Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning.

Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical machine learning approaches. Multiple unsupervised learning algorithms (principal component analysis, agglomerative clustering, and K-means) as well as supervised linear and nonlinear regression algorithms (linear regression, AdaBoost ensemble learning, artificial neural network) have been applied. As a result, a small set of biomolecules has been identified, knowledge of adsorption free energies of which to a specific nanomaterial can be used to predict, within the developed machine learning model, adsorption free energies of other biomolecules. Furthermore, the methodology of grouping of nanomaterials according to their interactions with biomolecules has been presented.

Development of a bottom-up coarse-grained model for interactions of lipids with TiO 2 nanoparticles.

Understanding interactions of inorganic nanoparticles with biomolecules is important in many biotechnology, nanomedicine, and toxicological research, however, the size of typical nanoparticles makes their direct modeling by atomistic simulations unfeasible. Here, we present a bottom-up coarse-graining approach for modeling titanium dioxide (TiO 2 ) nanomaterials in contact with phospholipids that uses the inverse Monte Carlo method to optimize the effective interactions from the structural data obtained in small-scale all-atom simulations of TiO 2 surfaces with lipids in aqueous solution. The resulting coarse-grained models are able to accurately reproduce the structural details of lipid adsorption on different titania surfaces without the use of an explicit solvent, enabling significant computational resource savings and favorable scaling. Our coarse-grained simulations show that small spherical TiO 2 nanoparticles ( r = 2 nm) can only be partially wrapped by a lipid bilayer with phosphoethanolamine headgroups, however, the lipid adsorption increases with the radius of the nanoparticle. The current approach can be used to study the effect of the size and shape of TiO 2 nanoparticles on their interactions with cell membrane lipids, which can be a determining factor in membrane wrapping as well as the recently discovered phenomenon of nanoquarantining, which involves the formation of layered nanomaterial-lipid structures.

Multiscale modeling reveals the ion-mediated phase separation of nucleosome core particles.

Due to the vast length scale inside the cell nucleus, multiscale models are required to understand chromatin folding, structure, and dynamics and how they regulate genomic activities such as DNA transcription, replication, and repair. We study the interactions and structure of condensed phases formed by the universal building block of chromatin, the nucleosome core particle (NCP), using bottom-up multiscale coarse-grained (CG) simulations with a model extracted from all-atom MD simulations. In the presence of the multivalent cations Mg(H2O)62+ or CoHex3+, we analyze the internal structures of the NCP aggregates and the contributions of histone tails and ions to the aggregation patterns. We then derive a "super" coarse-grained (SCG) NCP model to study the macroscopic scale phase separation of NCPs. The SCG simulations show the formation of NCP aggregates with Mg(H2O)62+ concentration-dependent densities and sizes. Variation of the CoHex3+ concentrations results in highly ordered lamellocolumnar and hexagonal columnar phases in agreement with experimental data. The results give detailed insights into nucleosome interactions and for understanding chromatin folding in the cell nucleus.

Coarse-Grained Modeling Using Neural Networks Trained on Structural Data.

We propose a method of bottom-up coarse-graining, in which interactions within a coarse-grained model are determined by an artificial neural network trained on structural data obtained from multiple atomistic simulations. The method uses ideas of the inverse Monte Carlo approach, relating changes in the neural network weights with changes in average structural properties, such as radial distribution functions. As a proof of concept, we demonstrate the method on a system interacting by a Lennard-Jones potential modeled by a simple linear network and a single-site coarse-grained model of methanol-water solutions. In the latter case, we implement a nonlinear neural network with intermediate layers trained by atomistic simulations carried out at different methanol concentrations. We show that such a network acts as a transferable potential at the coarse-grained resolution for a wide range of methanol concentrations, including those not included in the training set.

Biomolecular Adsorprion at ZnS Nanomaterials: A Molecular Dynamics Simulation Study of the Adsorption Preferences, Effects of the Surface Curvature and Coating.

