Modeling the Interaction of L-Hydroxyproline, a Constituent of Collagen, with a Hydrated TiO2 lattice at Varied Concentrations: Examining Surface and Long-Range Effects

Modeling the Interaction of L-Hydroxyproline, a Constituent of Collagen, with a Hydrated TiO2 lattice at Varied Concentrations: Examining Surface and Long-Range Effects | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling the Interaction of L-Hydroxyproline, a Constituent of Collagen, with a Hydrated TiO2 lattice at Varied Concentrations: Examining Surface and Long-Range Effects Maria Valentini, Pierluigi Caboni, Giovanni Sanna, Massimo Pisu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4400232/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jul, 2024 Read the published version in BioNanoScience → Version 1 posted 9 You are reading this latest preprint version Abstract This study investigates the interaction between L-hydroxyproline (LHP), a key component of collagen, and a hydrated titanium dioxide (TiO 2 ) lattice at various LHP concentrations. It represents the first step toward a broader project aimed at recycling agri-food wastes and byproducts, particularly mussel byssus, to enhance existing nano-coatings and design new ones. We performed gas chromatography-mass spectrometry analysis of byssus, which revealed 22 metabolites, confirming glycine, L-proline, and particularly LHP as key biomolecules. Subsequently, molecular dynamics (MD) simulations provided insights into LHP-lattice interaction mechanisms, revealing the TiO 2 lattice's ability to align LHP rings near-perpendicular to the lattice surface and near-parallel to each other, facilitated by the LHP tail functional group. This indicates optimal LHP packing, particularly close to the surface, and the formation of durable bonds between LHPs and lattice atoms. The analysis, particularly radial distribution functions, indicates that lattice-driven organizing interactions extend from the surface region to the bulk liquid phase thanks to the LHP– and water–mediated contributions. Overall, the simulation provides a chemical-physics rationale to explain improved collagen adhesion to the TiO 2 lattice, contributing to understanding collagen-TiO 2 interactions, and offering valuable insights for nanomaterials, biomaterials, tissue engineering, and biomedical applications. Titanium Dioxide Lattice Molecular Dynamics Collagene L-hydroxyproline Nanomaterials for Biomedicine and Biomedical Applications Nanomaterial Functionalization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Collagen stands out as a key constituent within the extracellular matrix, serving as a structural protein in connective tissues, skin, and bones. Its primary building blocks include proline, hydroxyproline, and glycine [ 1 ], with proline emerging as an indispensable amino acid prominently present in collagen. In particular, proline's unique structure plays a key role in contributing to stabilization and overall integrity of connective tissues and hydroxylation of proline residues in collagen is vital for its stability. In fact, the latter post-translational modification enhances intermolecular interactions, reinforcing the collagen structure [ 2 – 4 ]. In biological organisms, collagen proteins provide exceptional adhesion properties [ 5 ]. Therefore, the embedding of collagen components in nanostructured materials and nano-lattices have been extensively studied with the aim to design new materials with improved properties, such as adhesion and mechanical resistance [ 6 ], antibacterial characteristics [ 7 ] and surface protection [ 8 ]. In this context, in recent years it is particularly relevant for the health sector the research of bioadhesives with improved adhesion and biological compatibility [ 9 ]. In fact, titanium is the principal material employed nowadays in biomedical applications, biofunctionalization and covering of medical implants, above all dental and orthopedic ones [ 10 ], due to its biocompatibility and the unique crystal structures with highly reactive surfaces. Therefore the study of (hydroxy)proline–titanium lattice interaction is important to understand the adhesion between biological tissues and medical implant materials, to improve implant features, such as biocompatibility, biointegration, mechanical properties and non toxicity, and ultimately to avoid implant loosening, infection or rejection by immune system reaction [ 11 ]. Titanium dioxide exists in multiple crystal structures, with the most common being rutile and anatase. These structures impart unique properties, such as high surface area and photocatalytic activity, to TiO 2 . TiO 2 surfaces are widely known for their reactivity, and their interaction with biomolecules is heavily influenced by surface properties, with functional groups on TiO 2 surfaces that can engage in hydrogen bonding and electrostatic interactions with biomolecules, particularly with hydroxyl groups on hydroxylated proline residues [ 12 – 14 ]. Moreover, titanium dioxide is widely used in biomedical applications due to its excellent biocompatibility. For instance, it is often employed in implants, dental materials, and drug delivery systems [ 15 – 17 ]. More precisely, TiO 2 nanoparticles can be incorporated into drug delivery systems, where interaction with collagen may influence the release kinetics of encapsulated substances, providing a platform for controlled drug delivery. TiO 2 surfaces, when appropriately modified, could serve as substrates for biomineralization processes, influencing the deposition of hydroxyapatite or other mineral phases. This is relevant for applications in bone tissue engineering [ 15 ]. In detail, modified TiO 2 surfaces can provide a platform for the controlled deposition of collagen, facilitating cell adhesion and tissue regeneration. Finally TiO 2 's photocatalytic properties have applications in sterilization and decontamination, with implications for wound healing and antimicrobial applications [ 18 ]. In general, the interaction collagen–lattice may influence the adsorption and alignment of collagen on TiO 2 surfaces [ 19 – 21 ]. Moreover, the surface of TiO 2 can be modified by introducing specific functional groups that enhance interactions with collagen. This tailored surface chemistry can influence the adhesion, proliferation, and differentiation of cells interacting with the biomaterial [ 22 – 26 ]. Due to the complexity and variety of the involved biological systems, the experimental studies of the interaction between biological environment and nanostructured materials are quite difficult to set up and to interpret, while computational studies, particularly Molecular Dynamics (MD), can be more effective and offer the opportunity to analyze specific steps in multi-step interaction mechanisms at specific temporal and spatial scales [ 27 , 28 ]. Adhesion forces among collagene and titanium dioxide layers may be experimentally evaluated through Atomic Force Microscopy [ 29 , 30 ]. These results were effectively reproduced by restrained MD techniques [ 31 ], showing how the interface between titanium surface and collagen peptides is controlled by local interaction between peptide amino acid side chains and localized surface charge (of Ti, O or OH), particularly for those aminoacids charged at physiological pH conditions. To optimize titanium layer adsorption or adhesion it is also important to analyze the different roles played by distinct titanium dioxide surface orientations. In this case, e.g. quantum DFT simulations were used to prove that the (110) surface is particularly efficient in hydrogen activation and thus in collagen protein adsorption, mainly controlled by specific collagen amino acids, namely glycine, proline and hydroxyproline [ 32 ]. The interaction at the interface between nano-structured components and biological systems is also extremely important in nanotoxicology, to assess the risk for the environment and for both human and animal health [ 33 , 34 ]. In this field, the adopted computational modeling techniques also include MD simulations, that e.g. were able to show how biomolecule adsorption on titanium dioxide lattices is truly effective at physiological pH conditions [ 35 ]. The authors find that molecular modeling is able to show how the interaction is dominated by titanium surface characteristics, including defects and contaminations, and by biomolecular structure and conformational changes, above all functional groups orientation with respect to the surface [ 35 ]. As previously mentioned, all these details are not easily obtainable from experiments. We conclude by synthesizing that the interaction between collagen components, particularly proline, and titanium dioxide lattice is a multifaceted topic with implications for biomaterials, tissue engineering, biomedical applications and new materials. Exploring and understanding these interactions opens avenues for the development of advanced materials with tailored properties for specific needs. In the meantime simulations may provide the opportunities to confirm and explain experimental results, identify novel underlying molecular mechanisms and help to design new materials for biomedical and industrial applications. The presented research work is inserted in a wider applied research activity that includes the development of innovative nanotechnological coatings, by exploiting agri-food production wastes and byproducts, and nanotoxicology studies on environment and exposed people. In this activity, observing the mussel's exceptional adhesion abilities, we decided to chemically characterize the byssus obtained from the mussels, a waste product of the mussel farming industry, in order to recognize the relevant molecules. Then, we simulated the previously identified molecules, mixed in various concentrations in an hydrated titanium dioxide lattice, with the aim to understand the impact on lattice characteristics and improve nano–coating functionalization and to define in future the optimal biomolecule mixing to commercial products. Materials and Methods: chemical characterization and modeling Byssus sample preparation, GC-MS Analysis and mixture preparation The byssus samples were dried in an oven at a temperature of 70°C for 5 days. The dried samples were then finely ground by means of a cryogenic ball miller to obtain an impalpable powder. These fine byssus samples were then prepared for gas chromatographic analysis combined with mass spectrometry (GC-MS). An aliquot of the fine byssus (100 mg, 6 samples) was weighed and placed in an Eppendorf tube to which 125 µL of chloroform and 250 µL of methanol were then added for the extraction of molecular constituents [ 36 ]. Subsequently the samples were left to stand for 1 h at room temperature and during this time they were shaken at regular intervals of 15 min. A further 380 µL of chloroform and 90 µL of aqueous potassium chloride 0.2 N were then added and then the samples were centrifuged for 10 min at 24100 rcf. After centrifugation we observed the separation of the two phases: the aqueous phase in the upper part of the Eppendorf tube and the organic phase in the lower one with a rather compact interface. Using a variable volume micropipette, 200 µL of aqueous solution and 200 µL of organic solution were collected and both samples were placed in glass containers (vials) for subsequent analyzes. The contents of the samples were then brought to dryness under a gentle nitrogen flow and, subsequently, 70 µL of a mixture of 0.01 g of methylhydroxylamine in 1 ml anhydrous pyridine were added to each sample. The samples were then left to stand for 17 hours, at the end of which each sample was functionalized to be analyzed by GC-MS with the addition of 50 µL of N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA). The samples were then shaken and left to stand for 1 hour at room temperature. Finally, 500 µL of hexane were added for GC-MS analysis [ 36 ]. The analysis was performed using a gas chromatograph combined with a triple quadrupole mass spectrometer (Thermofisher TSQ9000 GC-MSMS) equipped with an autosampler for liquids. The capillary column was in DB-5MS fused silica (30 m × 0.250 mm, film thickness 0.250 µm). The injector and the interface were at a temperature of 280°C. The temperature of the transfer line and of the ion source were 250°C and 300°C, respectively. The ions were generated at 70 eV with the electron impact ionization method and were recorded at 1.6 scans/s over the m/z mass range 50–550. The oven temperature was programmed as follows: 1 minute at constant 60°C temperature and then temperature constantly increased to 300°C in 25 min, and then temperature again left stable for 1 min. Helium was used as the carrier gas with a flow of 1mL/min, 1µL of sample was injected in splitless mode [ 37 ]. Raw GC-MS data files were database-searched against the National Institute of Standards and Technology (NIST) Mass SpectralDatabase (2.0) [ 38 ] and Golm metabolome database [ 39 ]. Confirmation of sample metabolites was performed by (a) comparison of their relative retention times and mass fragmentation to those of pure standards and (b) computer matching against NIST as well as retention indices as calculated according to Kovats for alkanes C9 − C36 compared to those reported by [ 40 ]. On the other hand, to obtain the powders to be mixed to the nanotechnological coats, the byssus samples were dried in an oven at a temperature of 70°C for 5 days. The dried samples were then finely ground by means of a cryogenic ball mill. This system allowed the sample to be cooled to the point of friability by liquid nitrogen and thus made the powders quite fine. In fact, liquid nitrogen is ideal for cryogenic freezing of material before adding to paints because it can reach nearly − 195°C, allowing the user to provide a steady stream of extremely cold liquid to freeze materials subjected to grinding. Figure 1 . GC-MS analysis of the byssus extract. The chromatographic peaks represent each metabolite, the retention time in minutes is reported on the abscissa and the instrumental response in arbitrary units on the ordinate. The arrows evidentiate the selected relevant metabolites. Three metabolites, in particular, have been identified as the most relevant and thus interesting for the development of the new mixtures: Glycine, L-Proline and L-Hydroxyproline, reported in Table 1 . As previously observed, these three amino acids represent the main components of the collagen protein, for humans as for mussels [ 41 ]. Table 1 Metabolite list. In the table are reported the retention time and the percentage in mass with respect to the full sample. The related three peaks on the chromatogram are evidenced in Fig. 1 . RT (min) COMPOUND % 16.39 Glycine 0.60 19.73 L-proline 2.54 22.85 L-Hydroxyproline 0.82 L-hydroxyproline Model Among all molecules identified, the most relevant are L-hydroxyproline (Table 3 , Fig. 10 ) and L-proline. In the extracellular matrix, proline is enzymatically transformed into L-hydroxyproline, thus L-Hydroxyproline represents the proline form most active in-vivo and we thus decided to model its interaction with titanium lattice. From now on we will indicate the L-hydroxyproline molecule (C 6 H 9 NO 3 ) by LHP. Table 2 reports the main chemical data for LHP and Fig. 2 shows its 2D and 3D structures. Table 2 L-Hydroxyproline main chemical characteristics. PubChem CID 5810 Molecular Formula C 5 H 9 NO 3 Molecular Weight 131.13 IUPAC Name (2S,4R)-4-hydroxypyrrolidine-2-carboxylic acid The structure of L-Hydroxyproline was retrieved in the ChemSpider database, with entry id CSID:5605. The atomic coordinates were stored in MOL file format. We have then used the ACPYPE server realized by Sousa da Silva and coworkers [ 42 , 43 ] to obtain the PDB file completed with H atoms. The topology and force field parameter file for CHARMM General force field [ 44 ] was also obtained by the same web server. All these files can be retrieved in the supplementary information. Titanium Dioxide Lattice Model Our model for the dioxide titanium lattice has been realized in the following way. The unit cell of rutile TiO 2 (Fig. 3 ) was obtained from the Crystallography Open Database [ 45 ], entry id COD:1530150. The unit cell dimensions along 𝑥, 𝑦 and 𝓏 direction are, respectively, 4.59 Å, 4.59 Å and 2.96 Å, resulting in a volume of 62.362 Å 3 and a density of 4.25 g/cm 3 . The atomic coordinates were stored in CIF format. The unit cell for the 100 surface orientation of the rutile crystal structure has been identified through VESTA [ 46 ] and saved in a PDB format file. It contains 2 Ti and 4 O atoms. The model of the TIO 2 lattice has been built using the NAMD tool [ 47 ], and specifically, the script replicate_crystal.tcl [ 48 ]: starting from the PDB file of the unit cell and replicating it 3 times along the 𝑥 direction and 10 times along both 𝑦 and 𝓏 directions. In this way we model a titanium dioxide slab, because we are mainly interested in surface effects rather than bulk ones. The final lattice has thus 600 Ti atoms and 1200 O atoms inserted in a box with sizes [13.384 Å, 46.942 Å, 29.902 Å]. The density of the resulting boxed TiO 2 lattice is 4.234 g/cm 3 which is in line with the density of the unit cell and the TiO 2 in nature. The replication process from the unit cell maintained the lattice in the 100 surface orientation of the rutile phase. The CHARMM force field topology file, that contains information about the bounds between atoms, necessary to run simulations by using the NAMD tool, was manually generated in agreement to the single unit cell parameters. Topology and force field parameter files are included in the supplementary information. Molecular Dynamics Simulation The Titanium dioxide lattice was placed in the center of the simulation box, with the minimum thickness along 𝑥 direction, and 70 Å of liquid phase with density 1.00 g/cm 3 (water and LHP) were added on each side along 𝑥 direction using PACKMOL [ 49 ]. We obtained four systems with specific proportion water:LHP, and the resulting system overall density was in all cases 1.29 g/cm 3 (Table 3 , Fig. 4 ). The number of LHP and water molecules in the system were calibrated to explore LHP concentration in the liquid phase ranging from 0 to 1 g/cm 3 , namely we selected four systems with LHP concentration in the liquid phase equal to 1.00, 0.10, 0.01 and 0.00 g/cm 3 . Notice that a fifth system has been added, with intermediate 0.10 g/cm 3 LHP concentration but titanium lattice kept fixed during dynamics. Table 3 Sizes and densities of the five simulated systems. Composition LHP concentration in the liquid phase (g/cm 3 ) Simulation Box dimensions Lx, Ly, Lz (Å) Total Number of atoms A TiO 2 lattice + 6577 TIP3P water 0 153.384 46.942 29.902 21531 B TiO 2 lattice + 9 LHP + 6550 TIP3P 0.00996 153.384 46.942 29.902 21512 C TiO 2 lattice + 91 LHP + 5950 TIP3P 0.10070 153.384 46.942 29.902 21288 D TiO 2 lattice + 910 LHP 1.00700 153.384 46.942 29.902 18780 E TiO 2 lattice + 91 LHP + 5950 TIP3P (fixed lattice). 