Multiscale Molecular Modeling Research Articles

Multiscale molecular modeling of chromatin with MultiMM: From nucleosomes to the whole genome

Motivation: We present a user-friendly 3D chromatin simulation model for the human genome based on OpenMM, addressing the challenges posed by existing models with use-specific implementations. Our approach employs a multi-scale energy minimization strategy, capturing chromatin's hierarchical structure. Initiating with a Hilbert curve-based structure, users can input files specifying nucleosome positioning, loops, compartments, or subcompartments.Results: The model utilizes an energy minimization approach with a large choice of numerical integrators, providing the entire genome's structure within minutes. Output files include the generated structures for each chromosome, offering a versatile and accessible tool for chromatin simulation in bioinformatics studies. Furthermore, MultiMM is capable of producing nucleosome-resolution structures by making simplistic geometric assumptions about the structure and the density of nucleosomes on the DNA.Code availability: Open-source software and the manual are freely available on https://github.com/SFGLab/MultiMM or via pip https://pypi.org/project/MultiMM/.

Multiscale Modeling Approach to Understand Mechanism of Deposit Control by Sulfonate-Based Lubricant Detergents.

Protecting the material surfaces from deposits and insoluble sludge particles extends the engine life and reduces waste. Lubricant detergents in engine oils are essential additive technologies that prevent deposit formation in internal combustion engines. In this study, the effect of sulfonate detergent on deposit formation in a passenger car engine is investigated with experimental and multiscale molecular modeling methods to present a unified approach. First principles density-functional theory calculations, statistical sampling methods, all-atom molecular dynamics simulations, and coarse-grained simulations are examined to elucidate deposit control mechanism of sulfonate detergents. Analysis of the results reveals that sludge particles in the drain oil are similar in structure to piston deposits, and they might be the precursors of the piston deposits. Main factor for controlling the sludge particle deposition is the prevention of their colloidal aggregation at the microscale in the base oil matrix. Aggregation can be mitigated by the intercalation of detergent polar groups between the particles. This is followed by the extension of hydrophobic tails into the oil phase, which decreases further aggregation via formation of a repulsive layer.

Identification of dual-target isoxazolidine-isatin hybrids with antidiabetic potential: Design, synthesis, in vitro and multiscale molecular modeling approaches

In the development of novel antidiabetic agents, a novel series of isoxazolidine-isatin hybrids were designed, synthesized, and evaluated as dual α‐amylase and α-glucosidase inhibitors. The precise structures of the synthesized scaffolds were characterized using different spectroscopic techniques and elemental analysis. The obtained results were compared to those of the reference drug, acarbose (IC50 = 296.6 ± 0.825 μM for α‐amylase & IC50 = 780.4 ± 0.346 μM for α-glucosidase). Among the title compounds, 5d exhibited impressive α-amylase and α-glucosidase inhibitory activity with IC50 values of 30.39 ± 1.52 μM and 65.1 ± 3.11 μM, respectively, followed by 5h (IC50 = 46.65 ± 2.3 μM; IC50 = 85.16 ± 4.25 μM) and 5f (IC50 = 55.71 ± 2.78 μM; IC50 = 106.77 ± 5.31 μM). Mechanistic studies revealed that the most potent derivative 5d bearing the chloro substituent attached to the oxoindolin-3-ylidene core, and acarbose, are a competitive inhibitors of α-amylase and α-glucosidase, respectively. Structure activity relationship (SAR) was examined to guide further structural optimization of the most appropriate substituent(s). Moreover, drug-likeness qualities and ADMET prediction of the most active analogue, 5d was also performed. Subsequently, 5d was subjected to molecular docking and dynamic simulation during the progression of 120 ns analysis to check the essential ligand-receptor patterns, and to estimate its stability. In silico studies were found in good agreement with the in vitro enzymatic inhibitions results. In conclusion, we demonstrated that most potent compound 5d could be exploited as dual potential inhibitor of α-amylase and α-glucosidase for possible management of diabetes.