The understanding of interactions between nanomaterials and biological molecules is of primary importance for biomedical applications of nanomaterials, as well as for the evaluation of their possible toxic effects. Here, we carried out extensive molecular dynamics simulations of the adsorption properties of about 30 small molecules representing biomolecular fragments at ZnS surfaces in aqueous media. We computed adsorption free energies and potentials of mean force of amino acid side chain analogs, lipids, and sugar fragments to ZnS (110) crystal surface and to a spherical ZnS nanoparticle. Furthermore, we investigated the effect of poly-methylmethacrylate (PMMA) coating on the adsorption preferences of biomolecules to ZnS. We found that only a few anionic molecules: aspartic and glutamic acids side chains, as well as the anionic form of cysteine show significant binding to pristine ZnS surface, while other molecules show weak or no binding. Spherical ZnS nanoparticles show stronger binding of these molecules due to binding at the edges between different surface facets. Coating of ZnS by PMMA changes binding preferences drastically: the molecules that adsorb to a pristine ZnS surface do not adsorb on PMMA-coated surfaces, while some others, particularly hydrophobic or aromatic amino-acids, show high binding affinity due to binding to the coating. We investigate further the hydration properties of the ZnS surface and relate them to the binding preferences of biomolecules.

Ab Initio Derived Classical Force Field for Molecular Dynamics Simulations of ZnO Surfaces in Biological Environment.

Zinc oxide nanostructures are used in an ever increasing line of applications in technology and biomedical fields. This requires a detailed understanding of the phenomena that occur at the surface particularly in aqueous environments and in contact with biomolecules. In this work, we used ab initio molecular dynamics (AIMD) simulations to determine structural details of ZnO surfaces in water and to develop a general and transferable classical force field for hydrated ZnO surfaces. AIMD simulations show that water molecules dissociate near unmodified ZnO surfaces, forming hydroxyl groups at about 65% of the surface Zn atoms and protonating 3-coordinated surface oxygen atoms, while the rest of the surface Zn atoms bind molecularly adsorbed waters. Several force field atom types for ZnO surface atoms were identified by analysis of the specific connectivities of atoms. The analysis of the electron density was then used to determine partial charges and Lennard-Jones parameters for the identified force field atom types. The obtained force field was validated by comparison with AIMD results and with available experimental data on adsorption and immersion enthalpies, as well as adsorption free energies of several amino acids in methanol. The developed force field can be used for modeling of ZnO in aqueous and other fluid environments and in interaction with biomolecules.

Neutron scattering study of polyamorphic THF·17(H2O) - toward a generalized picture of amorphous states and structures derived from clathrate hydrates.

From crystalline tetrahydrofuran clathrate hydrate, THF-CH (THF·17H2O, cubic structure II), three distinct polyamorphs can be derived. First, THF-CH undergoes pressure-induced amorphization when pressurized to 1.3 GPa in the temperature range 77-140 K to a form which, in analogy to pure ice, may be called high-density amorphous (HDA). Second, HDA can be converted to a densified form, VHDA, upon heat-cycling at 1.8 GPa to 180 K. Decompression of VHDA to atmospheric pressure below 130 K produces the third form, recovered amorphous (RA). Results from neutron scattering experiments and molecular dynamics simulations provide a generalized picture of the structure of amorphous THF hydrates with respect to crystalline THF-CH and liquid THF·17H2O solution (∼2.5 M). Although fully amorphous, HDA is heterogeneous with two length scales for water-water correlations (less dense local water structure) and guest-water correlations (denser THF hydration structure). The hydration structure of THF is influenced by guest-host hydrogen bonding. THF molecules maintain a quasiregular array, reminiscent of the crystalline state, and their hydration structure (out to 5 Å) constitutes ∼23H2O. The local water structure in HDA is reminiscent of pure HDA-ice featuring 5-coordinated H2O. In VHDA, the hydration structure of HDA is maintained but the local water structure is densified and resembles pure VHDA-ice with 6-coordinated H2O. The hydration structure of THF in RA constitutes ∼18 H2O molecules and the water structure corresponds to a strictly 4-coordinated network, as in the liquid. Both VHDA and RA can be considered as homogeneous.

Water structure, dynamics and reactivity on a TiO2-nanoparticle surface: new insights from ab initio molecular dynamics.