0.10070 153.384 46.942 29.902 21288 We built the five systems starting from the TiO 2 lattice model previously described, using VMD [ 50 ] and its plugins PSFgen and Solvate. To model a titanium lattice immersed in water and LHP, we adopted a well consolidated and published force field [ 51 ] to describe atomic types and their interaction. For electronic interaction terms the total charge of the unitary cell system Ti 2 O 4 has been assigned to zero, while single atom charges have been obtained by standard Gasteiger’s method [ 52 ]. We have then performed Molecular Dynamics simulations of the five systems reported in Table 3 and Fig. 4 with NAMD, adopting a well-established protocol [ 53 , 54 ]. In detail, for all the minimization, heating, equilibration and production runs we used: the NVT ensemble with periodic boundary conditions in three dimensions, PME [ 55 ] for full system periodic electrostatic, a Langevin thermostat with damping coefficient equal to 1 ps -1 , and a standard velocity form of the Verlet method for integration, with a timestep of 2 fs. For all systems we have used the same simulation protocol, which synthetically consists of: (1) 1000 steps of minimization at 0K, (2) heating of the system for 30,000 steps with an increment in temperature of 0.01 K/step to reach the final temperature of 300K, (3) equilibration of the system for 2 ns (1,000,000 steps) and finally (4) a production run of 100 ns (50,000,000 steps). During the production run a system snapshot is saved every 10 ps, thus resulting in a total of 10,000 frames for a 100 ns run. The analysis has been then performed on a time series of 10,000 samples of the specific observables. Discussion Analysis of MD system trajectories. All the system trajectories have been analyzed by using the MDAnalysis tool [ 56 ] embedded into Python scripts to execute the MDAnalisys routines, perform a simple post processing and plot the results. The number of LHP molecules in contact with the TiO 2 lattice have been extracted from the trajectory files using an ad hoc python code. In the next sections, we will first present the results of individual analyses and then provide an overview. Water and LHP Densities Water density along the 𝑥 direction has been calculated using the MDAnalysis module LinearDensity. The 𝑥 direction is the direction of maximum extension of the box, with the TiO 2 lattice spanning from coordinate 𝑥=70.00 Å to 𝑥=83.38 Å. As expected, we observe for all the systems (Fig. 5 Left) that water density decreases to zero approaching 𝑥=70 Å and similarly on the other side at 𝑥=83 Å, meaning that water has not infiltrated the TiO 2 lattice but just wet the surface. Very close to the lattice surface there’s a slight increase of water molecules only for system A and B, while for system C there’s a net water molecule depletion as a specular effect of the higher concentration of LHP molecules close to the surface (Fig. 5 Right). The density of L-Hydroxyproline has been evaluated in the same way (Fig. 5 ) and also in this case it is evident that LHP molecules do not pass inside the TiO 2 lattice. Moreover, it is evident also that the density of LHP molecules tends to increase while going closer to the lattice surface. In particular, for system B and C, LHP molecules are only present essentially very close to the lattice surface, in a sense they “stick” to the surface. This is the first hint that the lattice surface is able to create bonds with LHP molecules and coordinate them according to some optimal packing, as will be confirmed and furtherly elucidated by subsequent analyses. In system D the LHP density is approximately constant along 𝑥 direction, with an increase close to lattice surface. This is surely due to the presence of just one liquid phase in system D, but could be a hint that we reached surface adsorption saturation. The LHP molecule stick-to-surface effect is particularly evident for system E (Fig. 5 Right). Radial Distribution Functions \(\) We have evaluated the Radial Distribution Functions (RDFs) for the five systems. RDFs represent the number of atoms coordinated by a given atom, at a certain radial distance, mediated along the whole MD trajectory [ 57 ]. They are of course symmetrical functions. RDFs provide detailed information on: i) lattice structure and lattice–water interactions, including hydration shells, ii) interaction between lattice and LHP molecules, iii) reciprocal water–LHP interactions. RDFs thus, in particular, allowed us to explore the organization of LHP molecules around the lattice. Moreover, RDFs offered the opportunity to re-validate the force field in our specific case, by evaluating the RDFs of the hydrated systems –particularly A– and checking that they confirm the correct lattice structure and topology and maintain these properties during the simulation. For simplicity, in order to build the RDFs associated with LHP, we considered only the unique N atom of LHP. We will present the resulting RDFs in the following sections (with some pictures in supplementary information), organized by the typology of the analyzed interaction (lattice–lattice, lattice–water, water–water, LHP–water, LHP–lattice, LHP–LHP). Lattice structure (Ti–Ti, Ti–O, O–O) The plots of the RDFs relative to the lattice structure (Ti–Ti, Ti–O and O–O) are reported in the supplementary information Fig. 1 S. In all the RDFs is immediately recognizable the distance pattern that characterizes the lattice unitary cell. Particularly for the Ti-Ti pair, the first shell is about at 1.6 Å and is quite sharp, while the two others shells are evident at 4.5 Å and 9 Å and are wider. The last shell is quite widespread because of the fluctuations due to lattice vibrations. This is evidence that the lattice loses the perfectly crystalline structure we started with (Fig. 4 )in favor of a less regular but still periodic conformation. This is also confirmed by the final system snapshot, where the lattice surface is evidently locally deformed (Fig. 12 , Center). Such a local deformation is coherent with the size of the lattice slab, with the lattice internal vibrations (particularly due to O atoms) and with the presence of the solvent. The persistence of the lattice structure, although less regular than the initial crystal model, is in accordance with the behavior of real titanium dioxide surfaces in water-based solutions. Interaction lattice-water (Ti–Ow, O–Ow, Ti–Hw, O–Hw) To highlight the lattice's ability to coordinate the water layers around its surfaces, in particular we specifically evaluated the RDF for the atom pair Ti–water O (Fig. 6 a), showing hydration shells around titanium at about 3.5, 4.5, 6 and 8 Å. The first shell is quite sharp while the others are wider and wider with increasing distances. The system E, with lattice fixed, does not show a well defined ability to coordinate water shells, but the first one. The RDF for the pair lattice O–water O (Fig. 6 b) shows a similar behavior, with a first sharp peak centered at 2.8 Å, and two wider shells at 5.3 and 7.3 Å. In this case, there's more clear evidence of the coordination ability of system E too. The same reasoning holds for Ti–water H (Fig. 6 c), with shells at 3.8 Å (sharp) and 8 Å (wider), and for lattice O–water H (Fig. 6 d), with shells at 2 Å (sharp), 3.1 and 6 Å (wider). Noticeably, the RDFs for the oxygen atom (on lattice or water) show a much sharper first shell than in the case of water H. Overall, systems A, B and C do not differ meaningfully although, as expected, the RDF is slightly higher at long ranges for systems containing more water molecules. All the graphs show a final plateau followed by a decrease around a radius of 20 Å. This demonstrates that the effect of the lattice surface does not extend beyond a radius of about 15 Å, confirming that the slab thickness is sufficient to organize water and that the relative box size is adequate to observe the decay of this influence. Water structure (Ow–Ow, Ow–Hw, Hw–Hw) For the case of the water–water RDF, there is no surprise, as can be seen in supplementary information Fig. 2 S, confirming the standard facts about water structure and organization. Interaction LHP-water (N–Ow, N–Hw) Quite more interestingly, water and LHP (Fig. 7 ) relate with two shells at 3 and 5.6 Å for water oxygen versus LHP nitrogen, and at 2 and 5.2 Å for water hydrogen versus LHP nitrogen. Peaks are higher for higher system water content and, as expected, the qualitative behavior is the same for all systems, including E too. Noticeably, secondary peaks are higher than the first one. Overall, we may interpret this as the ability of water and LHP to reciprocally interact in a way to convey the lattice–liquid interaction from surface/short to long ranges. Interaction lattice-LHP (Ti–N, O–N) From Fig. 8 it is evident the ability of the lattice to coordinate a well defined first shell of adsorbed LHP molecules and a sequence of system–dependent, less defined secondary shells, with higher peak values. RDF values roughly increase with water content in the system. For the Ti–N pair the first shell is located at about 3.8 Å (3.5 Å for system E), while for the O–N pair the first shell is very well defined and located at about 3 Å for all systems (but E). For the Ti–N pair, system E shows another well defined peak at about 6.4 Å. A secondary shell is also visible for system B at 5.8 Å and for system C at 7.2, Å, for the pair lattice O–LHP N RDF. As observed for lattice-water interactions, the radius range is sufficiently large to capture all the LHP shells, as evidenced by the RDF decay for all systems beyond a radius of about 15 Å. Comparing systems B and C, we observe that the higher the water content in the system, the greater the range of lattice-LHP interaction. Therefore, the presence of water appears to be relevant in propagating specifically lattice–LHP interactions to longer distances. Interaction LHP-LHP (N–N) Finally, the structure of the LHP organization in systems C, D, E (Fig. 9 ) shows a common wide peak centered at about 5.8 Å, with peak height decreasing with LHP content. The width of the peak is due to the size of the LHP molecule and to the fact we considered just one single atom (N) as its representative. System B shows a quite fluctuating behavior due to the poor statistics. A first, shorter peak, is present at about 3.5 Å but is well defined only for system E. Similar to what is observed for lattice–LHP interactions, LHP self-interactions can be seen as a mechanism for extending surface lattice effects to greater distances. Water Orientational Relaxation We also calculated a water autocorrelation property, namely the water orientational relaxation (WOR), as shown in supplementary information. The WOR provides an estimate of how quickly the water molecules are rotating or changing direction [ 58 ]. If the WOR varies little, we can assume that the water molecules are rotating or changing direction very slowly. As can be seen from Fig. 3 S in supplementary information, for our system the plot decay is very slow. Therefore we may conclude that the presence of the lattice significantly reduces the translational and rotational degrees of freedom of the water molecules. Joining this result to the interaction lattice-water analysis, we can conclude that the first adsorbed water layer is present, as evidenced by the density peaks for the various RDFs (Ti–Ow, O–Ow, Ti–Hw, O–Hw), and it is permanently "structured," as it is formed by water molecules that do change (rotating and translating) quite slowly, compared to the system time evolution scale. Number of LHP molecules in contact with the TiO 2 lattice We considered an LHP molecule “in contact” with the titanium lattice if at least one atom of LHP has a distance from the lattice surface within 1 Å. We checked that stronger conditions with smaller distance thresholds do not meaningfully change the results. To do the computation, the position of the titanium lattice surface is recalculated at every frame due to lattice fluctuations. Figure 10 shows the time evolution of the number of LHP molecules in contact with the lattice surface, for the four systems B, C and D. The average number of LHP molecules in contact with the lattice surface is 1.5, 10.1, 20.0 and 70.7 for system B (9 LHP), C, E (91 LHP) and D (910 LHP), respectively. Furthermore, as depicted in Fig. 10 , a slightly increasing trend is observed, indicating that, over time, a significant portion of the LHP molecules are becoming progressively more efficiently packed close to the lattice surface. For system D the lattice surface divided by the average number of LHP molecules in contact corresponds to 19.8 Å 2 . Comparing it to the LHP polar (topological) surface area of about 70 Å 2 we have a hint that the LHP packing on the surface is quite optimal, possibly reaching adsorption saturation. LHP Conformation and Orientation To assess if the LHP molecule undergoes conformational changes during the simulation, we evaluated the dihedral angle α between four specific atoms over time for each LHP. Specifically, we selected three atoms on the near-planar ring functional group (CD, N and CA) and one atom (C) on the longer tail (Fig. 11 Left). The time evolution of such dihedral angle α , averaged across all the examined LHP molecules, is reported in Fig. 11 for systems B, C and D. From a view inspection of the flat graphs in Fig. 11 it is then possible to say, with a higher statistical accuracy with the increasing number of LHP molecules, that the conformation of the LHP molecules is quite stable with an average dihedral angle of about 110 O (precisely: 111.5 O , 109.2 O and 109.8 O for for system B, C and D, respectively). After assessing that LHP did not undergo conformational changes during the simulation, we aimed to understand the preferred alignments of L-Hydroxyproline in response to the presence of the TiO 2 lattice surface. To accomplish this, we evaluated the orientation of LHP relative to the lattice surface. This was achieved by transforming the (𝑥,𝑦,𝓏) coordinates of each LHP's constituent atoms into bond-angle-torsion (BAT) coordinates using the BAT routine in the MDAnalysis tool [ 56 ]. In particular, in BAT coordinates, the external degrees of freedom of the LHP molecule are defined by a translation and a rotation in space. The translational degrees of freedom are the three (𝑥,𝑦,𝓏) coordinates of the molecular center of mass (CM). The rotational degrees of freedom are defined through the versor-angle representation (see Fig. 12 , Left for angles definition): the rotation versor points from the center of mass to a second atom, and is specified by the azimuthal and polar angles θ and φ , and a third angle ω provides the rotation of a third atom about the axis. The remaining bond and torsion coordinates represent the LHP internal degree of freedom of the LHP molecule, to which we are not interested and, in any case, absent a conformational change, the LHP molecule can be considered a “rigid body” for our purposes. The ranges for the three angles of the BAT representation are: [0,2 π ] for θ and [- π , π ] for φ and ω . While the time evolution of such new external coordinates along the time is not very informative, their distributions, as we will see, are extremely interesting. To have some insight on physical and geometrical meaning of the three angular coordinates, just think of a perfectly planar molecule, and suppose the interaction with the lattice is such that the molecular plane stays perfectly perpendicular to the lattice surface (that in our case is parallel to 𝓏𝑦 plane, being the lattice slab perpendicular to 𝑥 axis by system construction). In this case θ = π /2≃1.571 and ω = 0 mean that the molecular plane is exactly perpendicular to the lattice surface and parallel to 𝓏𝑥 plane. Due to the