecules tend to cluster. These insights provide a comprehensive understanding of the molecular interactions that underlies CBD’s low aqueous solubility and suggests potential strategies for enhancing its bioavailability, which could optimize its therapeutic potential.

Plastics, especially polyethylene terephthalate (PET), are vital in modern life, with global production exceeding 400 million tons annually. This extensive use has led to significant plastic waste pollution, highlighting the need for effective recycling strategies. PET, one of the most recycled plastics, is a prime candidate for degradation into its original monomers through engineered PET hydrolases - enzymes with industrial potential. While previous engineering efforts have mainly focused on enhancing thermostability and catalytic efficiency, the crucial aspect of enzyme adsorption to PET surfaces has received less attention. This review specifically addresses the mechanisms of enzyme adsorption, detailing relevant experimental methods and simulation techniques while emphasizing the potential for engineering more effective PET hydrolases.

This review highlights cutting-edge techniques for modeling peptide-protein interactions and advancing computer-aided peptide-drug design. We examine significant progress in generating peptide poses through docking and artificial intelligence (AI), assessing peptide flexibility via enhanced molecular dynamics simulations, and analyzing binding interactions through free energy calculations. Additionally, we discuss how these insights can inform the rational design of therapeutic peptides by utilizing free energy metrics and strategic modifications to enhance their binding affinity and therapeutic potential. Looking forward, further integrating AI will be crucial for optimizing peptide design and enhancing drug development efforts.

Ion-protein interactions regulate biological processes and are the basis of key strategies of modulating protein phase diagrams and stability in drug development. Here, we report the mechanisms by which H-bonds and electrostatic interactions in ion-protein systems determine phase separation and amyloid formation. Using microscopy, small-angle X-ray scattering, circular dichroism and atomistic molecular dynamics (MD) simulations, we found that anions specifically interacting with insulin induced phase separation by neutralising the protein charge and forming H-bond bridges between insulin molecules. The same interaction was responsible for an enhanced insulin conformational stability and resistance to oligomerisation. Under aggregation conditions, the anion-protein interaction translated into the activation of a coalescence process, leading to amyloid-like microparticles. This reaction is alternative to conformationally-driven pathways, giving rise to elongated amyloid-like fibrils and occurs in the absence of preferential ion-protein binding. Our findings depict a unifying scenario in which common interactions dictated both phase separation at low temperatures and the occurrence of pronounced heterogeneity in the amyloid morphology at high temperatures, similar to what has previously been reported for protein crystal growth.

Posttranslational modifications of Y‐box binding protein (YB)‐1 are the prerequisite for its very different protein functions. Here, we investigate the underlying molecular mechanisms of YB‐1 guanidinylation and link increased serum urea levels as well as the activity of glycine amidinotransferase (GATM) with guanidinylation. Computer simulations show changes in stability and conformation of the YB‐1 protein induced by these modifications. In particular, the secondary structure of the doubly guanidinylated YB‐1 (YB‐1‐2G) shows a reduced tendency to form β ‐sheets, and the modified cold shock domain is more exposed to the solvent. Protein–protein docking techniques in conjunction with molecular dynamics simulations confirm the binding between YB‐1 and its receptor Notch‐3 at EGF domains 17–24 but show no significant differences in the binding behavior of YB‐1 and YB‐1‐2G. This is confirmed in two different types of receptor‐ligand binding assays. In addition, we demonstrate for the first time a high‐affinity binding of YB‐1 to another ligand binding site on the Notch‐3 receptor, thereby achieving effective displacement of the canonical ligand Jagged. In conclusion, we identified molecular processes that lead to the guanidinylation of YB‐1 and revealed their effects on the structure and binding to receptor Notch‐3.

Amyloid proteins are characterized by their tendency to aggregate into amyloid fibrils, which are often associated with devastating diseases. Aggregation pathways typically involve unfolding or misfolding of monomeric proteins and formation of transient oligomers and protofibrils before the final aggregation product is formed. The conformational dynamics and polymorphic and volatile nature of these aggregation intermediates make their characterization by experimental techniques alone insufficient and also require computational approaches. Over the past 25 years, the size of simulated amyloid aggregation systems and the length of these simulations have increased significantly. These advances are discussed here. The review includes simulation approaches that model the aggregating peptides or proteins at both the all-atom and coarse-grained levels, use molecular dynamics simulations or Monte Carlo sampling to simulate the conformational changes, and present results for various amyloid peptides and proteins ranging from Lys-Phe-Phe-Glu (KFFE) as the smallest system to A beta as an intermediate-sized peptide to alpha-synuclein. The presentation of the history of amyloid aggregation simulations concludes with a discussion of where the future of these simulations may lie. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods

Peptide fibrillization is crucial in biological processes such as amyloid-related diseases and hormone storage, involving complex transitions between folded, unfolded, and aggregated states. We here employ light to induce reversible transitions between aggregated and nonaggregated states of a peptide, linked to the parathyroid hormone (PTH). The artificial light-switch 3-{[(4-aminomethyl)phenyl]diazenyl}benzoic acid (AMPB) is embedded into a segment of PTH, the peptide PTH25–37, to control aggregation, revealing position-dependent effects. Through in silico design, synthesis, and experimental validation of 11 novel PTH25–37-derived peptides, we predict and confirm the amyloid-forming capabilities of the AMPB-containing peptides. Quantum-chemical studies shed light on the photoswitching mechanism. Solid-state NMR studies suggest that β-strands are aligned parallel in fibrils of PTH25–37, while in one of the AMPB-containing peptides, β-strands are antiparallel. Simulations further highlight the significance of π–π interactions in the latter. This multifaceted approach enabled the identification of a peptide that can undergo repeated phototriggered transitions between fibrillated and defibrillated states, as demonstrated by different spectroscopic techniques. With this strategy, we unlock the potential to manipulate PTH to reversibly switch between active and inactive aggregated states, representing the first observation of a photostimulus-responsive hormone.