Water structure, dynamics and reactivity at the surface of a small TiO2-nanoparticle fully immersed in water was investigated by an ab initio molecular dynamics simulation. Several modes of water binding were identified by assigning each atom to an atom type, representing a distinct chemical environment in the ab initio ensemble, and then computing radial distribution functions between the atom types. Surface reactivity was investigated by monitoring how populations of atom types change during the simulation. In order to acquire further insight, electron densities for a set of representative system snapshots were analyzed using an atoms-in-molecules approach. Our results reveal that water dissociation, where a water molecule splits at a bridging oxygen site to form a hydroxyl group and a protonated oxygen bridge, can occur by a mechanism involving transfer of a proton over several water molecules. The hydroxyl group and protonated oxygen bridge formed in the process persist (on a 10 ps time scale) and the hydroxyl group undergoes exchange using a mechanism similar to the one responsible for water dissociation. Rotational and translational dynamics of water molecules around the nanoparticle were analyzed in terms of reorientational time correlation functions and mean square displacement. While reorientation of water O-H vectors decreases quickly in the proximity of the nanoparticle surface, translational diffusion slows down more gradually. Our results give new insight into water structure, dynamics and reactivity on TiO2-nanoparticle surfaces and suggest that water dissociation on curved TiO2-nanoparticle surfaces can occur via more complex mechanisms than those previously identified for flat defect-free surfaces.

A Bottom-Up Coarse-Grained Model for Nucleosome-Nucleosome Interactions with Explicit Ions.

The nucleosome core particle (NCP) is a large complex of 145-147 base pairs of DNA and eight histone proteins and is the basic building block of chromatin that forms the chromosomes. Here, we develop a coarse-grained (CG) model of the NCP derived through a systematic bottom-up approach based on underlying all-atom MD simulations to compute the necessary CG interactions. The model produces excellent agreement with known structural features of the NCP and gives a realistic description of the nucleosome-nucleosome attraction in the presence of multivalent cations (Mg(H2O)62+ or Co(NH3)63+) for systems comprising 20 NCPs. The results of the simulations reveal structural details of the NCP-NCP interactions unavailable from experimental approaches, and this model opens the prospect for the rigorous modeling of chromatin fibers.

Atomistic Molecular Dynamics Simulations of Lipids Near TiO2 Nanosurfaces.

Understanding of interactions between inorganic nanomaterials and biomolecules, and particularly lipid bilayers, is crucial in many biotechnological and biomedical applications, as well as for the evaluation of possible toxic effects caused by nanoparticles. Here, we present a molecular dynamics study of adsorption of two important constituents of the cell membranes, 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), lipids to a number of titanium dioxide planar surfaces, and a spherical nanoparticle under physiological conditions. By constructing the number density profiles of the lipid headgroup atoms, we have identified several possible binding modes and calculated their relative prevalence in the simulated systems. Our estimates of the adsorption strength, based on the total fraction of adsorbed lipids, show that POPE binds to the selected titanium dioxide surfaces stronger than DMPC, due to the ethanolamine group forming hydrogen bonds with the surface. Moreover, while POPE shows a clear preference toward anatase surfaces over rutile, DMPC has a particularly high affinity to rutile(101) and a lower affinity to other surfaces. Finally, we study how lipid concentration, addition of cholesterol, as well as titanium dioxide surface curvature may affect overall adsorption.

First principles characterisation of bio-nano interface.

Nanomaterials possess a wide range of potential applications due to their novel properties and exceptionally high activity as a result of their large surface to volume ratios compared to bulk matter. The active surface may present both advantage and risk when the nanomaterials interact with living organisms. As the overall biological impact of nanomaterials is triggered and mediated by interactions at the bio-nano interface, an ability to predict those from the atomistic descriptors, especially before the material is produced, can present enormous advantage for the development of nanotechnology. Fast screening of nanomaterials and their variations for specific biological effects can be enabled using computational materials modelling. The challenge lies in the range of scales that needs to be crossed from the material-specific atomistic representation to the relevant length scales covering typical biomolecules (proteins and lipids). In this work, we present a systematic multiscale approach that allows one to evaluate crucial interactions at the bionano interface from the first principles without any prior information about the material and thus establish links between the details of the nanomaterials structure to protein-nanoparticle interactions. As an example, an advanced computational characterization of titanium dioxide nanoparticles (6 different surfaces of rutile and anatase polymorphs) has been performed. We computed characteristics of the titanium dioxide interface with water using density functional theory for electronic density, used these parameters to derive an atomistic force field, and calculated adsorption energies for essential biomolecules on the surface of titania nanoparticles via direct atomistic simulations and coarse-grained molecular dynamics. Hydration energies, as well as adsorption energies for a set of 40 blood proteins are reported.