symmetry, angle φ can be any value in [- π , π ] (the versor does not change in this case). When the alignment is not perfect, of course the single angular value is replaced by an angular distribution centered on the average angle. We start discussing the angle distribution for system D (see Fig. 14 first column for distributions, Table 4 D (all) -row for mean values), the system with the higher number of LHP molecules and thus with the better statistics. The θ angle distribution for system D is a relatively narrow gaussian with mean value ⟨ θ ⟩≃1.58 (very close to π /2≃1.57) with a half-width (i.e. full width of the curve at half the maximum) of about Δ θ ≃0.6≃ π /5. The ω angle distribution is quite narrow, well centered around the average ⟨ ω ⟩≃0.00, with a half-width of about Δ ω ≃0.12≃π/26. As previously discussed, this is a clear hint that the near-planar ring structure of LHP is oriented preferentially almost perpendicularly to the lattice surface and parallel to 𝓏𝑥 plane. Therefore the LHP molecules are also preferentially aligned with all the planar rings parallels among them. Being this the average on minimum energy configuration, this is also a demonstration that in such a way the LHP molecule is able to exploit the formation of durable and strong hydrogen bondings with the lattice by its penetrating tails. Moreover, such LHP molecular orientation results in an evident optimal packing close to the lattice surface, in particular reducing ring sterical hindrance. A snapshot from the dynamics simulation confirms this analysis (Fig. 12 Left), showing all the LHP planar rings roughly perpendicular to the lattice surface and near-parallel among them. The φ angle distribution deserves a specific analysis. It is a narrow gaussian with angle mean value ⟨ φ ⟩≃-0.03≃- π /128 and half width of about Δ φ ≃0.15≃ π /21. Now, the LHP deviation from exact perpendicularity to the lattice surface (that is θ not exactly π /2) is mainly linked to the presence in the LHP structure of the two tails departing from the ring. The effect of this deviation, a kind of symmetry breaking , is that the value of the φ angle collapses from any possible value in its range to a single value φ ≃0, in such a way to maintains LHP orientation perpendicular to the lattice and parallel to 𝓏𝑥 plane. Therefore, also φ angle distribution is in line with the interpretation we gave previously. For system B, the same effects are observed (Fig. 13 Left, Table 4 ). In particular ⟨ θ ⟩≃1.54, but in this case, the smaller number of LHP molecules provide much less statistics and the gaussian half-widths are much larger, about ten times more than for system D. The same consideration holds for system C (Fig. 13 Center, Table 4 ), where ⟨ θ ⟩≃1.59, with the gaussian half-widths roughly twice larger than for system D. Finally system E (Fig. 13 Right, Table 4 ) shows the same gaussian distributions of system C (same number of LHP), with the most noticeable difference that the average θ is slightly higher: ⟨ θ ⟩≃1.66. Overall, angular distributions for all the systems confirm the following interpretation: the presence of the lattice surface orient LHP molecules such that the LHP ring is near-perpendicular to the lattice and shows a well defined orientation parallel to 𝓏𝑥 plane, that is all LHP have parallel ring planes. The lattice's ability to locally deform its surface, as evident from time evolution snapshots and RDFs, helped by the transition to a periodic but less rigid and regular structure, thus seems to play a role in proper optimal LHPs packing. To delve more in depth into this phenomenon, we analyzed the LHP molecules closer to the lattice surface, engaging the stronger interactions with the lattice. We focused on system D, given that LHP molecules are concentrated just on the lattice surface for both B and C systems (see LHP density plot in Fig. 5 ). Therefore, we considered the LHP molecules with center of mass distance less than 2 Å and 4 Å from the lattice surface for system D (Fig. 14 Right, Center, respectively). In this case, we observe the same qualitative global behavior as previously discussed, with a little but noticeable difference: ⟨ θ ⟩ and ⟨ φ ⟩ shift to slightly higher values (Fig. 14 , Table 4 ) and a similar behavior is observed for ⟨ ω ⟩ for D(all) and D(< 2Å). Moreover, the angular distributions half–widths do not meaningfully change, but for ω distribution. These facts means that: i) lattice coordinating ability propagates to longer distances, and ii) closer to the titanium lattice dioxide surface there’s a tendency of the LHP molecule to slightly deviate from the previously discussed orientation in such a way to take into account local surface variations (see Fig. 12 , Center). Table 4 Mean values φ , θ and ω angles for the LHP center of mass distance within 2 and 4 Å from lattice surface (indicated in the table as “<2Å” and “<4Å”) and for all the LHP molecules in the simulation box (indicated as “all”). As reference π /2≃1.57. System Mean φ Mean θ Mean ω B (all) -0.055409 1.538524 0.211060 C (all) 0.015809 1.586896 -0.026982 D (< 2Å) -0.22151 1.67029 0.079226 D (< 4Å) -0.21772 1.63154 -0.027438 D (all) -0.02823 1.584371 -0.0023771 E (all) 0.0369916 1.6560866 0.0943499 Overview The analysis firmly indicates that the (100) titanium dioxide surface effectively forms robust and durable bondings with LHP molecules at both short and long ranges. This occurs to different degrees but independently of the LHP concentration in the liquid phase and results in LHP optimal packing in the liquid bulk and particularly around the lattice surface. The organization of LHP molecules into a structured shell pattern around the lattice surface is evident from RDFs, with water and LHP densities revealing that LHP molecules tend to be primarily displaced in close proximity to the lattice surface, leading (when present) to water depletion to accommodate them. This is particularly evident for system C. In detail, the RDF analysis highlights the lattice's ability to coordinate, for the Ti–N pair, a first shell approximately at 3.8 Å (3.5 Å for system E), while for the O–N pair, the first shell is consistently well-defined at about 3 Å across all systems (but E). RDF values generally increase with water content, with secondary shells observed at specific distances, namely for system B at 5.8 Å and for system C at 7.2, Å. The optimal packing of LHP molecules close to the lattice surface is furtherly supported by orientation analysis and trajectory view inspections, revealing the near-perpendicular orientation of the LHP ring to the lattice surface and the near-parallel alignment among the LHP rings themselves. In this way, the LHP molecules are able to form strong and durable hydrogen bondings with the lattice atoms by exploiting their penetrating tail functional groups. Moreover, such LHP molecular orientation evidently reduces ring sterical hindrance effect, thus resulting in an optimal packing close to the lattice surface. System-specific analyses demonstrate that LHP closer proximity to the titanium dioxide surface prompts reciprocal accommodation between LHP molecules and lattice atoms. In fact, observing orientation angle distributions of LHP molecules within 2 Å and 4 Å from the surface, it is possible to see how LHP molecular orientation adjustments near the surface account for local surface variations due to lattice transition to a less regular yet periodic arrangement. Angular Gaussian distributions and RDFs in system D reveal that surface interactions extend to longer ranges via self-interactions among LHP molecules, thus orienting similarly all the LHP molecules in the liquid phase. Also systems B and C exhibit long-range contributions that in this case can be attributed to water-mediated interactions too. The number of LHP molecules in contact with the lattice slightly increases with simulation time, indicating improved dynamic packing of LHP molecules near the surface, resulting in stronger bondings and reduced system total energy. Again, this can be achieved by LHP and water molecules coordination jointly to local lattice surface rearrangement. Finally, the RDF analysis affirms that the molecular dynamics simulation accurately captures the titanium dioxide lattice structure and its interaction with water, providing a second level validation of the adopted force field. In particular the force field consistently reproduces the unitary cell interaction topology and reveals the usual water shells. WOR analysis confirms that the first water shell is also consistently structured for all systems, that is the water molecules do not undergo meaningful rotations or translations in time. Lattice-lattice interactions among systems A, B, C, and D are consistent, while lattice-water interactions show similar but slightly higher RDFs in systems with more water molecules. The latter aspect is evidence that the presence of LHP molecules does not significantly interfere with lattice ability to coordinate water shells. Water-LHP interactions reveal well-defined reciprocal shells, reinforcing the idea that LHP presence does not significantly interfere with lattice-water coordination capabilities. Conclusions In conclusion, our comprehensive MD simulations and analyses shed light on the dynamic behavior of the titanium dioxide lattice in the presence of L-hydroxyproline, a key collagen component. The trajectory analysis, particularly focusing on RDFs and LHP orientation, provided crucial insights into the adsorption of LHP molecules at lattice surfaces. First of all, our analysis indicates a lattice transition from an initial perfectly crystalline structure to less regular but regular structure, influenced by the internal vibrations and the presence of LHP and water molecules. This transition prompts local surface rearrangement that helps the optimal LHP packing. In particular, the radial distribution functions elucidated the intricate interactions within the system, emphasizing the lattice's ability to coordinate both water and LHP molecules in shells. The evaluation of water and LHP densities revealed water depletion to make room for LHP close to the surface of the TiO 2 lattice, with LHP exhibiting furtherly a preferential orientation, suggesting the formation of specific bonds. This observation aligns with the orientation analysis, confirming the coordination of LHP molecules by the lattice surfaces. Quite interestingly, the LHP analysis demonstrated a stable molecular conformation throughout the simulation, but a specific orientation with respect to lattice surface, thus emphasizing the TiO 2 lattice's role in organizing LHP molecules. In detail, the external angular rotation coordinates revealed a preferential alignment of the LHP rings near-perpendicular to the lattice surface and near-parallel among them. This is indicative of durable bondings formation both among LHPs and between LHPs and lattice atoms, the latter thanks to the extending LHP tail functional group. The lattice–driven organizing interactions extend from surface to the liquid phase bulk through LHP– and water–mediated contributions. Overall, our findings contribute valuable insights into the dynamic interplay between the TiO 2 lattice, water, and LHP molecules. This understanding is crucial for advancing biomaterial development, especially in the context of biomedical implants and nano–coatings, where optimizing adhesion and functionality are of paramount importance. The knowledge gained from this study can inform the design of nanotechnological coatings and new materials for enhanced biomedical applications. Declarations Funding This work was carried out with financial support from the Region of Sardinia (QCC&HPC project). The chemical characterization of byssus was performed in part with funding from "POR FESR Sardegna 2014 - 2020 Axis 1 Action 1.1.3, Aid for Research and Development Projects". Acknowledgments We thank the HPC group at CRS4. Conflict of interest The authors have no conflicts of interest to declare. Ethical Approval This declaration is not applicable. References Karamanos NK, Theocharis AD, Piperigkou Z et al (2021). A guide to the composition and functions of the extracellular matrix. The FEBS Journal, 288(24):6850-6912. https://doi.org/10.1111/febs.15776. Ottani V, Martini D, Franchi,M, Ruggeri A, Raspanti, M. (2002). Hierarchical structures in fibrillar collagens. Micron, 33(7-8):587-596. https://doi.org/10.1016/s0968-4328(02)00033-1. Gjaltema RA, Bank RA (2017). Molecular insights into prolyl and lysyl hydroxylation of fibrillar collagens in health and disease. Critical Reviews in Biochemistry and Molecular Biology, 52(1):74-95. https://doi.org/10.1080/10409238.2016.1269716. Mienaltowski MJ, Gonzales NL, Beall JM, Pechanec MY (2021). Basic Structure, Physiology, and Biochemistry of Connective Tissues and Extracellular Matrix Collagens. Advances in Experimental Medicine and Biology, 1348:5-43. https://doi.org/10.1007/978-3-030-80614-9_2. Silverman HG, Roberto FF (2007). Understanding Marine Mussel Adhesion. Marine Biotechnology, 9:661–681. https://doi.org/10.1007/s10126-007-9053-x. Krivosheeva O, Sababi M, Dedinaite A, Claesson PM (2013). Nanostructured composite layers of mussel adhesive protein and ceria nanoparticles. Langmuir. 29(30):9551-9561. https://doi.org/10.1021/la401693x. Bronk JK, Russell BH, Rivera J, Pasqualini R et al (2014). A multifunctional streptococcal collagen-mimetic protein coating prevents bacterial adhesion and promotes osteoid formation on titanium, Acta Biomaterialia, 10(7):3354-3362, https://doi.org/10.1016/j.actbio.2014.04.005. Bose S, Li S, Mele M, Williams CJ, Silberschmidt VV (2022). Stability and mechanical performance of collagen films under different environmental conditions, Polymer Degradation and Stability, 97:109853, https://doi.org/10.1016/j.polymdegradstab.2022.109853. Pandey N, Soto-Garcia LF, Liao J, Zimmern P, Nguyen KT, Hong Y (2020). Mussel-inspired bioadhesives in healthcare: design parameters, current trends, and future perspectives. Biomaterials Science, 8(5):1240-1255. https://doi.org/10.1039/c9bm01848d. Stich T, Alagboso F, Křenek T, Kovářík T, Alt V, Docheva D (2021). Implant-bone-interface: Reviewing the impact of titanium surface modifications on osteogenic processes in vitro and in vivo. Bioeng Transl Med. July 12;7(1):e10239. https://doi.org/10.1002/btm2.10239. Sowmiya M, Senthilkumar K (2016). Adsorption of proline, hydroxyproline and glycine on anatase (001) surface: a first-principle study. Theoretical Chemistry Accounts, 135(12):12. https://doi.org/10.1007/s00214-015-1783-7. Oliveira WF, Arrud, IRS, Silva GMM et al (2017). Functionalization of titanium dioxide nanotubes with biomolecules for biomedical applications. Materials Science and Engineering C, 81:597-606. https://doi.org/10.1016/j.msec.2017.08.017. Kulkarni M, Mazare A, Gongadze E, Perutkova et al (2015). Titanium nanostructures for biomedical applications. Nanotechnology, 26(6):062002. https://doi.org/10.1088/0957-4484/26/6/062002. Yin ZF, Wu L, Yang HG, Su YH (2013). Recent progress in biomedical applications of titanium dioxide. Physical Chemistry Chemical Physics, 15(14):4844-4858. https://doi.org/10.1039/c3cp43938k. Bharadwaz A, Jayasuriya AC (2020). Recent trends in the application of widely used natural and synthetic polymer nanocomposites in bone tissue regeneration. Materials Science and Engineering: C, 110, 110698. https://doi.org/10.1016/j.msec.2020.110698. Wang K, Jin H, Song Q, Huo J, Zhang J, Li P (2021). Titanium dioxide nanotubes as drug carriers for infection control and osteogenesis of bone implants. Drug Delivery and Translational Research, 11(4), 1456-1474. https://doi.org/10.1007/s13346-021-00980-z. Ulubayram K, Calamak S, Shahbazi R, Eroglu I (2015). Nanofibers based antibacterial drug design, delivery, and applications. Current Pharmaceutical Design, 21(15):1930-1943. https://doi.org/10.2174/1381612821666150302151804. Pelgrift RY, Friedman AJ (2013). Nanotechnology as a therapeutic tool to combat microbial resistance. Advanced Drug Delivery Reviews, 65(13-14):1803-1815. https://doi.org/10.1016/j.addr.2013.07.011. Colombo JS., Satoshi S, Okazaki J, Crean SJ, Sloan AJ, Waddington RJ (2012). In vivo monitoring of the bone healing process around different titanium alloy implant surfaces placed into fresh extraction sockets. Journal of Dentistry, 40(4):338-346. https://doi.org/10.1016/j.jdent.2012.01.010. Mueller CK, Thorwarth M, Schmidt M, Schlegel KA, Schultze-Mosgau S (2011). Comparative analysis of osseointegration of titanium implants with acid-etched surfaces and different biomolecular coatings. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics, 112(6):726-736. https://doi.org/10.1016/j.tripleo.2011.01.004. Klinger MM, Rahemtulla F, Prince CW, Lucas LC, Lemons JE (1998). Proteoglycans at the bone-implant interface. Critical Reviews in Oral Biology & Medicine, 9(4):449-463. doi:10.1177/10454411980090040401. Sheng X, Wang A, Wang Z, Liu H, Wang J, Li C (2022). Advanced Surface Modification for 3D-Printed Titanium Alloy Implant Interface Functionalization. Frontiers in Bioengineering and Biotechnology, 10:850110. https://doi.org/10.3389/fbioe.2022.850110. Chouirfa H, Bouloussa H, Migonney V, Falentin-Daudré C (2019). Review of titanium surface modification techniques and coatings for antibacterial applications. Acta Biomaterialia, 83:37-54. https://doi.org/10.1016/j.actbio.2018.10.036. Souza JCM, Sordi MB, Kanazawa M, Ravindran S et al (2019). Nano-scale modification of titanium implant surfaces to enhance osseointegration. Acta Biomaterialia, 94:112-131. https://doi.org/10.1016/j.actbio.2019.05.045. Rupp F, Liang L, Geis-Gerstorfer J, Scheideler L, Hüttig F (2018). Surface characteristics of dental implants: A review. Dental Materials, 34(1):40-57. https://doi.org/10.1016/j.dental.2017.09.007. Su Y, Luo C, Zhang Z, Hermawan H, Zhu D, Huang J et al (2017). Bioinspired surface functionalization of metallic biomaterials. Journal of the Mechanical Behavior of Biomedical Materials, 77:90-105. https://doi.org/10.1016/j.jmbbm.2017.08.035. Costa D, Garrain PA, Baaden M (2013). Understanding small biomolecule-biomaterial interactions: A review of fundamental theoretical and experimental approaches for biomolecule interactions with inorganic surfaces. Journal of Biomedical Materials Research Part A, 101A(6):1210-1222. Monti S (2007). Molecular Dynamics Simulations of Collagen-like Peptide Adsorption on Titanium-Based Material Surfaces. The Journal of Physical Chemistry C, 111(16):6086-6094. https://doi.org/10.1021/jp070266t. Butt, HJ, Berger R, Bonaccurso E, Chen Y, Wang, J (2007). Impact of atomic force microscopy on interface and colloid science. Advances in Colloid and Interface Science, 133(2):91-104. https://doi.org/10.1016/j.cis.2007.06.001 Strasser S, Zink A, Janko M, Heckl WM, Thalhammer S (2007). Structural investigations on native collagen type I fibrils using AFM. Biochemical and Biophysical Research Communications, 354(1):27-32. Köppen S, Langel W (2010). Simulation of adhesion forces and energies of peptides on titanium dioxide surfaces. Langmuir. 26(19):15248-56. https://doi.org/10.1021/la101687m. Wei B, Tielens F, Calatayud M (2019). Understanding the Role of Rutile TiO2 Surface Orientation on Molecular Hydrogen Activation. Nanomaterials, 9(9):1199. https://doi.org/10.3390/nano9091199. Yanamala N., Kagan VE, Shvedova AA (2013). Molecular modeling in structural nano-toxicology: interactions of nanoparticles with nano-machinery of cells. Advances in Drug Delivery Reviews, 65(15):2070-2077. https://doi.org/10.1016/j.addr.2013.05.005. Selvaraj C, Sakkiah S, Tong W, Hong H (2018). Molecular dynamics simulations and applications in computational toxicology and nanotoxicology. Food and Chemical Toxicology, 112:495-506. https://doi.org/10.1016/j.fct.2017.08.028. YazdanYar, A, Aschauer U, Bowen P (2018). Interaction of biologically relevant ions and organic molecules with titanium oxide (rutile) surfaces: A review on molecular dynamics studies. Colloids and Surfaces B: Biointerfaces, 161:563-577. https://doi.org/10.1016/j.colsurfb.2017.11.004. Eloh K, Sasanelli N, Maxia A, Caboni P (2016). Untargeted Metabolomics of Tomato Plants after Root-Knot Nematode Infestation. J Agric Food Chem. 64(29):5963-5968. https://doi.org/10.1021/acs.jafc.6b02181. Fais G, Malavasi V, Scano P. et al (2021). Metabolomics and lipid profile analysis of Coccomyxa melkonianii SCCA 048. Extremophiles 25:357–368. https://doi.org/10.1007/s00792-021-01234-z. Stein SE (1999). An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J. Am. Soc. Mass Spectrom. 10(8):770−781. https://doi.org/10.1016/S1044-0305(99)00047-1. Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel et al. (2005), GMD@CSB. DB: TheGolm metabolome database. Bioinformatics,21(8):1635−1638. https://doi.org/10.1093/bioinformatics/bti236. Adams RP (2007). Identification of Essential Oil Components by GasChromatography/Mass Spectrometry. Allured Publishing Corporation Carol Stream, 4th Edition. Rodriguez F, Moràn L, Gonzales G et al. (2017). Collagen extraction from mussel byssus: a new marine collagen source with physicochemical properties of industrial interest. Journal of Food Science and Technology, 54:1228-1238. https://doi.org/10.1007/s13197-017-2566-z. Sousa da Silva AW, Vranken WF. (2012) ACPYPE - AnteChamber PYthon Parser interfacE. BMC Res Notes 5:367. https://doi.org/10.1186/1756-0500-5-367. Kagami, LP, Sousa da Silva AW, Diaz A, Vranken W (2023). The ACPYPE web server for small-molecule MD topology generation, Bioinformatics, 39(6):btad350, https://doi.org/10.1093/bioinformatics/btad350. Vanommeslaeghe K, Hatcher E, Acharya C. et al. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 2009;31:671–90. 10.1002/jcc.21367.45. Khitrova VI, Bundule M, Pinsker ZG (1977). An electron-diffraction investigation of titanium dioxide in thin films. Kristallografiya, 22:1253-1258. Momma K and Izumi F (2011). VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data. J. Appl. Crystallogr., 44: 1272-1276. Phillips JC, Hardy DJ, Maia JDC, Stone JE, et al. (2020). Scalable molecular dynamics on CPU and GPU architectures with NAMD. Journal of Chemical Physics, 153:044130, https://doi.org/doi:10.1063/5.0014475. Aksimentiev A, Comer J (2011). Bionanotechnology Tutorial, https://www.ks.uiuc.edu/Training/Tutorials/science/bionano/bionano-tutorial.pdf L Martinez, R Andrade, EG Birgin, JM Martinez. (2009) Packmol: A package for building initial configurations for molecular dynamics simulations. Journal of Computational Chemistry, 30, 2157-2164, https://doi.org/10.1002/jcc.21224. Humphrey W, Dalke A, and Schulten K (1996). VMD - Visual Molecular Dynamics, J. Molec. Graphics, 14(1):33-38. Brandt EG, and Alexander P. Lyubartsev AP (2015). Systematic Optimization of a force field for Classical Simulations of TiO2–Water Interfaces. The Journal of Physical Chemistry C 119(32):18110-18125. https://doi.org/10.1021/acs.jpcc.5b02669. Gasteiger J, Marsili M (1980). Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges, Tetrahedron, 36(22):3219-3228. https://doi.org/10.1016/0040-4020(80)80168-2. Kumar A, Melis P, Genna V, Cocco E, Marrosu MG, Pieroni E (2014). Antigenic peptide molecular recognition by the DRB1-DQB1 haplotype modulates multiple sclerosis susceptibility. Mol Biosyst. 2014 Aug;10(8):2043-54. https://doi.org/10.1039/c4mb00203b. Valentini M, Pieroni E, Concas A, Pisu M (2022). Docking and Molecular Dynamics Simulations Reveal a Possible Site of Interaction of Olive Leaf Extract Hydroxytyrosol with Polyunsaturated Fatty Acid 5-Lipoxygenase Human Enzyme". European Journal of Biology and Biotechnology, 3(2): 6-11. Ewald PP (1921). Die berechnung optischer und electrostatischer gitterpotentiale, Ann. Phys. 64:253-287; translated by A. Cornell, Atomics International Library, 1964. Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O (2011). MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. J. Comput. Chem. 32: 2319–2327. doi:10.1002/jcc.21787. Gavagnin E (2018). Pair correlation functions for identifying spatial correlation in discrete domains. Physical Review E. 97(1):062104. https://doi.org/10.1103/PhysRevE.97.062104. Yeh YL, and Chung-Yuan Mou CY (1999). Orientational Relaxation Dynamics of Liquid Water Studied by Molecular Dynamics Simulation, J. Phys. Chem. B, 103:3699-3705. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformations.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jul, 2024 Read the published version in BioNanoScience → Version 1 posted Editorial decision: Revision requested 13 Jun, 2024 Reviews received at journal 13 Jun, 2024 Reviewers agreed at journal 08 Jun, 2024 Reviews received at journal 08 Jun, 2024 Reviewers agreed at journal 02 Jun, 2024 Reviewers invited by journal 14 May, 2024 Editor assigned by journal 14 May, 2024 Submission checks completed at journal 14 May, 2024 First submitted to journal 10 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4400232","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304470211,"identity":"9f90d839-fdb3-47ee-81ec-fa64a6ff3aed","order_by":0,"name":"Maria Valentini","email":"","orcid":"","institution":"Center for Advanced Studies Research and Development in Sardinia","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Valentini","suffix":""},{"id":304470212,"identity":"ad1f6146-cf51-4c78-99df-c58a4d236fb6","order_by":1,"name":"Pierluigi Caboni","email":"","orcid":"","institution":"University of Cagliari","correspondingAuthor":false,"prefix":"","firstName":"Pierluigi","middleName":"","lastName":"Caboni","suffix":""},{"id":304470213,"identity":"6d3f1baf-8e48-4d5e-8814-4ac43914d382","order_by":2,"name":"Giovanni Sanna","email":"","orcid":"","institution":"QNT Nano","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Sanna","suffix":""},{"id":304470214,"identity":"9d70e79f-1fa4-4206-a4b5-31017593b4c7","order_by":3,"name":"Massimo Pisu","email":"","orcid":"","institution":"Center for Advanced Studies Research and Development in Sardinia","correspondingAuthor":false,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Pisu","suffix":""},{"id":304470215,"identity":"9fd0d89b-0d21-466c-9c29-cfb5d34c1578","order_by":4,"name":"Enrico Pieroni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAkElEQVRIiWNgGAWjYFAC5gNAgg0IiQdsCUCCj4GNBD08BkBCjoF4a/ilez5+rvhjxsAn30CkFsk5ZzdLnm1LI8FhBjdytzE2NhwjQYv9jZxnjA1//pNii0QOG2MDUD3xWiTuHDOWbGxj42EDBzYxgH9288OPDX/Y5OSbDxBtDYTiIVY9QssoGAWjYBSMAtwAANkbHr/bPIVBAAAAAElFTkSuQmCC","orcid":"","institution":"Center for Advanced Studies Research and Development in Sardinia","correspondingAuthor":true,"prefix":"","firstName":"Enrico","middleName":"","lastName":"Pieroni","suffix":""}],"badges":[],"createdAt":"2024-05-10 10:55:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4400232/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4400232/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12668-024-01559-x","type":"published","date":"2024-07-30T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57023843,"identity":"3a70e4d4-6876-425e-90cf-150516aa47ea","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109885,"visible":true,"origin":"","legend":"\u003cp\u003eGC-MS analysis of the byssus extract. The chromatographic peaks represent each metabolite, the retention time in minutes is reported on the abscissa and the instrumental response in arbitrary units on the ordinate. The arrows evidentiate the selected relevant metabolites.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/7823cd06deaf720aacfb4e42.png"},{"id":57023845,"identity":"990a8a07-1af4-4fbf-b446-018c3a90a453","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79337,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D structures (central: “balls and sticks” and right: just “stick” representation) of L-Hydroxyproline. The stick representation shows that the functional ring group is near-planar and there are two chemical groups departing from it (hereby referred to as tails) with angles roughly 90\u003csup\u003eO\u003c/sup\u003e and 110\u003csup\u003eO\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/987a6d4663aa5e48e5635c4f.png"},{"id":57023848,"identity":"f5109d28-0b51-4a06-beb3-593c438630da","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53566,"visible":true,"origin":"","legend":"\u003cp\u003eRutile titanium dioxide unitary cell. Oxygen in red and Titanium in pale pink.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/279e20ae4290cbdd4cc7c45c.png"},{"id":57023852,"identity":"7a304983-c0e6-4905-96c0-50b592aec093","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":822023,"visible":true,"origin":"","legend":"\u003cp\u003eStarting configurations for the five systems described in \u003cstrong\u003eTable 3\u003c/strong\u003e. The horizontal direction represents the 𝑥 axis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/be2e941232215b4ac0e22691.png"},{"id":57023844,"identity":"55310a98-531a-45cd-bb91-e452917c29f5","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86942,"visible":true,"origin":"","legend":"\u003cp\u003eWater density along the 𝑥 direction for systems A, B, C and E (left) and LHP density along the 𝑥 direction for system B, C, D and E\u003cstrong\u003e \u003c/strong\u003e(right).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/f68772594ebbd913e5c9e50f.png"},{"id":57023857,"identity":"ed6915d6-91f8-4427-943d-81503137749b","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":187931,"visible":true,"origin":"","legend":"\u003cp\u003eRDF for the pairs A) Ti–Ow, B) O–Ow, C) Ti–Hw RDF and D) O–Hw.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/d436fc67c2934d8f85e80360.png"},{"id":57023856,"identity":"7945b88b-025e-4c24-b339-d4448cdf5f21","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":84235,"visible":true,"origin":"","legend":"\u003cp\u003eRDFs for the pairs left) Ow–N and right) Hw–N\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/9b172f2e56d19665dbf03e3c.png"},{"id":57023854,"identity":"a9e38b0d-3989-429f-b012-57ee93bc26fd","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":74302,"visible":true,"origin":"","legend":"\u003cp\u003eRDFs for the pairs left) Ti–N and right) O–N\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/731c529469c7a8cbd3d411ee.png"},{"id":57023849,"identity":"274cab77-64df-4dde-823e-9be77dc87cd0","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":65836,"visible":true,"origin":"","legend":"\u003cp\u003eRDF for the N–N pair.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/dc203db17c617dd9e296c486.png"},{"id":57024462,"identity":"3a269a8a-299e-44f0-a3a6-8d4f59afa299","added_by":"auto","created_at":"2024-05-23 14:35:50","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":58714,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal evolution of the number of LHP molecules in contact with the titanium dioxide lattice surface, for all the systems. Average values of the number of LHP in contact with the lattice are: 0.92, 6.16 and 57.43 for B, C and D, respectively.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/e166d66f2eff26839448f0ad.png"},{"id":57023847,"identity":"abe87413-335e-425e-9c6e-9490e239a1e0","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":142179,"visible":true,"origin":"","legend":"\u003cp\u003eOn the left: dihedral angle plots for system B, C and D (top to bottom). On the right: the LHP dihedral angle α is defined by the four atoms N, CD, CA, C.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/7eec2c6a8c943189298e8780.png"},{"id":57023858,"identity":"1c6a50ae-ec71-420e-a4f3-c08abf56ce24","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":466768,"visible":true,"origin":"","legend":"\u003cp\u003eLeft: Definition of angle\u0026nbsp; \u003cem\u003eθ\u003c/em\u003e, \u003cem\u003eφ\u003c/em\u003e, \u003cem\u003eω\u003c/em\u003e superimposed to the LHP molecule. Molecule position and orientation is obtained by CM coordinates and the three angles: \u003cem\u003eθ\u003c/em\u003e, \u003cem\u003eφ\u003c/em\u003e defining the versor around which rotate and\u0026nbsp; \u003cem\u003eω\u003c/em\u003e defining the rotation angle around the versor. Notice that the lattice slab is perpendicular to the 𝑥 axis. Center: lattice surrounded by LHP molecules (system D).\u0026nbsp; Right: snapshot from MD simulation, with Titanium in pale gray, Oxygen in red , LHP residues in cyan. Only LHP residues within 10 Å from TIO\u003csub\u003e2\u003c/sub\u003e residues are visualized (system D).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/9b75215b3894b8b9291c458b.png"},{"id":57023853,"identity":"7819bcc0-faad-40b6-9323-398c60094351","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":25800,"visible":true,"origin":"","legend":"\u003cp\u003eExternal angular rotation distributions for system B, C, E (left to right). Dashed vertical line indicates the average angle value.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/27a2dbe71a525df41a194979.png"},{"id":57024463,"identity":"b72e5b99-4197-49d2-a3f2-d3023b9e2fcf","added_by":"auto","created_at":"2024-05-23 14:35:50","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":74487,"visible":true,"origin":"","legend":"\u003cp\u003eLeft: External angular distributions for system D. Dashed vertical line indicates the average angle value. Center (Right): angular distributions for system D and LHP molecules with CM within 4 Å (2 Å) from the lattice surface.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/b651d844d6893c7553a5d85b.png"},{"id":61793479,"identity":"407b77c4-9cad-40cf-beeb-f81ac1906ef6","added_by":"auto","created_at":"2024-08-05 16:13:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3384475,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/fd6aa64c-c815-4ec4-8865-765a3c313889.pdf"},{"id":57023846,"identity":"887969c0-2c18-40f8-b714-fc99d00336c4","added_by":"auto","created_at":"2024-05-23 14:27:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":741518,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformations.docx","url":"https://assets-eu.researchsquare.com/files/rs-4400232/v1/a60e843b338f964c12320172.