The aggregation of amyloid-β (Aβ) peptides, particularly Aβ1-42, plays a key role in Alzheimer's disease pathogenesis. In this study, we investigate how dimerisation transforms the free energy surface (FES) of the Aβ1-42 monomer when it interacts with another Aβ1-42 peptide. We find that the monomer FES is a structurally inverted funnel with a disordered state at the global minimum. However, in the presence of a second Aβ1-42 peptide, the landscape becomes a folding funnel, leading to a β-hairpin state. Using first passage time analysis, we analyse the pathway for the transition from disordered to the β-hairpin state, which highlights the initial formation of a D23-K28 salt bridge as the driving force, together with hydrophobic contacts.

Plastic-degrading enzymes facilitate the biocatalytic recycling of poly(ethylene terephthalate) (PET), a significant synthetic polymer, and substantial progress has been made in utilizing PET hydrolases for industrial applications. To fully exploit the potential of these enzymes, a deeper mechanistic understanding followed by targeted protein engineering is essential. Through advanced molecular dynamics simulations and free energy analysis methods, we elucidated the complete pathway from the initial binding of two PET hydrolases-the thermophilic leaf-branch compost cutinase (LCC) and polyester hydrolase 1 (PES-H1)-to an amorphous PET substrate, ultimately leading to a PET chain entering the active site in a hydrolyzable conformation. Our findings indicate that initial PET binding is nonspecific and driven by polar and hydrophobic interactions. We demonstrate that the subsequent entry of PET into the active site can occur via one of three key pathways, identifying barriers related to both PET-PET and PET-enzyme interactions, as well as specific residues highlighted through in silico and in vitro mutagenesis. These insights not only enhance our understanding of the mechanisms underlying PET degradation and facilitate the development of targeted enzyme enhancement strategies but also provide a novel framework applicable to enzyme studies across various disciplines.

Plastic-degrading enzymes, particularly poly(ethylene terephthalate) (PET) hydrolases, have garnered significant attention in recent years as potential eco-friendly solutions for recycling plastic waste. However, understanding of their PET-degrading activity and influencing factors remains incomplete, impeding the development of uniform approaches for enhancing PET hydrolases for industrial applications. A key aspect of PET hydrolase engineering is optimizing the PET-hydrolysis reaction by lowering the associated free energy barrier. However, inconsistent findings have complicated these efforts. Therefore, our goal is to elucidate various aspects of enzymatic PET degradation by means of quantum mechanics/molecular mechanics (QM/MM) reaction simulations and analysis, focusing on the initial reaction step, acylation, in two thermophilic PET hydrolases, LCC and PES-H1, along with their highly active variants, LCCIG and PES-H1FY. Our findings highlight the impact of semiempirical QM methods on proton transfer energies, affecting the distinction between a two-step reaction involving a metastable tetrahedral intermediate and a one-step reaction. Moreover, we uncovered a concerted conformational change involving the orientation of the PET benzene ring, altering its interaction with the side-chain of the "wobbling" tryptophan from T-stacking to parallel π-π interactions, a phenomenon overlooked in prior research. Our study thus enhances the understanding of the acylation mechanism of PET hydrolases, in particular by characterizing it for the first time for the promising PES-H1FY using QM/MM simulations. It also provides insights into selecting a suitable QM method and a reaction coordinate, valuable for future studies on PET degradation processes.

Machine learning-guided optimization has become a driving force for recent improvements in protein engineering. In addition, new protein language models are learning the grammar of evolutionarily occurring sequences at large scales. This work combines both approaches to make predictions about mutational effects that support protein engineering. To this end, an easy-to-use software tool called TransMEP is developed using transfer learning by feature extraction with Gaussian process regression. A large collection of datasets is used to evaluate its quality, which scales with the size of the training set, and to show its improvements over previous fine-tuning approaches. Wet-lab studies are simulated to evaluate the use of mutation effect prediction models for protein engineering. This showed that TransMEP finds the best performing mutants with a limited study budget by considering the trade-off between exploration and exploitation. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

Among the various factors controlling the amyloid aggregation process, the influences of ions on the aggregation rate and the resulting structures are important aspects to consider, which can be studied by molecular simulations. There is a wide variety of protein force fields and ion models, raising the question of which model to use in such studies. To address this question, we perform molecular dynamics simulations of Aβ16-22 , a fragment of the Alzheimer's amyloid β peptide, using different protein force fields, AMBER99SB-disp (A99-d) and CHARMM36m (C36m), and different ion parameters. The influences of NaCl and CaCl2 at various concentrations are studied and compared with the systems without the addition of ions. Our results indicate a sensitivity of the peptide-ion interactions to the different ion models. In particular, we observe a strong binding of Ca2+ to residue E22 with C36m and also with the Åqvist ion model used together with A99-d, which slightly affects the monomeric Aβ16-22 structures and the aggregation rate, but significantly affects the oligomer structures formed in the aggregation simulations. For example, at high Ca2+ concentrations, there was a switch from an antiparallel to a parallel β-sheet. Such ionic influences are of biological relevance because local ion concentrations can change in vivo and could help explain the polymorphism of amyloid fibrils.