Bottom-Up Coarse-Grained Modeling of DNA.

Recent advances in methodology enable effective coarse-grained modeling of deoxyribonucleic acid (DNA) based on underlying atomistic force field simulations. The so-called bottom-up coarse-graining practice separates fast and slow dynamic processes in molecular systems by averaging out fast degrees of freedom represented by the underlying fine-grained model. The resulting effective potential of interaction includes the contribution from fast degrees of freedom effectively in the form of potential of mean force. The pair-wise additive potential is usually adopted to construct the coarse-grained Hamiltonian for its efficiency in a computer simulation. In this review, we present a few well-developed bottom-up coarse-graining methods, discussing their application in modeling DNA properties such as DNA flexibility (persistence length), conformation, "melting," and DNA condensation.

Atomistic Perspective on Biomolecular Adsorption on Functionalized Carbon Nanomaterials under Ambient Conditions.

The use of carbon-based nanomaterials is tremendously increasing in various areas of technological, bioengineering, and biomedical applications. The functionality of carbon-based nanomaterials can be further broadened via chemical functionalization of carbon nanomaterial surfaces. On the other hand, concern is rising on possible adverse effects when nanomaterials are taken up by biological organisms. In order to contribute into understanding of interactions of carbon-based nanomaterials with biological matter, we have investigated adsorption of small biomolecules on nanomaterials using enhanced sampling molecular dynamics. The biomolecules included amino acid side chain analogues, fragments of lipids, and sugar monomers. The adsorption behavior on unstructured amorphous carbon, pristine graphene and its derivatives (such as few-layer graphene, graphene oxide, and reduced graphene oxide) as well as pristine carbon nanotubes, and those functionalized with OH-, COOH-, COO-, NH2-, and NH3+ groups was investigated with respect to surface concentration. An adsorption profile, that is, the free energy as a function of distance from the nanomaterial surfaces, was determined for each molecule and surface using the Metadynamics approach. The results were analyzed in terms of chemical specificity, surface charge, and surface concentration. It was shown that although morphology of the nanomaterial has a limited effect on the adsorption properties, functionalization of the surface by various molecular groups can drastically change the adsorption behavior that can be used in the design of nanosurfaces with highly selective adsorption properties and safe for human health and environment.

Structural investigation of three distinct amorphous forms of Ar hydrate.

Three amorphous forms of Ar hydrate were produced using the crystalline clathrate hydrate Ar·6.5H2O (structure II, Fd3̄m, a ≈ 17.1 Å) as a precursor and structurally characterized by a combination of isotope substitution (36Ar) neutron diffraction and molecular dynamics (MD) simulations. The first form followed from the pressure-induced amorphization of the precursor at 1.5 GPa at 95 K and the second from isobaric annealing at 2 GPa and subsequent cooling back to 95 K. In analogy to amorphous ice, these amorphs are termed high-density amorphous (HDA) and very-high-density amorphous (VHDA), respectively. The third amorph (recovered amorphous, RA) was obtained when recovering VHDA to ambient pressure (at 95 K). The three amorphs have distinctly different structures. In HDA the distinction of the original two crystallographically different Ar guests is maintained as differently dense Ar-water hydration structures, which expresses itself in a split first diffraction peak in the neutron structure factor function. Relaxation of the local water structure during annealing produces a homogeneous hydration environment around Ar, which is accompanied with a densification by about 3%. Upon pressure release the homogeneous amorphous structure undergoes expansion by about 21%. Both VHDA and RA can be considered frozen solutions of immiscible Ar and water in which in average 15 and 11 water molecules, respectively, coordinate Ar out to 4 Å. The local water structures of HDA and VHDA Ar hydrates show some analogy to those of the corresponding amorphous ices, featuring H2O molecules in 5- and 6-fold coordination with neighboring molecules. However, they are considerably less dense. Most similarity is seen between RA and low density amorphous ice (LDA), which both feature strictly 4-coordinated H2O networks. It is inferred that, depending on the kind of clathrate structure and occupancy of cages, amorphous states produced from clathrate hydrates display variable local water structures.