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling the Interaction of L-Hydroxyproline, a Constituent of Collagen, with a Hydrated TiO2 lattice at Varied Concentrations: Examining Surface and Long-Range Effects","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCollagen stands out as a key constituent within the extracellular matrix, serving as a structural protein in connective tissues, skin, and bones. Its primary building blocks include proline, hydroxyproline, and glycine [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with proline emerging as an indispensable amino acid prominently present in collagen. In particular, proline's unique structure plays a key role in contributing to stabilization and overall integrity of connective tissues and hydroxylation of proline residues in collagen is vital for its stability. In fact, the latter post-translational modification enhances intermolecular interactions, reinforcing the collagen structure [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn biological organisms, collagen proteins provide exceptional adhesion properties [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, the embedding of collagen components in nanostructured materials and nano-lattices have been extensively studied with the aim to design new materials with improved properties, such as adhesion and mechanical resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], antibacterial characteristics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and surface protection [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this context, in recent years it is particularly relevant for the health sector the research of bioadhesives with improved adhesion and biological compatibility [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn fact, titanium is the principal material employed nowadays in biomedical applications, biofunctionalization and covering of medical implants, above all dental and orthopedic ones [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], due to its biocompatibility and the unique crystal structures with highly reactive surfaces. Therefore the study of (hydroxy)proline\u0026ndash;titanium lattice interaction is important to understand the adhesion between biological tissues and medical implant materials, to improve implant features, such as biocompatibility, biointegration, mechanical properties and non toxicity, and ultimately to avoid implant loosening, infection or rejection by immune system reaction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTitanium dioxide exists in multiple crystal structures, with the most common being rutile and anatase. These structures impart unique properties, such as high surface area and photocatalytic activity, to TiO\u003csub\u003e2\u003c/sub\u003e. TiO\u003csub\u003e2\u003c/sub\u003e surfaces are widely known for their reactivity, and their interaction with biomolecules is heavily influenced by surface properties, with functional groups on TiO\u003csub\u003e2\u003c/sub\u003e surfaces that can engage in hydrogen bonding and electrostatic interactions with biomolecules, particularly with hydroxyl groups on hydroxylated proline residues [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, titanium dioxide is widely used in biomedical applications due to its excellent biocompatibility. For instance, it is often employed in implants, dental materials, and drug delivery systems [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. More precisely, TiO\u003csub\u003e2\u003c/sub\u003e nanoparticles can be incorporated into drug delivery systems, where interaction with collagen may influence the release kinetics of encapsulated substances, providing a platform for controlled drug delivery. TiO\u003csub\u003e2\u003c/sub\u003e surfaces, when appropriately modified, could serve as substrates for biomineralization processes, influencing the deposition of hydroxyapatite or other mineral phases. This is relevant for applications in bone tissue engineering [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In detail, modified TiO\u003csub\u003e2\u003c/sub\u003e surfaces can provide a platform for the controlled deposition of collagen, facilitating cell adhesion and tissue regeneration. Finally TiO\u003csub\u003e2\u003c/sub\u003e's photocatalytic properties have applications in sterilization and decontamination, with implications for wound healing and antimicrobial applications [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn general, the interaction collagen\u0026ndash;lattice may influence the adsorption and alignment of collagen on TiO\u003csub\u003e2\u003c/sub\u003e surfaces [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, the surface of TiO\u003csub\u003e2\u003c/sub\u003e can be modified by introducing specific functional groups that enhance interactions with collagen. This tailored surface chemistry can influence the adhesion, proliferation, and differentiation of cells interacting with the biomaterial [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to the complexity and variety of the involved biological systems, the experimental studies of the interaction between biological environment and nanostructured materials are quite difficult to set up and to interpret, while computational studies, particularly Molecular Dynamics (MD), can be more effective and offer the opportunity to analyze specific steps in multi-step interaction mechanisms at specific temporal and spatial scales [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Adhesion forces among collagene and titanium dioxide layers may be experimentally evaluated through Atomic Force Microscopy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These results were effectively reproduced by restrained MD techniques [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], showing how the interface between titanium surface and collagen peptides is controlled by local interaction between peptide amino acid side chains and localized surface charge (of Ti, O or OH), particularly for those aminoacids charged at physiological pH conditions. To optimize titanium layer adsorption or adhesion it is also important to analyze the different roles played by distinct titanium dioxide surface orientations. In this case, e.g. quantum DFT simulations were used to prove that the (110) surface is particularly efficient in hydrogen activation and thus in collagen protein adsorption, mainly controlled by specific collagen amino acids, namely glycine, proline and hydroxyproline [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe interaction at the interface between nano-structured components and biological systems is also extremely important in nanotoxicology, to assess the risk for the environment and for both human and animal health [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this field, the adopted computational modeling techniques also include MD simulations, that e.g. were able to show how biomolecule adsorption on titanium dioxide lattices is truly effective at physiological pH conditions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The authors find that molecular modeling is able to show how the interaction is dominated by titanium surface characteristics, including defects and contaminations, and by biomolecular structure and conformational changes, above all functional groups orientation with respect to the surface [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As previously mentioned, all these details are not easily obtainable from experiments.\u003c/p\u003e \u003cp\u003eWe conclude by synthesizing that the interaction between collagen components, particularly proline, and titanium dioxide lattice is a multifaceted topic with implications for biomaterials, tissue engineering, biomedical applications and new materials. Exploring and understanding these interactions opens avenues for the development of advanced materials with tailored properties for specific needs. In the meantime simulations may provide the opportunities to confirm and explain experimental results, identify novel underlying molecular mechanisms and help to design new materials for biomedical and industrial applications.\u003c/p\u003e \u003cp\u003eThe presented research work is inserted in a wider applied research activity that includes the development of innovative nanotechnological coatings, by exploiting agri-food production wastes and byproducts, and nanotoxicology studies on environment and exposed people.\u003c/p\u003e \u003cp\u003eIn this activity, observing the mussel's exceptional adhesion abilities, we decided to chemically characterize the byssus obtained from the mussels, a waste product of the mussel farming industry, in order to recognize the relevant molecules. Then, we simulated the previously identified molecules, mixed in various concentrations in an hydrated titanium dioxide lattice, with the aim to understand the impact on lattice characteristics and improve nano\u0026ndash;coating functionalization and to define in future the optimal biomolecule mixing to commercial products.\u003c/p\u003e"},{"header":"Materials and Methods: chemical characterization and modeling","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eByssus sample preparation, GC-MS Analysis and mixture preparation\u003c/h2\u003e \u003cp\u003eThe byssus samples were dried in an oven at a temperature of 70\u0026deg;C for 5 days. The dried samples were then finely ground by means of a cryogenic ball miller to obtain an impalpable powder. These fine byssus samples were then prepared for gas chromatographic analysis combined with mass spectrometry (GC-MS). An aliquot of the fine byssus (100 mg, 6 samples) was weighed and placed in an Eppendorf tube to which 125 \u0026micro;L of chloroform and 250 \u0026micro;L of methanol were then added for the extraction of molecular constituents [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Subsequently the samples were left to stand for 1 h at room temperature and during this time they were shaken at regular intervals of 15 min. A further 380 \u0026micro;L of chloroform and 90 \u0026micro;L of aqueous potassium chloride 0.2 N were then added and then the samples were centrifuged for 10 min at 24100 rcf. After centrifugation we observed the separation of the two phases: the aqueous phase in the upper part of the Eppendorf tube and the organic phase in the lower one with a rather compact interface. Using a variable volume micropipette, 200 \u0026micro;L of aqueous solution and 200 \u0026micro;L of organic solution were collected and both samples were placed in glass containers (vials) for subsequent analyzes. The contents of the samples were then brought to dryness under a gentle nitrogen flow and, subsequently, 70 \u0026micro;L of a mixture of 0.01 g of methylhydroxylamine in 1 ml anhydrous pyridine were added to each sample. The samples were then left to stand for 17 hours, at the end of which each sample was functionalized to be analyzed by GC-MS with the addition of 50 \u0026micro;L of N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA). The samples were then shaken and left to stand for 1 hour at room temperature. Finally, 500 \u0026micro;L of hexane were added for GC-MS analysis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe analysis was performed using a gas chromatograph combined with a triple quadrupole mass spectrometer (Thermofisher TSQ9000 GC-MSMS) equipped with an autosampler for liquids. The capillary column was in DB-5MS fused silica (30 m \u0026times; 0.250 mm, film thickness 0.250 \u0026micro;m). The injector and the interface were at a temperature of 280\u0026deg;C. The temperature of the transfer line and of the ion source were 250\u0026deg;C and 300\u0026deg;C, respectively. The ions were generated at 70 eV with the electron impact ionization method and were recorded at 1.6 scans/s over the m/z mass range 50\u0026ndash;550. The oven temperature was programmed as follows: 1 minute at constant 60\u0026deg;C temperature and then temperature constantly increased to 300\u0026deg;C in 25 min, and then temperature again left stable for 1 min. Helium was used as the carrier gas with a flow of 1mL/min, 1\u0026micro;L of sample was injected in splitless mode [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRaw GC-MS data files were database-searched against the National Institute of Standards and Technology (NIST) Mass SpectralDatabase (2.0) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and Golm metabolome database [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConfirmation of sample metabolites was performed by (a) comparison of their relative retention times and mass fragmentation to those of pure standards and (b) computer matching against NIST as well as retention indices as calculated according to Kovats for alkanes C9\u0026thinsp;\u0026minus;\u0026thinsp;C36 compared to those reported by [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, to obtain the powders to be mixed to the nanotechnological coats, the byssus samples were dried in an oven at a temperature of 70\u0026deg;C for 5 days. The dried samples were then finely ground by means of a cryogenic ball mill. This system allowed the sample to be cooled to the point of friability by liquid nitrogen and thus made the powders quite fine. In fact, liquid nitrogen is ideal for cryogenic freezing of material before adding to paints because it can reach nearly \u0026minus;\u0026thinsp;195\u0026deg;C, allowing the user to provide a steady stream of extremely cold liquid to freeze materials subjected to grinding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. GC-MS analysis of the byssus extract. The chromatographic peaks represent each metabolite, the retention time in minutes is reported on the abscissa and the instrumental response in arbitrary units on the ordinate. The arrows evidentiate the selected relevant metabolites.\u003c/p\u003e \u003cp\u003eThree metabolites, in particular, have been identified as the most relevant and thus interesting for the development of the new mixtures: Glycine, L-Proline and L-Hydroxyproline, reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As previously observed, these three amino acids represent the main components of the collagen protein, for humans as for mussels [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetabolite list. In the table are reported the retention time and the percentage in mass with respect to the full sample. The related three peaks on the chromatogram are evidenced in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOMPOUND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-proline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Hydroxyproline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eL-hydroxyproline Model\u003c/h2\u003e \u003cp\u003eAmong all molecules identified, the most relevant are L-hydroxyproline (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) and L-proline. In the extracellular matrix, proline is enzymatically transformed into L-hydroxyproline, thus L-Hydroxyproline represents the proline form most active \u003cem\u003ein-vivo\u003c/em\u003e and we thus decided to model its interaction with titanium lattice. From now on we will indicate the L-hydroxyproline molecule (C\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e) by LHP. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the main chemical data for LHP and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows its 2D and 3D structures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eL-Hydroxyproline main chemical characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubChem CID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5810\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMolecular Formula\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e5\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMolecular Weight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIUPAC Name\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2S,4R)-4-hydroxypyrrolidine-2-carboxylic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe structure of L-Hydroxyproline was retrieved in the ChemSpider database, with entry id CSID:5605. The atomic coordinates were stored in MOL file format. We have then used the ACPYPE server realized by Sousa da Silva and coworkers [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] to obtain the PDB file completed with H atoms. The topology and force field parameter file for CHARMM General force field [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] was also obtained by the same web server. All these files can be retrieved in the supplementary information.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTitanium Dioxide Lattice Model\u003c/h2\u003e \u003cp\u003eOur model for the dioxide titanium lattice has been realized in the following way. The unit cell of rutile TiO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was obtained from the Crystallography Open Database [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], entry id COD:1530150. The unit cell dimensions along \u0026#119909;, \u0026#119910; and \u0026#120015; direction are, respectively, 4.59 \u0026Aring;, 4.59 \u0026Aring; and 2.96 \u0026Aring;, resulting in a volume of 62.362 \u0026Aring;\u003csup\u003e3\u003c/sup\u003e and a density of 4.25 g/cm\u003csup\u003e3\u003c/sup\u003e. The atomic coordinates were stored in CIF format. The unit cell for the 100 surface orientation of the rutile crystal structure has been identified through VESTA [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and saved in a PDB format file. It contains 2 Ti and 4 O atoms. The model of the TIO\u003csub\u003e2\u003c/sub\u003e lattice has been built using the NAMD tool [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and specifically, the script \u003cem\u003ereplicate_crystal.tcl\u003c/em\u003e [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]: starting from the PDB file of the unit cell and replicating it 3 times along the \u0026#119909; direction and 10 times along both \u0026#119910; and \u0026#120015; directions. In this way we model a titanium dioxide slab, because we are mainly interested in surface effects rather than bulk ones. The final lattice has thus 600 Ti atoms and 1200 O atoms inserted in a box with sizes [13.384 \u0026Aring;, 46.942 \u0026Aring;, 29.902 \u0026Aring;]. The density of the resulting boxed TiO\u003csub\u003e2\u003c/sub\u003e lattice is 4.234 g/cm\u003csup\u003e3\u003c/sup\u003e which is in line with the density of the unit cell and the TiO\u003csub\u003e2\u003c/sub\u003e in nature. The replication process from the unit cell maintained the lattice in the 100 surface orientation of the rutile phase. The CHARMM force field topology file, that contains information about the bounds between atoms, necessary to run simulations by using the NAMD tool, was manually generated in agreement to the single unit cell parameters. Topology and force field parameter files are included in the supplementary information.