All-trans retinoic acid acts as a dual-purpose inhibitor of SARS-CoV-2 infection and inflammation

Coronavirus disease 2019 (COVID-19) was an epidemic that effected human health caused by SARS-CoV-2 infection. All-trans retinoic acid (ATRA) has anti-inflammatory capability. In this article, we evaluated the effectiveness and revealed the molecular mechanism of ATRA for treating SARS-CoV-2 using deep learning, in vitro studies, multi-scale molecular modeling, and network pharmacology. The DeepDTA model suggested that ATRA would be effective against COVID-19. In vitro studies confirmed the antiviral activity of ATRA. Subsequently, multi-scale molecular modeling indicated that ATRA could binding to angiotensin converting enzyme 2 (ACE2), 3C-like protease (3CLpro), RNA dependent RNA polymerase (RdRp), helicase, and 3′-to-5′ exonuclease by non-covalent interactions. Additionally, network pharmacology suggested that ATRA alleviated inflammatory response by regulating the IL-17 signaling pathway and binding with TNF, PTGS2, and MAPK1 directly. In summary, our findings provide the first evidence that ATRA suppresses the entry and replication of SARS-CoV-2, and regulates inflammatory response of host cells.

From One-Dimensional Disordered Racemate to Ordered Racemic Conglomerates through Metal-Coordination-Driven Self-Assembly at the Liquid-Solid Interface.

In recent years, there has been significant focus on investigating and controlling chiral self-assembly, specifically in the context of enantiomeric separation. This study explores the self-assembly behavior of 4-dodecyl-3,6-di(2-pyridyl)pyridazine (DPP-C12) at the interface between heptanoic acid (HA) and highly oriented pyrolytic graphite (HOPG) using a combination of scanning tunneling microscopy (STM) and multiscale molecular modeling. The self-assembled monolayer structure formed by DPP-C12 is periodic in one direction, but aperiodic in the direction orthogonal to it. These structures resemble 1D disordered racemic compounds. Upon introducing palladium [Pd(II)] ions, complexing with DPP-C12, these 1D disordered racemic compounds spontaneously transform into 2D racemic conglomerates, which is rationalized with the assistance of force-field simulations. Our findings provide insights into the regulation of two-dimensional chirality.

Hydration structure and dynamics, ultraviolet–visible and fluorescence spectra of caffeine in ambient liquid water. A combined classical molecular dynamics and quantum chemical study

The hydration structure and related dynamics of caffeine diluted in ambient liquid water have been extensively studied by performing classical molecular dynamics simulations, using our previously developed potential model of caffeine and the TIP4P/2005 water model. The results obtained have revealed that the first hydration shell of caffeine contains on average 56 water molecules. Hydrated caffeine forms in total 3 hydrogen bonds with its neighbor water molecules, with the Ocaffeine … Hwater hydrogen bonds exhibiting similar lifetimes with the ones corresponding to Owater … Hwater hydrogen bonds in liquid water. The self-diffusion coefficient of caffeine has been found to be four times lower than the corresponding value for water, being also in agreement with recent experimental measurements. The presence of water molecules inside the solvation shell of caffeine changes significantly their low-frequency intermolecular vibrations, as reflected on the calculated atomic velocity time correlation functions and corresponding spectral densities. Using the estimated average intermolecular structure of the first hydration shell of caffeine, the molecular cluster caffeine@W56 was optimized via quantum chemical calculations and subsequently the time-dependent density functional theory was used in order to predict the ultraviolet–visible and fluorescence spectra of hydrated caffeine. The results obtained are in agreement with recent experimental studies, which have proposed that such spectroscopic measurements can be used for the direct determination of alkaloids in aqueous extracts of natural products. In this framework, multi-scale molecular modelling providing accurate predictions of experimental data could also be a very useful tool, linking theoretical physical chemistry with analytical chemistry applications.

Predicting Mutation-Induced Changes in the Electronic Properties of Photosynthetic Proteins from First Principles: The Fenna-Matthews-Olson Complex Example.