Prediction of Chronic Inflammation for Inhaled Particles: the Impact of Material Cycling and Quarantining in the Lung Epithelium.

On a daily basis, people are exposed to a multitude of health-hazardous airborne particulate matter with notable deposition in the fragile alveolar region of the lungs. Hence, there is a great need for identification and prediction of material-associated diseases, currently hindered due to the lack of in-depth understanding of causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modeling, it is determined herein that the long-lasting response to a single exposure can originate from the interplay between the newly discovered nanomaterial quarantining and nanomaterial cycling between different lung cell types. This new insight finally allows prediction of the spectrum of lung inflammation associated with materials of interest using only in vitro measurements and in silico modeling, potentially relating outcomes to material properties for a large number of materials, and thus boosting safe-by-design-based material development. Because of its profound implications for animal-free predictive toxicology, this work paves the way to a more efficient and hazard-free introduction of numerous new advanced materials into our lives.

Optimization of Slipids Force Field Parameters Describing Headgroups of Phospholipids.

The molecular mechanics force field Slipids developed in a series of works by Jämbeck and Lyubartsev (J. Phys. Chem. B 2012, 116, 3164-3179; J. Chem. Theory Comput. 2012, 8, 2938-2948) generally provides a good description of various lipid bilayer systems. However, it was also found that order parameters of C-H bonds in the glycerol moiety of the phosphatidylcholine headgroup deviate significantly from NMR results. In this work, the dihedral force field parameters have been reparameterized in order to improve the agreement with experiment. For this purpose, we have computed energies for a large amount of lipid headgroup conformations using density functional theory on the B3P86/cc-pvqz level and optimized dihedral angle parameters simultaneously to provide the best fit to the quantum chemical energies. The new parameter set was validated for three lipid bilayer systems against a number of experimental properties including order parameters, area per lipid, scattering form factors, bilayer thickness, area compressibility and lateral diffusion coefficients. In addition, the order parameter dependence on cholesterol content in the POPC bilayer was investigated. It is shown that the new force field significantly improves agreement with the experimental order parameters for the lipid headgroup while keeping good agreement with other experimentally measured properties.

Modelling of interactions between Aß(25-35) peptide and phospholipid bilayers: effects of cholesterol and lipid saturation.

Aggregation of amyloid beta (Aß) peptides in neuronal membranes is a known promoter of Alzheimer's disease. To gain insight into the molecular details of Aß peptide aggregation and its effect on model neuronal membranes, we carried out molecular dynamics simulations of the Aß(25-35) fragment of the amyloid precursor protein in phospholipid bilayers composed of either fully saturated or highly unsaturated lipids, in the presence or absence of cholesterol. It was found that the peptide does not penetrate through any of the considered membranes, but can reside in the headgroup region and upper part of the lipid tails showing a clear preference to a polyunsaturated cholesterol-free membrane. Due to the ordering and condensing effect upon addition of cholesterol, membranes become more rigid facilitating peptide aggregation on the surface. Except for the case of the cholesterol-free saturated lipid bilayer, the peptides have a small effect on the membrane structure and ordering. It was also found that the most "active" amino-acid for peptide-lipid and peptide-cholesterol interaction is methionine-35, followed by asparagine-27 and serine-26, which form hydrogen bonds between peptides and polar atoms of lipid headgroups. These amino acids are also primarily responsible for peptide aggregation. This work will be relevant for designing strategies to develop drugs to combat Alzheimer's disease.

Modeling DNA Flexibility: Comparison of Force Fields from Atomistic to Multiscale Levels.