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eThe Titanium dioxide lattice was placed in the center of the simulation box, with the minimum thickness along \u0026#119909; direction, and 70 \u0026Aring; of liquid phase with density 1.00 g/cm\u003csup\u003e3\u003c/sup\u003e (water and LHP) were added on each side along \u0026#119909; direction using PACKMOL [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. We obtained four systems with specific proportion water:LHP, and the resulting system overall density was in all cases 1.29 g/cm\u003csup\u003e3\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The number of LHP and water molecules in the system were calibrated to explore LHP concentration in the liquid phase ranging from 0 to 1 g/cm\u003csup\u003e3\u003c/sup\u003e, namely we selected four systems with LHP concentration in the liquid phase equal to 1.00, 0.10, 0.01 and 0.00 g/cm\u003csup\u003e3\u003c/sup\u003e. Notice that a fifth system has been added, with intermediate 0.10 g/cm\u003csup\u003e3\u003c/sup\u003e LHP concentration but titanium lattice kept fixed during dynamics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSizes and densities of the five simulated systems.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLHP concentration in the liquid phase (g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eSimulation Box dimensions \u003c/p\u003e \u003cp\u003eLx, Ly, Lz (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal Number of atoms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e lattice\u0026thinsp;+\u0026thinsp;6577 TIP3P water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e lattice\u0026thinsp;+\u0026thinsp;9 LHP\u0026thinsp;+\u0026thinsp;6550 TIP3P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e lattice\u0026thinsp;+\u0026thinsp;91 LHP\u0026thinsp;+\u0026thinsp;5950 TIP3P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e lattice\u0026thinsp;+\u0026thinsp;910 LHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e lattice\u0026thinsp;+\u0026thinsp;91 LHP\u0026thinsp;+\u0026thinsp;5950 TIP3P (fixed lattice).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe built the five systems starting from the TiO\u003csub\u003e2\u003c/sub\u003e lattice model previously described, using VMD [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and its plugins PSFgen and Solvate.\u003c/p\u003e \u003cp\u003eTo model a titanium lattice immersed in water and LHP, we adopted a well consolidated and published force field [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] to describe atomic types and their interaction. For electronic interaction terms the total charge of the unitary cell system Ti\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e has been assigned to zero, while single atom charges have been obtained by standard Gasteiger\u0026rsquo;s method [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. We have then performed Molecular Dynamics simulations of the five systems reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e with NAMD, adopting a well-established protocol [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In detail, for all the minimization, heating, equilibration and production runs we used: the NVT ensemble with periodic boundary conditions in three dimensions, PME [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] for full system periodic electrostatic, a Langevin thermostat with damping coefficient equal to 1 ps\u003csup\u003e-1\u003c/sup\u003e, and a standard velocity form of the Verlet method for integration, with a timestep of 2 fs. For all systems we have used the same simulation protocol, which synthetically consists of: (1) 1000 steps of minimization at 0K, (2) heating of the system for 30,000 steps with an increment in temperature of 0.01 K/step to reach the final temperature of 300K, (3) equilibration of the system for 2 ns (1,000,000 steps) and finally (4) a production run of 100 ns (50,000,000 steps). During the production run a system snapshot is saved every 10 ps, thus resulting in a total of 10,000 frames for a 100 ns run. The analysis has been then performed on a time series of 10,000 samples of the specific observables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eAnalysis of MD system trajectories.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll the system trajectories have been analyzed by using the MDAnalysis tool [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] embedded into Python scripts to execute the MDAnalisys routines, perform a simple post processing and plot the results. The number of LHP molecules in contact with the TiO\u003csub\u003e2\u003c/sub\u003e lattice have been extracted from the trajectory files using an ad hoc python code. In the next sections, we will first present the results of individual analyses and then provide an overview.\u003c/p\u003e\n\u003ch3\u003eWater and LHP Densities\u003c/h3\u003e\n\u003cp\u003eWater density along the \u0026#119909; direction has been calculated using the MDAnalysis module LinearDensity. The \u0026#119909; direction is the direction of maximum extension of the box, with the TiO\u003csub\u003e2\u003c/sub\u003e lattice spanning from coordinate \u0026#119909;=70.00 \u0026Aring; to \u0026#119909;=83.38 \u0026Aring;. As expected, we observe for all the systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e Left) that water density decreases to zero approaching \u0026#119909;=70 \u0026Aring; and similarly on the other side at \u0026#119909;=83 \u0026Aring;, meaning that water has not infiltrated the TiO\u003csub\u003e2\u003c/sub\u003e lattice but just wet the surface. Very close to the lattice surface there\u0026rsquo;s a slight increase of water molecules only for system A and B, while for system C there\u0026rsquo;s a net water molecule depletion as a specular effect of the higher concentration of LHP molecules close to the surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e Right). The density of L-Hydroxyproline has been evaluated in the same way (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and also in this case it is evident that LHP molecules do not pass inside the TiO\u003csub\u003e2\u003c/sub\u003e lattice. Moreover, it is evident also that the density of LHP molecules tends to increase while going closer to the lattice surface. In particular, for system B and C, LHP molecules are only present essentially very close to the lattice surface, in a sense they \u0026ldquo;stick\u0026rdquo; to the surface. This is the first hint that the lattice surface is able to create bonds with LHP molecules and coordinate them according to some optimal packing, as will be confirmed and furtherly elucidated by subsequent analyses. In system D the LHP density is approximately constant along \u0026#119909; direction, with an increase close to lattice surface. This is surely due to the presence of just one liquid phase in system D, but could be a hint that we reached surface adsorption saturation. The LHP molecule stick-to-surface effect is particularly evident for system E (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e Right).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRadial Distribution Functions \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eWe have evaluated the Radial Distribution Functions (RDFs) for the five systems. RDFs represent the number of atoms coordinated by a given atom, at a certain radial distance, mediated along the whole MD trajectory [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. They are of course symmetrical functions. RDFs provide detailed information on: i) lattice structure and lattice\u0026ndash;water interactions, including hydration shells, ii) interaction between lattice and LHP molecules, iii) reciprocal water\u0026ndash;LHP interactions. RDFs thus, in particular, allowed us to explore the organization of LHP molecules around the lattice. Moreover, RDFs offered the opportunity to re-validate the force field in our specific case, by evaluating the RDFs of the hydrated systems \u0026ndash;particularly A\u0026ndash; and checking that they confirm the correct lattice structure and topology and maintain these properties during the simulation. For simplicity, in order to build the RDFs associated with LHP, we considered only the unique N atom of LHP. We will present the resulting RDFs in the following sections (with some pictures in supplementary information), organized by the typology of the analyzed interaction (lattice\u0026ndash;lattice, lattice\u0026ndash;water, water\u0026ndash;water, LHP\u0026ndash;water, LHP\u0026ndash;lattice, LHP\u0026ndash;LHP).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLattice structure (Ti\u0026ndash;Ti, Ti\u0026ndash;O, O\u0026ndash;O)\u003c/h2\u003e \u003cp\u003eThe plots of the RDFs relative to the lattice structure (Ti\u0026ndash;Ti, Ti\u0026ndash;O and O\u0026ndash;O) are reported in the supplementary information Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS. In all the RDFs is immediately recognizable the distance pattern that characterizes the lattice unitary cell. Particularly for the Ti-Ti pair, the first shell is about at 1.6 \u0026Aring; and is quite sharp, while the two others shells are evident at 4.5 \u0026Aring; and 9 \u0026Aring; and are wider. The last shell is quite widespread because of the fluctuations due to lattice vibrations. This is evidence that the lattice loses the perfectly crystalline structure we started with (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)in favor of a less regular but still periodic conformation. This is also confirmed by the final system snapshot, where the lattice surface is evidently locally deformed (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, Center). Such a local deformation is coherent with the size of the lattice slab, with the lattice internal vibrations (particularly due to O atoms) and with the presence of the solvent. The persistence of the lattice structure, although less regular than the initial crystal model, is in accordance with the behavior of real titanium dioxide surfaces in water-based solutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInteraction lattice-water (Ti\u0026ndash;Ow, O\u0026ndash;Ow, Ti\u0026ndash;Hw, O\u0026ndash;Hw)\u003c/h2\u003e \u003cp\u003eTo highlight the lattice's ability to coordinate the water layers around its surfaces, in particular we specifically evaluated the RDF for the atom pair Ti\u0026ndash;water O (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), showing hydration shells around titanium at about 3.5, 4.5, 6 and 8 \u0026Aring;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first shell is quite sharp while the others are wider and wider with increasing distances. The system E, with lattice fixed, does not show a well defined ability to coordinate water shells, but the first one. The RDF for the pair lattice O\u0026ndash;water O (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) shows a similar behavior, with a first sharp peak centered at 2.8 \u0026Aring;, and two wider shells at 5.3 and 7.3 \u0026Aring;. In this case, there's more clear evidence of the coordination ability of system E too. The same reasoning holds for Ti\u0026ndash;water H (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), with shells at 3.8 \u0026Aring; (sharp) and 8 \u0026Aring; (wider), and for lattice O\u0026ndash;water H (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), with shells at 2 \u0026Aring; (sharp), 3.1 and 6 \u0026Aring; (wider). Noticeably, the RDFs for the oxygen atom (on lattice or water) show a much sharper first shell than in the case of water H. Overall, systems A, B and C do not differ meaningfully although, as expected, the RDF is slightly higher at long ranges for systems containing more water molecules. All the graphs show a final plateau followed by a decrease around a radius of 20 \u0026Aring;. This demonstrates that the effect of the lattice surface does not extend beyond a radius of about 15 \u0026Aring;, confirming that the slab thickness is sufficient to organize water and that the relative box size is adequate to observe the decay of this influence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWater structure (Ow\u0026ndash;Ow, Ow\u0026ndash;Hw, Hw\u0026ndash;Hw)\u003c/h2\u003e \u003cp\u003eFor the case of the water\u0026ndash;water RDF, there is no surprise, as can be seen in supplementary information Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eS, confirming the standard facts about water structure and organization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInteraction LHP-water (N\u0026ndash;Ow, N\u0026ndash;Hw)\u003c/h2\u003e \u003cp\u003eQuite more interestingly, water and LHP (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) relate with two shells at 3 and 5.6 \u0026Aring; for water oxygen versus LHP nitrogen, and at 2 and 5.2 \u0026Aring; for water hydrogen versus LHP nitrogen. Peaks are higher for higher system water content and, as expected, the qualitative behavior is the same for all systems, including E too. Noticeably, secondary peaks are higher than the first one. Overall, we may interpret this as the ability of water and LHP to reciprocally interact in a way to convey the lattice\u0026ndash;liquid interaction from surface/short to long ranges.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInteraction lattice-LHP (Ti\u0026ndash;N, O\u0026ndash;N)\u003c/h2\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e it is evident the ability of the lattice to coordinate a well defined first shell of adsorbed LHP molecules and a sequence of system\u0026ndash;dependent, less defined secondary shells, with higher peak values. RDF values roughly increase with water content in the system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the Ti\u0026ndash;N pair the first shell is located at about 3.8 \u0026Aring; (3.5 \u0026Aring; for system E), while for the O\u0026ndash;N pair the first shell is very well defined and located at about 3 \u0026Aring; for all systems (but E). For the Ti\u0026ndash;N pair, system E shows another well defined peak at about 6.4 \u0026Aring;. A secondary shell is also visible for system B at 5.8 \u0026Aring; and for system C at 7.2, \u0026Aring;, for the pair lattice O\u0026ndash;LHP N RDF. As observed for lattice-water interactions, the radius range is sufficiently large to capture all the LHP shells, as evidenced by the RDF decay for all systems beyond a radius of about 15 \u0026Aring;. Comparing systems B and C, we observe that the higher the water content in the system, the greater the range of lattice-LHP interaction. Therefore, the presence of water appears to be relevant in propagating specifically lattice\u0026ndash;LHP interactions to longer distances.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eInteraction LHP-LHP (N\u0026ndash;N)\u003c/h2\u003e \u003cp\u003eFinally, the structure of the LHP organization in systems C, D, E (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) shows a common wide peak centered at about 5.8 \u0026Aring;, with peak height decreasing with LHP content.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe width of the peak is due to the size of the LHP molecule and to the fact we considered just one single atom (N) as its representative. System B shows a quite fluctuating behavior due to the poor statistics. A first, shorter peak, is present at about 3.5 \u0026Aring; but is well defined only for system E. Similar to what is observed for lattice\u0026ndash;LHP interactions, LHP self-interactions can be seen as a mechanism for extending surface lattice effects to greater distances.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eWater Orientational Relaxation\u003c/h2\u003e \u003cp\u003eWe also calculated a water autocorrelation property, namely the water orientational relaxation (WOR), as shown in supplementary information. The WOR provides an estimate of how quickly the water molecules are rotating or changing direction [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. If the WOR varies little, we can assume that the water molecules are rotating or changing direction very slowly. As can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eS in supplementary information, for our system the plot decay is very slow. Therefore we may conclude that the presence of the lattice significantly reduces the translational and rotational degrees of freedom of the water molecules. Joining this result to the interaction lattice-water analysis, we can conclude that the first adsorbed water layer is present, as evidenced by the density peaks for the various RDFs (Ti\u0026ndash;Ow, O\u0026ndash;Ow, Ti\u0026ndash;Hw, O\u0026ndash;Hw), and it is permanently \"structured,\" as it is formed by water molecules that do change (rotating and translating) quite slowly, compared to the system time evolution scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNumber of LHP molecules in contact with the TiO\u003csub\u003e2\u003c/sub\u003e lattice\u003c/h2\u003e \u003cp\u003eWe considered an LHP molecule \u0026ldquo;in contact\u0026rdquo; with the titanium lattice if at least one atom of LHP has a distance from the lattice surface within 1 \u0026Aring;. We checked that stronger conditions with smaller distance thresholds do not meaningfully change the results. To do the computation, the position of the titanium lattice surface is recalculated at every frame due to lattice fluctuations. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the time evolution of the number of LHP molecules in contact with the lattice surface, for the four systems B, C and D. The average number of LHP molecules in contact with the lattice surface is 1.5, 10.1, 20.0 and 70.7 for system B (9 LHP), C, E (91 LHP) and D (910 LHP), respectively. Furthermore, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, a slightly increasing trend is observed, indicating that, over time, a significant portion of the LHP molecules are becoming progressively more efficiently packed close to the lattice surface.\u003c/p\u003e \u003cp\u003eFor system D the lattice surface divided by the average number of LHP molecules in contact corresponds to 19.8 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e. Comparing it to the LHP polar (topological) surface area of about 70 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e we have a hint that the LHP packing on the surface is quite optimal, possibly reaching adsorption saturation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLHP Conformation and Orientation\u003c/h2\u003e \u003cp\u003eTo assess if the LHP molecule undergoes conformational changes during the simulation, we evaluated the dihedral angle α between four specific atoms over time for each LHP. Specifically, we selected three atoms on the near-planar ring functional group (CD, N and CA) and one atom (C) on the longer tail (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e Left). The time evolution of such dihedral angle \u003cem\u003eα\u003c/em\u003e, averaged across all the examined LHP molecules, is reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e for systems B, C and D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom a view inspection of the \u003cem\u003eflat\u003c/em\u003e graphs in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e it is then possible to say, with a higher statistical accuracy with the increasing number of LHP molecules, that the conformation of the LHP molecules is quite stable with an average dihedral angle of about 110\u003csup\u003eO\u003c/sup\u003e (precisely: 111.5\u003csup\u003eO\u003c/sup\u003e, 109.2\u003csup\u003eO\u003c/sup\u003e and 109.8\u003csup\u003eO\u003c/sup\u003e for for system B, C and D, respectively).\u003c/p\u003e \u003cp\u003eAfter assessing that LHP did not undergo conformational changes during the simulation, we aimed to understand the preferred alignments of L-Hydroxyproline in response to the presence of the TiO\u003csub\u003e2\u003c/sub\u003e lattice surface. To accomplish this, we evaluated the orientation of LHP relative to the lattice surface. This was achieved by transforming the (\u0026#119909;,\u0026#119910;,\u0026#120015;) coordinates of each LHP's constituent atoms into bond-angle-torsion (BAT) coordinates using the BAT routine in the MDAnalysis tool [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In particular, in BAT coordinates, the external degrees of freedom of the LHP molecule are defined by a translation and a rotation in space. The translational degrees of freedom are the three (\u0026#119909;,\u0026#119910;,\u0026#120015;) coordinates of the molecular center of mass (CM). The rotational degrees of freedom are defined through the versor-angle representation (see Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, Left for angles definition): the rotation versor points from the center of mass to a second atom, and is specified by the azimuthal and polar angles \u003cem\u003eθ\u003c/em\u003e and \u003cem\u003eφ\u003c/em\u003e, and a third angle \u003cem\u003eω\u003c/em\u003e provides the rotation of a third atom about the axis. The remaining bond and torsion coordinates represent the LHP internal degree of freedom of the LHP molecule, to which we are not interested and, in any case, absent a conformational change, the LHP molecule can be considered a \u0026ldquo;rigid body\u0026rdquo; for our purposes. The ranges for the three angles of the BAT representation are: [0,2\u003cem\u003eπ\u003c/em\u003e] for \u003cem\u003eθ\u003c/em\u003e and [-\u003cem\u003eπ\u003c/em\u003e,\u003cem\u003eπ\u003c/em\u003e] for \u003cem\u003eφ\u003c/em\u003e and \u003cem\u003eω\u003c/em\u003e. While the time evolution of such new external coordinates along the time is not very informative, their distributions, as we will see, are extremely interesting.\u003c/p\u003e \u003cp\u003eTo have some insight on physical and geometrical meaning of the three angular coordinates, just think of a perfectly planar molecule, and suppose the interaction with the lattice is such that the molecular plane stays perfectly perpendicular to the lattice surface (that in our case is parallel to \u0026#120015;\u0026#119910; plane, being the lattice slab perpendicular to \u0026#119909; axis by system construction). In this case \u003cem\u003eθ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eπ\u003c/em\u003e/2≃1.571 and \u003cem\u003eω\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0 mean that the molecular plane is exactly perpendicular to the lattice surface and parallel to \u0026#120015;\u0026#119909; plane. Due to the symmetry, angle \u003cem\u003eφ\u003c/em\u003e can be any value in [-\u003cem\u003eπ\u003c/em\u003e,\u003cem\u003eπ\u003c/em\u003e] (the versor does not change in this case). When the alignment is not perfect, of course the single angular value is replaced by an angular distribution centered on the average angle.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe start discussing the angle distribution for system D (see Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e first column for distributions, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cem\u003e(all)\u003c/em\u003e-row for mean values), the system with the higher number of LHP molecules and thus with the better statistics. The \u003cem\u003eθ\u003c/em\u003e angle distribution for system D is a relatively narrow gaussian with mean value ⟨\u003cem\u003eθ\u003c/em\u003e⟩≃1.58 (very close to \u003cem\u003eπ\u003c/em\u003e/2≃1.57) with a half-width (i.e. full width of the curve at half the maximum) of about Δ\u003cem\u003eθ\u003c/em\u003e≃0.6≃\u003cem\u003eπ\u003c/em\u003e/5. The \u003cem\u003eω\u003c/em\u003e angle distribution is quite narrow, well centered around the average ⟨\u003cem\u003eω\u003c/em\u003e⟩≃0.00, with a half-width of about Δ\u003cem\u003eω\u003c/em\u003e≃0.12≃π/26. As previously discussed, this is a clear hint that the near-planar ring structure of LHP is oriented preferentially almost perpendicularly to the lattice surface and parallel to \u0026#120015;\u0026#119909; plane. Therefore the LHP molecules are also preferentially aligned with all the planar rings parallels among them. Being this the average on minimum energy configuration, this is also a demonstration that in such a way the LHP molecule is able to exploit the formation of durable and strong hydrogen bondings with the lattice by its penetrating tails. Moreover, such LHP molecular orientation results in an evident optimal packing close to the lattice surface, in particular reducing ring sterical hindrance. A snapshot from the dynamics simulation confirms this analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e Left), showing all the LHP planar rings roughly perpendicular to the lattice surface and near-parallel among them. The \u003cem\u003eφ\u003c/em\u003e angle distribution deserves a specific analysis. It is a narrow gaussian with angle mean value ⟨\u003cem\u003eφ\u003c/em\u003e⟩≃-0.03≃-\u003cem\u003eπ\u003c/em\u003e/128 and half width of about Δ\u003cem\u003eφ\u003c/em\u003e≃0.15≃\u003cem\u003eπ\u003c/em\u003e/21. Now, the LHP deviation from exact perpendicularity to the lattice surface (that is \u003cem\u003eθ\u003c/em\u003e not exactly \u003cem\u003eπ\u003c/em\u003e/2) is mainly linked to the presence in the LHP structure of the two tails departing from the ring. The effect of this deviation, a kind of \u003cem\u003esymmetry breaking\u003c/em\u003e, is that the value of the \u003cem\u003eφ\u003c/em\u003e angle collapses from any possible value in its range to a single value \u003cem\u003eφ\u003c/em\u003e≃0, in such a way to maintains LHP orientation perpendicular to the lattice and parallel to \u0026#120015;\u0026#119909; plane. Therefore, also \u003cem\u003eφ\u003c/em\u003e angle distribution is in line with the interpretation we gave previously.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor system B, the same effects are observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e Left, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In particular ⟨\u003cem\u003eθ\u003c/em\u003e⟩≃1.54, but in this case, the smaller number of LHP molecules provide much less statistics and the gaussian half-widths are much larger, about ten times more than for system D. The same consideration holds for system C (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e Center, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), where ⟨\u003cem\u003eθ\u003c/em\u003e⟩≃1.59, with the gaussian half-widths roughly twice larger than for system D. Finally system E (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e Right, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) shows the same gaussian distributions of system C (same number of LHP), with the most noticeable difference that the average \u003cem\u003eθ\u003c/em\u003e is slightly higher: ⟨\u003cem\u003eθ\u003c/em\u003e⟩≃1.66.\u003c/p\u003e \u003cp\u003eOverall, angular distributions for all the systems confirm the following interpretation: the presence of the lattice surface orient LHP molecules such that the LHP ring is near-perpendicular to the lattice and shows a well defined orientation parallel to \u0026#120015;\u0026#119909; plane, that is all LHP have parallel ring planes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe lattice's ability to locally deform its surface, as evident from time evolution snapshots and RDFs, helped by the transition to a periodic but less rigid and regular structure, thus seems to play a role in proper optimal LHPs packing. To delve more in depth into this phenomenon, we analyzed the LHP molecules closer to the lattice surface, engaging the stronger interactions with the lattice. We focused on system D, given that LHP molecules are concentrated just on the lattice surface for both B and C systems (see LHP density plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Therefore, we considered the LHP molecules with center of mass distance less than 2 \u0026Aring; and 4 \u0026Aring; from the lattice surface for system D (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e Right, Center, respectively). In this case, we observe the same qualitative global behavior as previously discussed, with a little but noticeable difference: ⟨\u003cem\u003eθ\u003c/em\u003e⟩ and ⟨\u003cem\u003eφ\u003c/em\u003e⟩ shift to slightly higher values (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and a similar behavior is observed for ⟨\u003cem\u003eω\u003c/em\u003e⟩ for D(all) and D(\u0026lt;\u0026thinsp;2\u0026Aring;). Moreover, the angular distributions half\u0026ndash;widths do not meaningfully change, but for \u003cem\u003eω\u003c/em\u003e distribution. These facts means that: i) lattice coordinating ability propagates to longer distances, and ii) closer to the titanium lattice dioxide surface there\u0026rsquo;s a tendency of the LHP molecule to slightly deviate from the previously discussed orientation in such a way to take into account local surface variations (see Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, Center).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean values \u003cem\u003eφ\u003c/em\u003e, \u003cem\u003eθ\u003c/em\u003e and \u003cem\u003eω\u003c/em\u003e angles for the LHP center of mass distance within 2 and 4 \u0026Aring; from lattice surface (indicated in the table as \u0026ldquo;\u0026lt;2\u0026Aring;\u0026rdquo; and \u0026ldquo;\u0026lt;4\u0026Aring;\u0026rdquo;) and for all the LHP molecules in the simulation box (indicated as \u0026ldquo;all\u0026rdquo;). As reference \u003cem\u003eπ\u003c/em\u003e/2≃1.57.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean \u003cem\u003eφ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean \u003cem\u003eθ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean \u003cem\u003eω\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB (all)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.055409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.538524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.211060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC (all)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.586896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.026982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD (\u0026lt;\u0026thinsp;2\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.22151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD (\u0026lt;\u0026thinsp;4\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.21772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.63154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.027438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD (all)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.584371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0023771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE (all)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0369916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6560866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0943499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOverview\u003c/h2\u003e \u003cp\u003eThe analysis firmly indicates that the (100) titanium dioxide surface effectively forms robust and durable bondings with LHP molecules at both short and long ranges. This occurs to different degrees but independently of the LHP concentration in the liquid phase and results in LHP optimal packing in the liquid bulk and particularly around the lattice surface. The organization of LHP molecules into a structured shell pattern around the lattice surface is evident from RDFs, with water and LHP densities revealing that LHP molecules tend to be primarily displaced in close proximity to the lattice surface, leading (when present) to water depletion to accommodate them. This is particularly evident for system C. In detail, the RDF analysis highlights the lattice's ability to coordinate, for the Ti\u0026ndash;N pair, a first shell approximately at 3.8 \u0026Aring; (3.5 \u0026Aring; for system E), while for the O\u0026ndash;N pair, the first shell is consistently well-defined at about 3 \u0026Aring; across all systems (but E). RDF values generally increase with water content, with secondary shells observed at specific distances, namely for system B at 5.8 \u0026Aring; and for system C at 7.2, \u0026Aring;. The optimal packing of LHP molecules close to the lattice surface is furtherly supported by orientation analysis and trajectory view inspections, revealing the near-perpendicular orientation of the LHP ring to the lattice surface and the near-parallel alignment among the LHP rings themselves. In this way, the LHP molecules are able to form strong and durable hydrogen bondings with the lattice atoms by exploiting their penetrating tail functional groups. Moreover, such LHP molecular orientation evidently reduces ring sterical hindrance effect, thus resulting in an optimal packing close to the lattice surface. System-specific analyses demonstrate that LHP closer proximity to the titanium dioxide surface prompts reciprocal accommodation between LHP molecules and lattice atoms. In fact, observing orientation angle distributions of LHP molecules within 2 \u0026Aring; and 4 \u0026Aring; from the surface, it is possible to see how LHP molecular orientation adjustments near the surface account for local surface variations due to lattice transition to a less regular yet periodic arrangement. Angular Gaussian distributions and RDFs in system D reveal that surface interactions extend to longer ranges via self-interactions among LHP molecules, thus orienting similarly all the LHP molecules in the liquid phase. Also systems B and C exhibit long-range contributions that in this case can be attributed to water-mediated interactions too.\u003c/p\u003e \u003cp\u003eThe number of LHP molecules in contact with the lattice slightly increases with simulation time, indicating improved dynamic packing of LHP molecules near the surface, resulting in stronger bondings and reduced system total energy. Again, this can be achieved by LHP and water molecules coordination jointly to local lattice surface rearrangement.