Multiscale molecular modeling is utilized to predict optical absorption and circular dichroism spectra of two single-point mutants of the Fenna-Matthews-Olson photosynthetic pigment-protein complex. The modeling approach combines classical molecular dynamics simulations with structural refinement of photosynthetic pigments and calculations of their excited states in a polarizable protein environment. The only experimental input to the modeling protocol is the X-ray structure of the wild-type protein. The first-principles modeling reproduces changes in the experimental optical spectra of the considered mutants, Y16F and Q198V. Interestingly, the Q198V mutation has a negligible effect on the electronic properties of the targeted bacteriochlorophyll a pigment. Instead, the electronic properties of several other pigments respond to this mutation. The molecular modeling demonstrates that a single-point mutation can induce long-range effects on the protein structure, while extensive structural changes near a pigment do not necessarily lead to significant changes in the electronic properties of that pigment.

Protein-ligand interactions from a quantum fragmentation perspective: The case of the SARS-CoV-2 main protease interacting with α-ketoamide inhibitors.

We present a hybrid, multi-method, computational scheme for protein/ligand systems well suited to be used on modern and forthcoming massively parallel computing systems. The scheme relies on a multi-scale polarizable molecular modeling, approach to perform molecular dynamics simulations, and on an efficient Density Functional Theory (DFT) linear scaling method to post-process simulation snapshots. We use this scheme to investigate recent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2 virus. We assessed the reliability and the coherence of the hybrid scheme, in particular, by checking the ability of MM and DFT to reproduce results from high-end abinitio computations regarding such inhibitors. The DFT approach enables an a posteriori fragmentation of the system and an investigation into the strength of interaction among identified fragment pairs. We show the necessity of accounting for a large set of plausible protease/inhibitor conformations to generate reliable interaction data. Finally, we point out ways to further improve α-ketoamide inhibitors to more strongly interact with particular protease domains neighboring the active site.

Towards a Rational Design of Antibody-Recruiting Molecules through a Computational Microscopy View of their Interactions with the Target Antibody.

Antibody recruiting molecules (ARMs) are an innovative class of chimeric molecules, consisting of an antibody-binding ligand (ABL) and a target-binding ligand (TBL). ARMs mediate ternary complex formation between a target cell of interest for elimination and endogenous antibodies that are present in human serum. Clustering of fragment crystallizable (Fc) domains on the surface of antibody-bound cells mediate destruction of the target cell by innate immune effector mechanisms. ARMs are typically designed by conjugating small molecule haptens to a (macro)molecular scaffold, without considering the structure of the respective anti-hapten antibody. Here we report on a computational molecular modeling methodology that allows for studying the close contacts between ARMs and the anti-hapten antibody, considering (1) the spacer length between ABL and TBL; (2) the number of ABL and TBL, and (3) the molecular scaffold onto which these are positioned. Our model predicts the difference in binding modes of the ternary complex and predicts which ARMs are optimal recruiters. Avidity measurements of the ARM-antibody complex and ARM-mediated antibody recruitment to cell surfaces in vitro confirmed these computational modeling predictions. This kind of multiscale molecular modelling holds potential for design of drug molecules that rely on antibody binding for their mechanism of action.

Resolving a guanine-quadruplex structure in the SARS-CoV-2 genome through circular dichroism and multiscale molecular modeling.

The genome of SARS-CoV-2 coronavirus is made up of a single-stranded RNA fragment that can assume a specific secondary structure, whose stability can influence the virus's ability to reproduce. Recent studies have identified putative guanine quadruplex sequences in SARS-CoV-2 genome fragments that are involved in coding for both structural and non-structural proteins. In this contribution, we focus on a specific G-rich sequence referred to as RG-2, which codes for the non-structural protein 10 (Nsp10) and assumes a guanine-quadruplex (G4) arrangement. We provide the secondary structure of RG-2 G4 at atomistic resolution by molecular modeling and simulation, validated by the superposition of experimental and calculated electronic circular dichroism spectra. Through both experimental and simulation approaches, we have demonstrated that pyridostatin (PDS), a widely recognized G4 binder, can bind to and stabilize RG-2 G4 more strongly than RG-1, another G4 forming sequence that was previously proposed as a potential target for antiviral drug candidates. Overall, this study highlights RG-2 as a valuable target to inhibit the translation and replication of SARS-CoV-2, paving the way towards original therapeutic approaches against emerging RNA viruses.