Accurate parametrization of force fields (FFs) is of ultimate importance for computer simulations to be reliable and to possess a predictive power. In this work, we analyzed, in multi-microsecond simulations of a 40-base-pair DNA fragment, the performance of four force fields, namely, the two recent major updates of CHARMM and two from the AMBER family. We focused on a description of double-helix DNA flexibility and dynamics both at atomistic and at mesoscale level in coarse-grained (CG) simulations. In addition to the traditional analysis of different base-pair and base-step parameters, we extended our analysis to investigate the ability of the force field to parametrize a CG DNA model by structure-based bottom-up coarse-graining, computing DNA persistence length as a function of ionic strength. Our simulations unambiguously showed that the CHARMM36 force field is unable to preserve DNA's structural stability at over-microsecond time scale. Both versions of the AMBER FF, parmbsc0 and parmbsc1, showed good agreement with experiment, with some bias of parmbsc0 parameters for intermediate A/B form DNA structures. The CHARMM27 force field provides stable atomistic trajectories and overall (among the considered force fields) the best fit to experimentally determined DNA flexibility parameters both at atomistic and at mesoscale level.

Effect of lipid saturation on amyloid-beta peptide partitioning and aggregation in neuronal membranes: molecular dynamics simulations.

Aggregation of amyloid-[Formula: see text] (Aß) peptides, cleaved from the amyloid precursor protein, is known as a precursor of the Alzheimer's disease (AD). It is also known that Alzheimer's disease is characterized by a substantial decrease of the amount of polyunsaturated lipids in the neuronal membranes of the frontal gray matter. To get insight into possible interconnection of these phenomena, we have carried out molecular dynamics simulations of two fragments of A[Formula: see text] peptide, A[Formula: see text][Formula: see text] and A[Formula: see text][Formula: see text], in four different lipid bilayers: two monocomponent ones (14:0-14:0 PC, 18:0-22:6 PC), and two bilayers containing mixtures of 18:0-18:0 PE, 22:6-22:6 PE, 16:0-16:0 PC and 18:1-18:1 PC lipids of composition mimicking neuronal membranes in a "healthy" and "AD" brain. The simulations showed that the presence of lipids with highly unsaturated 22:6cis fatty acids chains strongly affects the interaction of amyloid-[Formula: see text] peptides with lipid membranes. The polyunsaturated lipids cause stronger adsorption of A[Formula: see text]-peptides by the membrane and lead to weaker binding between peptides when the latter form aggregates. This difference in the behaviour observed in monocomponent bilayers is propagated in a similar fashion to the mixed membranes mimicking composition of neuronal membranes in "healthy" and "AD" brains, with "healthy" membrane having higher fraction of polyunsaturated lipids. Our simulations give strong indication that it can be physical-chemical background of the interconnection between amyloid fibrillization causing Alzheimer's disease, and content of polyunsaturated lipids in the neuronal membranes.

A multiscale analysis of DNA phase separation: from atomistic to mesoscale level.

DNA condensation and phase separation is of utmost importance for DNA packing in vivo with important applications in medicine, biotechnology and polymer physics. The presence of hexagonally ordered DNA is observed in virus capsids, sperm heads and in dinoflagellates. Rigorous modelling of this process in all-atom MD simulations is presently difficult to achieve due to size and time scale limitations. We used a hierarchical approach for systematic multiscale coarse-grained (CG) simulations of DNA phase separation induced by the three-valent cobalt(III)-hexammine (CoHex3+). Solvent-mediated effective potentials for a CG model of DNA were extracted from all-atom MD simulations. Simulations of several hundred 100-bp-long CG DNA oligonucleotides in the presence of explicit CoHex3+ ions demonstrated aggregation to a liquid crystalline hexagonally ordered phase. Following further coarse-graining and extraction of effective potentials, we conducted modelling at mesoscale level. In agreement with electron microscopy observations, simulations of an 10.2-kb-long DNA molecule showed phase separation to either a toroid or a fibre with distinct hexagonal DNA packing. The mechanism of toroid formation is analysed in detail. The approach used here is based only on the underlying all-atom force field and uses no adjustable parameters and may be generalised to modelling chromatin up to chromosome size.