Mechanism of the Early Catalytic Events in the Collagenolysis by Matrix Metalloproteinase-1.

Metalloproteinase-1 (MMP-1) catalyzed collagen degradation is essential for a wide variety of normal physiological processes, while at the same time contributing to several diseases in humans. Therefore, a comprehensive understanding of this process is of great importance. Although crystallographic and spectroscopic studies provided fundamental information about the structure and function of MMP-1, the precise mechanism of collagen degradation especially considering the complex and flexible structure of the substrate, remains poorly understood. In addition, how the protein environment dynamically reorganizes at the atomic scale into a catalytically active state capable of collagen hydrolysis remains unknown. In this study, we applied experimentally-guided multiscale molecular modeling methods including classical molecular dynamics (MD), well-tempered (WT) classical metadynamics (MetD), combined quantum mechanics/molecular mechanics (QM/MM) MD and QM/MM MetD simulations to explore and characterize the early catalytic events of MMP-1 collagenolysis. Importantly the study provided a complete atomic and dynamic description of the transition from the open to the closed form of the MMP-1•THP complex. Notably, the formation of catalytically active Michaelis complex competent for collagen cleavage was characterized. The study identified the changes in the coordination state of the catalytic zinc(II) associated with the conformational transformation and the formation of catalytically productive ES complex. Our results confirm the essential role of the MMP-1 catalytic domain's α-helices (hA, hB and hC) and the linker region in the transition to the catalytically competent ES complex. Overall, the results provide unique mechanistic insight into the conformational transformations and associated changes in the coordination state of the catalytic zinc(II) that would be important for the design of effective MMP-1 inhibitors.

The evolutionary advantage of an aromatic clamp in plant family 3 glycoside exo-hydrolases

In the barley β-d-glucan glucohydrolase, a glycoside hydrolase family 3 (GH3) enzyme, the Trp286/Trp434 clamp ensures β-d-glucosides binding, which is fundamental for substrate hydrolysis during plant growth and development. We employ mutagenesis, high-resolution X-ray crystallography, and multi-scale molecular modelling methods to examine the binding and conformational behaviour of isomeric β-d-glucosides during substrate-product assisted processive catalysis that operates in GH3 hydrolases. Enzyme kinetics reveals that the W434H mutant retains broad specificity, while W434A behaves as a strict (1,3)-β-d-glucosidase. Investigations of reactant movements on the nanoscale reveal that processivity is sensitive to mutation-specific alterations of the tryptophan clamp. While wild-type and W434H utilise a lateral cavity for glucose displacement and sliding of (1,3)-linked hydrolytic products through the catalytic site without dissociation, consistent with their high hydrolytic rates, W434A does not adopt processive catalysis. Phylogenomic analyses of GH3 hydrolases disclose the evolutionary advantage of the tryptophan clamp that confers broad specificity, high catalytic efficiency, and processivity.

Towards Molecular Understanding of the Functional Role of UbiJ-UbiK2 Complex in Ubiquinone Biosynthesis by Multiscale Molecular Modelling Studies.

Ubiquinone (UQ) is a polyisoprenoid lipid found in the membranes of bacteria and eukaryotes. UQ has important roles, notably in respiratory metabolisms which sustain cellular bioenergetics. Most steps of UQ biosynthesis take place in the cytosol of E. coli within a multiprotein complex called the Ubi metabolon, that contains five enzymes and two accessory proteins, UbiJ and UbiK. The SCP2 domain of UbiJ was proposed to bind the hydrophobic polyisoprenoid tail of UQ biosynthetic intermediates in the Ubi metabolon. How the newly synthesised UQ might be released in the membrane is currently unknown. In this paper, we focused on better understanding the role of the UbiJ-UbiK2 heterotrimer forming part of the metabolon. Given the difficulties to gain functional insights using biophysical techniques, we applied a multiscale molecular modelling approach to study the UbiJ-UbiK2 heterotrimer. Our data show that UbiJ-UbiK2 interacts closely with the membrane and suggests possible pathways to enable the release of UQ into the membrane. This study highlights the UbiJ-UbiK2 complex as the likely interface between the membrane and the enzymes of the Ubi metabolon and supports that the heterotrimer is key to the biosynthesis of UQ8 and its release into the membrane of E. coli.

Modeling Polyzwitterion-Based Drug Delivery Platforms: A Perspective of the Current State-of-the-Art and Beyond.

Drug delivery platforms are anticipated to have biocompatible and bioinert surfaces. PEGylation of drug carriers is the most approved method since it improves water solubility and colloid stability and decreases the drug vehicles’ interactions with blood components. Although this approach extends their biocompatibility, biorecognition mechanisms prevent them from biodistribution and thus efficient drug transfer. Recent studies have shown (poly)zwitterions to be alternatives for PEG with superior biocompatibility. (Poly)zwitterions are super hydrophilic, mainly stimuli-responsive, easy to functionalize and they display an extremely low protein adsorption and long biodistribution time. These unique characteristics make them already promising candidates as drug delivery carriers. Furthermore, since they have highly dense charged groups with opposite signs, (poly)zwitterions are intensely hydrated under physiological conditions. This exceptional hydration potential makes them ideal for the design of therapeutic vehicles with antifouling capability, i.e., preventing undesired sorption of biologics from the human body in the drug delivery vehicle. Therefore, (poly)zwitterionic materials have been broadly applied in stimuli-responsive “intelligent” drug delivery systems as well as tumor-targeting carriers because of their excellent biocompatibility, low cytotoxicity, insignificant immunogenicity, high stability, and long circulation time. To tailor (poly)zwitterionic drug vehicles, an interpretation of the structural and stimuli-responsive behavior of this type of polymer is essential. To this end, a direct study of molecular-level interactions, orientations, configurations, and physicochemical properties of (poly)zwitterions is required, which can be achieved via molecular modeling, which has become an influential tool for discovering new materials and understanding diverse material phenomena. As the essential bridge between science and engineering, molecular simulations enable the fundamental understanding of the encapsulation and release behavior of intelligent drug-loaded (poly)zwitterion nanoparticles and can help us to systematically design their next generations. When combined with experiments, modeling can make quantitative predictions. This perspective article aims to illustrate key recent developments in (poly)zwitterion-based drug delivery systems. We summarize how to use predictive multiscale molecular modeling techniques to successfully boost the development of intelligent multifunctional (poly)zwitterions-based systems.

Self-Assembly of an Amphiphilic Bile Acid Dimer: A Combined Experimental and Theoretical Study of Its Medium-Responsive Fluorescence.

This work describes the synthesis and aggregation behavior of a dimeric bile acid derivative in which two steroid cores are bridged by a p-di(phenylethynyl)phenylene fluorophore. The studied compound contains three key characteristics: (a) restricted conformational equilibrium in solution, (b) efficient fluorescence conferred by the bridge, and (c) medium responsiveness encoded in the steroid fragments. The incorporation of the three components afforded a compound that generates nano- and micrometric spherical particles with aggregation-responsive fluorescence emission. The observed self-assembly process of the featured molecule was induced by the gradual addition of water to the tetrahydrofuran (THF) solution. This aggregation led to significant changes in fluorescence that went from two bands at λem values of 370 and 390 nm in pure THF to a new spectrum with two maxima at λem values of 395 and 418 nm at high water contents, without a decrease in emission. The observed changes can be ascribed to weakly coupled aggregation, a hypothesis supported by multiscale molecular modeling, which sheds light on the mechanism of the self-assembly of this unconventional amphiphile.

Surface Electrostatics Govern the Emulsion Stability of Biomolecular Condensates.

Liquid-liquid phase separation underlies the formation of biological condensates. Physically, such systems are microemulsions that in general have a propensity to fuse and coalesce; however, many condensates persist as independent droplets in the test tube and inside cells. This stability is crucial for their function, but the physicochemical mechanisms that control the emulsion stability of condensates remain poorly understood. Here, by combining single-condensate zeta potential measurements, optical microscopy, tweezer experiments, and multiscale molecular modeling, we investigate how the nanoscale forces that sustain condensates impact their stability against fusion. By comparing peptide-RNA (PR25:PolyU) and proteinaceous (FUS) condensates, we show that a higher condensate surface charge correlates with a lower fusion propensity. Moreover, measurements of single condensate zeta potentials reveal that such systems can constitute classically stable emulsions. Taken together, these results highlight the role of passive stabilization mechanisms in protecting biomolecular condensates against coalescence.

Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond.

Important contemporary biological and materials problems often depend on interactions that span orders of magnitude differences in spatial and temporal dimensions. This Tutorial Review attempts to provide an introduction to such fascinating problems through a series of case studies, aimed at beginning researchers, graduate students, postdocs and more senior colleagues who are changing direction to focus on multiscale aspects of their research. The choice of specific examples is highly personal, with examples either chosen from our own work or outstanding multiscale efforts from the literature. I start with various embedding schemes, as exemplified by polarizable continuum models, 3-D RISM, molecular DFT and frozen-density embedding. Next, QM/MM (quantum mechanical/molecular mechanical) techniques are the workhorse of pm-to-nm/ps-to-ns simulations; examples are drawn from enzymes and from nanocatalysis for oil-sands upgrading. Using polarizable force-fields in the QM/MM framework represents a burgeoning subfield; with examples from ion channels and electron dynamics in molecules subject to strong external fields, probing the atto-second dynamics of the electrons with RT-TDDFT (real-time - time-dependent density functional theory) eventually coupled with nuclear motion through the Ehrenfest approximation. This is followed by a section on coarse graining, bridging dimensions from atoms to cells. The penultimate chapter gives a quick overview of multiscale approaches that extend into the meso- and macro-scales, building on atomistic and coarse-grained techniques to enter the world of materials engineering, on the one hand, and cell biology, on the other. A final chapter gives just a glimpse of the burgeoning impact of machine learning on the structure-dynamics front. I aim to capture the excitement of contemporary leading-edge breakthroughs in the description of physico-chemical systems and processes in complex environments, with only enough historical content to provide context and aid the next generation of methodological development. While I aim also for a clear description of the essence of methodological breakthroughs, equations are kept to a minimum and detailed formalism and implementation details are left to the references. My approach is very selective (case studies) rather than exhaustive. I think that these case studies should provide fodder to build as complete a reference tree on multiscale modelling as the reader may wish, through forward and backward citation analysis. I hope that my choices of cases will excite interest in newcomers and help to fuel the growth of multiscale modelling in general.

The neutralization effect of montelukast on SARS-CoV-2 is shown by multiscale in silico simulations and combined in vitro studies

Small molecule inhibitors have previously been investigated in different studies as possible therapeutics in the treatment of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In the current drug repurposing study, we identified the leukotriene (D4) receptor antagonist montelukast as a novel agent that simultaneously targets two important drug targets of SARS-CoV-2. We initially demonstrated the dual inhibition profile of montelukast through multiscale molecular modeling studies. Next, we characterized its effect on both targets by different in vitro experiments including the enzyme (main protease) inhibition-based assay, surface plasmon resonance (SPR) spectroscopy, pseudovirus neutralization on HEK293T/hACE2+TMPRSS2, and virus neutralization assay using xCELLigence MP real-time cell analyzer. Our integrated in silico and in vitro results confirmed the dual potential effect of montelukast both on the main protease enzyme inhibition and virus entry into the host cell (spike/ACE2). The virus neutralization assay results showed that SARS-CoV-2 virus activity was delayed with montelukast for 20 h on the infected cells. The rapid use of new small molecules in the pandemic is very important today. Montelukast, whose pharmacokinetic and pharmacodynamic properties are very well characterized and has been widely used in the treatment of asthma since 1998, should urgently be completed in clinical phase studies and, if its effect is proved in clinical phase studies, it should be used against coronavirus disease 2019 (COVID-19).

Multiscale modelling techniques in life cycle assessment: Application to nanostructured polymer systems in the maritime industry

One of the major challenges of Life Cycle Assessment is in the generation of life cycle inventories for complex or novel materials. This paper aims at broadening the perspective in Life Cycle Assessment methodology, exploiting multiscale modelling towards the generation of inventory data during an early-stage product design. Our approach involves the usage of molecular modelling techniques, such as electronic, atomistic or mesoscale models, in combination with continuum models, such as process simulation or finite element methods, to provide data for the generation of life cycle inventories of nanostructured materials. In particular, each simulation is performed at a specific length and time scale through specific software, passing information from the lower to the upper scale. We applied our procedure to a comparative Life Cycle Assessment among suitable materials for the production of a set of cylinder head cover of a marine engine, i.e., aluminium alloys and nano-engineered thermoplastic polymers. Engineering judgment, Ashby charts and maritime regulations led the initial identification of suitable materials. As a result, polyamide 6,6 reinforced with 30% wt. glass fibers and mixed with phosphate-based flame retardants have been identified as promising candidate for the replacement of aluminium traditional covers. Nonetheless, we introduced nanoclays, i.e., exfoliated montmorillonite, as a replacement for a quota of conventional flame retardant, aiming at reducing the usage of pollutant chemicals. We then resorted to atomistic and finite-element techniques to investigate the compliance of nanocomposite materials with several Key Performance Indicators. The nanocomposite formulations obtained from simulations have been finally employed as input data for life cycle inventories of material alternatives. Using a Life Cycle Assessment approach, we could identify the most environmental-friendly nanocomposite formulation, which was then compared with traditional aluminium through an additional comparative cradle-to-grave study. Among the suitable nanocomposite formulations, the material with the minimum amount of traditional flame retardant exhibited the lowest scores among the majority of Environmental Footprint impact categories. To account for the entire lifetime of the marine engine, a set of four nanocomposite covers has been compared with a set of three aluminium covers made by two different metal alloys. Four recycling scenarios for aluminium covers have been examined, assuming an incremental number of end-of-use products reintroduced into a circular life cycle, while nanocomposite ones were disposed into landfill. In turn, if the aluminium products are insufficiently recycled, our results promote the usage of nano-engineered polymer products. As the amount of reused metal covers increases, indeed, polymer-based covers become unfavorable with respect to aluminium cast alloy products. Accordingly, an increasing amount of post-consumer aluminium diminishes the need of primary metal, yielding considerable advantages in terms of environmental footprint. Consequently, stakeholders should commit in recycling metal components not only at ship dismantling, but also during ordinary maintenance. Our study showed how multiscale molecular modelling proved to be useful for providing necessary inventory data for complex nanostructured materials. This preliminary study paves the way for future development of the proposed framework, in which in-silico techniques based on distinct paradigms can mutually contribute towards an improvement of products ecodesign.

Interfacial Modeling of Fibrinogen Adsorption onto LiNbO3 Single Crystal-Single Domain Surfaces.

For the development of next-generation protein-based biosensor surfaces, it is important to understand how functional proteins, such as fibrinogen (FBG), interact with polar substrate surfaces in order to prepare highly sensitive points of medical care diagnostics. FBG, which is a fibrous protein with an extracellular matrix, has both positively and negatively charged regions on its 3-dimensional surface, which makes interpreting how it effectively binds to polarized surfaces challenging. In this study, single-crystal LiNbO3 (LNO) substrates that have surface charges were used to investigate the adsorption of FBG protruding polar fragments on the positively and negatively charged LNO surfaces. We performed a combination of experiments and multi-scale molecular modeling to understand the binding of FBG in vacuum and water-solvated surfaces of LNO. XPS measurements showed that the FBG adsorption on LNO increased with increment in solution concentration on surfaces independent of charges. Multi-scale molecular modeling employing Quantum Mechanics, Monte Carlo, and Molecular Mechanics addressed the phenomenon of FBG fragment bonding on LNO surfaces. The binding simulation validated the experimental observation using zeta potential measurements which showed presence of solvated medium influenced the adsorption phenomenon due to the negative surface potential.