\u003c/p\u003e \u003cp\u003eFinally, the RDF analysis affirms that the molecular dynamics simulation accurately captures the titanium dioxide lattice structure and its interaction with water, providing a second level validation of the adopted force field. In particular the force field consistently reproduces the unitary cell interaction topology and reveals the usual water shells. WOR analysis confirms that the first water shell is also consistently structured for all systems, that is the water molecules do not undergo meaningful rotations or translations in time. Lattice-lattice interactions among systems A, B, C, and D are consistent, while lattice-water interactions show similar but slightly higher RDFs in systems with more water molecules. The latter aspect is evidence that the presence of LHP molecules does not significantly interfere with lattice ability to coordinate water shells. Water-LHP interactions reveal well-defined reciprocal shells, reinforcing the idea that LHP presence does not significantly interfere with lattice-water coordination capabilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our comprehensive MD simulations and analyses shed light on the dynamic behavior of the titanium dioxide lattice in the presence of L-hydroxyproline, a key collagen component. The trajectory analysis, particularly focusing on RDFs and LHP orientation, provided crucial insights into the adsorption of LHP molecules at lattice surfaces. First of all, our analysis indicates a lattice transition from an initial perfectly crystalline structure to less regular but regular structure, influenced by the internal vibrations and the presence of LHP and water molecules. This transition prompts local surface rearrangement that helps the optimal LHP packing. In particular, the radial distribution functions elucidated the intricate interactions within the system, emphasizing the lattice's ability to coordinate both water and LHP molecules in shells. The evaluation of water and LHP densities revealed water depletion to make room for LHP close to the surface of the TiO\u003csub\u003e2\u003c/sub\u003e lattice, with LHP exhibiting furtherly a preferential orientation, suggesting the formation of specific bonds. This observation aligns with the orientation analysis, confirming the coordination of LHP molecules by the lattice surfaces. Quite interestingly, the LHP analysis demonstrated a stable molecular conformation throughout the simulation, but a specific orientation with respect to lattice surface, thus emphasizing the TiO\u003csub\u003e2\u003c/sub\u003e lattice's role in organizing LHP molecules. In detail, the external angular rotation coordinates revealed a preferential alignment of the LHP rings near-perpendicular to the lattice surface and near-parallel among them. This is indicative of durable bondings formation both among LHPs and between LHPs and lattice atoms, the latter thanks to the extending LHP tail functional group. The lattice\u0026ndash;driven organizing interactions extend from surface to the liquid phase bulk through LHP\u0026ndash; and water\u0026ndash;mediated contributions.\u003c/p\u003e \u003cp\u003eOverall, our findings contribute valuable insights into the dynamic interplay between the TiO\u003csub\u003e2\u003c/sub\u003e lattice, water, and LHP molecules. This understanding is crucial for advancing biomaterial development, especially in the context of biomedical implants and nano\u0026ndash;coatings, where optimizing adhesion and functionality are of paramount importance. The knowledge gained from this study can inform the design of nanotechnological coatings and new materials for enhanced biomedical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was carried out with financial support from the Region of Sardinia (QCC\u0026amp;HPC project). The chemical characterization of byssus was performed in part with funding from \u0026quot;POR FESR Sardegna 2014 - 2020 Axis 1 Action 1.1.3, Aid for Research and Development Projects\u0026quot;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the HPC group at CRS4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis declaration is not applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKaramanos NK, Theocharis AD, Piperigkou Z et al (2021). A guide to the composition and functions of the extracellular matrix. The FEBS Journal, 288(24):6850-6912. https://doi.org/10.1111/febs.15776.\u003c/li\u003e\n\u003cli\u003eOttani V, Martini D, Franchi,M, Ruggeri A, Raspanti, M. (2002). Hierarchical structures in fibrillar collagens. Micron, 33(7-8):587-596. https://doi.org/10.1016/s0968-4328(02)00033-1. \u003c/li\u003e\n\u003cli\u003eGjaltema RA, Bank RA (2017). Molecular insights into prolyl and lysyl hydroxylation of fibrillar collagens in health and disease. Critical Reviews in Biochemistry and Molecular Biology, 52(1):74-95. https://doi.org/10.1080/10409238.2016.1269716.\u003c/li\u003e\n\u003cli\u003eMienaltowski MJ, Gonzales NL, Beall JM, Pechanec MY (2021). Basic Structure, Physiology, and Biochemistry of Connective Tissues and Extracellular Matrix Collagens. Advances in Experimental Medicine and Biology, 1348:5-43. https://doi.org/10.1007/978-3-030-80614-9_2. \u003c/li\u003e\n\u003cli\u003eSilverman HG, Roberto FF (2007). Understanding Marine Mussel Adhesion. Marine Biotechnology, 9:661\u0026ndash;681. https://doi.org/10.1007/s10126-007-9053-x.\u003c/li\u003e\n\u003cli\u003eKrivosheeva O, Sababi M, Dedinaite A, Claesson PM (2013). Nanostructured composite layers of mussel adhesive protein and ceria nanoparticles. Langmuir. 29(30):9551-9561. https://doi.org/10.1021/la401693x. \u003c/li\u003e\n\u003cli\u003eBronk JK, Russell BH, Rivera J, Pasqualini R et al (2014). A multifunctional streptococcal collagen-mimetic protein coating prevents bacterial adhesion and promotes osteoid formation on titanium, Acta Biomaterialia, 10(7):3354-3362, https://doi.org/10.1016/j.actbio.2014.04.005.\u003c/li\u003e\n\u003cli\u003eBose S, Li S, Mele M, Williams CJ, Silberschmidt VV (2022). Stability and mechanical performance of collagen films under different environmental conditions, Polymer Degradation and Stability, 97:109853, https://doi.org/10.1016/j.polymdegradstab.2022.109853.\u003c/li\u003e\n\u003cli\u003ePandey N, Soto-Garcia LF, Liao J, Zimmern P, Nguyen KT, Hong Y (2020). Mussel-inspired bioadhesives in healthcare: design parameters, current trends, and future perspectives. Biomaterials Science, 8(5):1240-1255. https://doi.org/10.1039/c9bm01848d. \u003c/li\u003e\n\u003cli\u003eStich T, Alagboso F, Křenek T, Kov\u0026aacute;ř\u0026iacute;k T, Alt V, Docheva D (2021). Implant-bone-interface: Reviewing the impact of titanium surface modifications on osteogenic processes in vitro and in vivo. Bioeng Transl Med. July 12;7(1):e10239. https://doi.org/10.1002/btm2.10239. \u003c/li\u003e\n\u003cli\u003eSowmiya M, Senthilkumar K (2016). Adsorption of proline, hydroxyproline and glycine on anatase (001) surface: a first-principle study. Theoretical Chemistry Accounts, 135(12):12. https://doi.org/10.1007/s00214-015-1783-7.\u003c/li\u003e\n\u003cli\u003eOliveira WF, Arrud, IRS, Silva GMM et al (2017). Functionalization of titanium dioxide nanotubes with biomolecules for biomedical applications. Materials Science and Engineering C, 81:597-606. https://doi.org/10.1016/j.msec.2017.08.017. \u003c/li\u003e\n\u003cli\u003eKulkarni M, Mazare A, Gongadze E, Perutkova et al (2015). Titanium nanostructures for biomedical applications. Nanotechnology, 26(6):062002. https://doi.org/10.1088/0957-4484/26/6/062002. \u003c/li\u003e\n\u003cli\u003eYin ZF, Wu L, Yang HG, Su YH (2013). Recent progress in biomedical applications of titanium dioxide. Physical Chemistry Chemical Physics, 15(14):4844-4858. https://doi.org/10.1039/c3cp43938k. \u003c/li\u003e\n\u003cli\u003eBharadwaz A, Jayasuriya AC (2020). Recent trends in the application of widely used natural and synthetic polymer nanocomposites in bone tissue regeneration. Materials Science and Engineering: C, 110, 110698. https://doi.org/10.1016/j.msec.2020.110698. \u003c/li\u003e\n\u003cli\u003eWang K, Jin H, Song Q, Huo J, Zhang J, Li P (2021). Titanium dioxide nanotubes as drug carriers for infection control and osteogenesis of bone implants. Drug Delivery and Translational Research, 11(4), 1456-1474. https://doi.org/10.1007/s13346-021-00980-z. \u003c/li\u003e\n\u003cli\u003eUlubayram K, Calamak S, Shahbazi R, Eroglu I (2015). Nanofibers based antibacterial drug design, delivery, and applications. Current Pharmaceutical Design, 21(15):1930-1943. https://doi.org/10.2174/1381612821666150302151804. \u003c/li\u003e\n\u003cli\u003ePelgrift RY, Friedman AJ (2013). Nanotechnology as a therapeutic tool to combat microbial resistance. Advanced Drug Delivery Reviews, 65(13-14):1803-1815. https://doi.org/10.1016/j.addr.2013.07.011.\u003c/li\u003e\n\u003cli\u003eColombo JS., Satoshi S, Okazaki J, Crean SJ, Sloan AJ, Waddington RJ (2012). In vivo monitoring of the bone healing process around different titanium alloy implant surfaces placed into fresh extraction sockets. Journal of Dentistry, 40(4):338-346. https://doi.org/10.1016/j.jdent.2012.01.010. \u003c/li\u003e\n\u003cli\u003eMueller CK, Thorwarth M, Schmidt M, Schlegel KA, Schultze-Mosgau S (2011). Comparative analysis of osseointegration of titanium implants with acid-etched surfaces and different biomolecular coatings. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics, 112(6):726-736. https://doi.org/10.1016/j.tripleo.2011.01.004.\u003c/li\u003e\n\u003cli\u003eKlinger MM, Rahemtulla F, Prince CW, Lucas LC, Lemons JE (1998). Proteoglycans at the bone-implant interface. Critical Reviews in Oral Biology \u0026amp; Medicine, 9(4):449-463. doi:10.1177/10454411980090040401.\u003c/li\u003e\n\u003cli\u003eSheng X, Wang A, Wang Z, Liu H, Wang J, Li C (2022). Advanced Surface Modification for 3D-Printed Titanium Alloy Implant Interface Functionalization. Frontiers in Bioengineering and Biotechnology, 10:850110. https://doi.org/10.3389/fbioe.2022.850110. \u003c/li\u003e\n\u003cli\u003eChouirfa H, Bouloussa H, Migonney V, Falentin-Daudr\u0026eacute; C (2019). Review of titanium surface modification techniques and coatings for antibacterial applications. Acta Biomaterialia, 83:37-54. https://doi.org/10.1016/j.actbio.2018.10.036.\u003c/li\u003e\n\u003cli\u003eSouza JCM, Sordi MB, Kanazawa M, Ravindran S et al (2019). Nano-scale modification of titanium implant surfaces to enhance osseointegration. Acta Biomaterialia, 94:112-131. https://doi.org/10.1016/j.actbio.2019.05.045.\u003c/li\u003e\n\u003cli\u003eRupp F, Liang L, Geis-Gerstorfer J, Scheideler L, H\u0026uuml;ttig F (2018). Surface characteristics of dental implants: A review. Dental Materials, 34(1):40-57. https://doi.org/10.1016/j.dental.2017.09.007.\u003c/li\u003e\n\u003cli\u003eSu Y, Luo C, Zhang Z, Hermawan H, Zhu D, Huang J et al (2017). Bioinspired surface functionalization of metallic biomaterials. Journal of the Mechanical Behavior of Biomedical Materials, 77:90-105. https://doi.org/10.1016/j.jmbbm.2017.08.035. \u003c/li\u003e\n\u003cli\u003eCosta D, Garrain PA, Baaden M (2013). Understanding small biomolecule-biomaterial interactions: A review of fundamental theoretical and experimental approaches for biomolecule interactions with inorganic surfaces. Journal of Biomedical Materials Research Part A, 101A(6):1210-1222.\u003c/li\u003e\n\u003cli\u003eMonti S (2007). Molecular Dynamics Simulations of Collagen-like Peptide Adsorption on Titanium-Based Material Surfaces. The Journal of Physical Chemistry C, 111(16):6086-6094. https://doi.org/10.1021/jp070266t.\u003c/li\u003e\n\u003cli\u003eButt, HJ, Berger R, Bonaccurso E, Chen Y, Wang, J (2007). Impact of atomic force microscopy on interface and colloid science. Advances in Colloid and Interface Science, 133(2):91-104. https://doi.org/10.1016/j.cis.2007.06.001\u003c/li\u003e\n\u003cli\u003eStrasser S, Zink A, Janko M, Heckl WM, Thalhammer S (2007). Structural investigations on native collagen type I fibrils using AFM. Biochemical and Biophysical Research Communications, 354(1):27-32.\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;ppen S, Langel W (2010). Simulation of adhesion forces and energies of peptides on titanium dioxide surfaces. Langmuir. 26(19):15248-56. https://doi.org/10.1021/la101687m.\u003c/li\u003e\n\u003cli\u003eWei B, Tielens F, Calatayud M (2019). Understanding the Role of Rutile TiO2 Surface Orientation on Molecular Hydrogen Activation. Nanomaterials, 9(9):1199. https://doi.org/10.3390/nano9091199.\u003c/li\u003e\n\u003cli\u003eYanamala N., Kagan VE, Shvedova AA (2013). Molecular modeling in structural nano-toxicology: interactions of nanoparticles with nano-machinery of cells. Advances in Drug Delivery Reviews, 65(15):2070-2077. https://doi.org/10.1016/j.addr.2013.05.005.\u003c/li\u003e\n\u003cli\u003eSelvaraj C, Sakkiah S, Tong W, Hong H (2018). Molecular dynamics simulations and applications in computational toxicology and nanotoxicology. Food and Chemical Toxicology, 112:495-506. https://doi.org/10.1016/j.fct.2017.08.028.\u003c/li\u003e\n\u003cli\u003eYazdanYar, A, Aschauer U, Bowen P (2018). Interaction of biologically relevant ions and organic molecules with titanium oxide (rutile) surfaces: A review on molecular dynamics studies. Colloids and Surfaces B: Biointerfaces, 161:563-577. https://doi.org/10.1016/j.colsurfb.2017.11.004.\u003c/li\u003e\n\u003cli\u003eEloh K, Sasanelli N, Maxia A, Caboni P (2016). Untargeted Metabolomics of Tomato Plants after Root-Knot Nematode Infestation. J Agric Food Chem. 64(29):5963-5968. https://doi.org/10.1021/acs.jafc.6b02181.\u003c/li\u003e\n\u003cli\u003eFais G, Malavasi V, Scano P. et al (2021). Metabolomics and lipid profile analysis of Coccomyxa melkonianii SCCA 048. Extremophiles 25:357\u0026ndash;368. https://doi.org/10.1007/s00792-021-01234-z.\u003c/li\u003e\n\u003cli\u003eStein SE (1999). An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J. Am. Soc. Mass Spectrom. 10(8):770\u0026minus;781. https://doi.org/10.1016/S1044-0305(99)00047-1.\u003c/li\u003e\n\u003cli\u003eKopka J, Schauer N, Krueger S, Birkemeyer C, Usadel et al. (2005), GMD@CSB. DB: TheGolm metabolome database. Bioinformatics,21(8):1635\u0026minus;1638. https://doi.org/10.1093/bioinformatics/bti236.\u003c/li\u003e\n\u003cli\u003eAdams RP (2007). Identification of Essential Oil Components by GasChromatography/Mass Spectrometry. Allured Publishing Corporation Carol Stream, 4th Edition.\u003c/li\u003e\n\u003cli\u003eRodriguez F, Mor\u0026agrave;n L, Gonzales G et al. (2017). Collagen extraction from mussel byssus: a new marine collagen source with physicochemical properties of industrial interest. Journal of Food Science and Technology, 54:1228-1238. https://doi.org/10.1007/s13197-017-2566-z.\u003c/li\u003e\n\u003cli\u003eSousa da Silva AW, Vranken WF. (2012) ACPYPE - AnteChamber PYthon Parser interfacE. BMC Res Notes 5:367. https://doi.org/10.1186/1756-0500-5-367.\u003c/li\u003e\n\u003cli\u003eKagami, LP, Sousa da Silva AW, Diaz A, Vranken W (2023). The ACPYPE web server for small-molecule MD topology generation, Bioinformatics, 39(6):btad350, https://doi.org/10.1093/bioinformatics/btad350.\u003c/li\u003e\n\u003cli\u003eVanommeslaeghe K, Hatcher E, Acharya C. et al. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 2009;31:671\u0026ndash;90. 10.1002/jcc.21367.45. \u003c/li\u003e\n\u003cli\u003eKhitrova VI, Bundule M, Pinsker ZG (1977). An electron-diffraction investigation of titanium dioxide in thin films. Kristallografiya, 22:1253-1258.\u003c/li\u003e\n\u003cli\u003eMomma K and Izumi F (2011). VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data. J. Appl. Crystallogr., 44: 1272-1276.\u003c/li\u003e\n\u003cli\u003ePhillips JC, Hardy DJ, Maia JDC, Stone JE, et al. (2020). Scalable molecular dynamics on CPU and GPU architectures with NAMD. Journal of Chemical Physics, 153:044130, https://doi.org/doi:10.1063/5.0014475.\u003c/li\u003e\n\u003cli\u003eAksimentiev A, Comer J (2011). Bionanotechnology Tutorial, https://www.ks.uiuc.edu/Training/Tutorials/science/bionano/bionano-tutorial.pdf\u003c/li\u003e\n\u003cli\u003eL Martinez, R Andrade, EG Birgin, JM Martinez. (2009) Packmol: A package for building initial configurations for molecular dynamics simulations. Journal of Computational Chemistry, 30, 2157-2164, https://doi.org/10.1002/jcc.21224.\u003c/li\u003e\n\u003cli\u003eHumphrey W, Dalke A, and Schulten K (1996). VMD - Visual Molecular Dynamics, J. Molec. Graphics, 14(1):33-38.\u003c/li\u003e\n\u003cli\u003eBrandt EG, and Alexander P. Lyubartsev AP (2015). Systematic Optimization of a force field for Classical Simulations of TiO2\u0026ndash;Water Interfaces. The Journal of Physical Chemistry C 119(32):18110-18125. https://doi.org/10.1021/acs.jpcc.5b02669.\u003c/li\u003e\n\u003cli\u003eGasteiger J, Marsili M (1980). Iterative partial equalization of orbital electronegativity\u0026mdash;a rapid access to atomic charges, Tetrahedron, 36(22):3219-3228. https://doi.org/10.1016/0040-4020(80)80168-2.\u003c/li\u003e\n\u003cli\u003eKumar A, Melis P, Genna V, Cocco E, Marrosu MG, Pieroni E (2014). Antigenic peptide molecular recognition by the DRB1-DQB1 haplotype modulates multiple sclerosis susceptibility. Mol Biosyst. 2014 Aug;10(8):2043-54. https://doi.org/10.1039/c4mb00203b.\u003c/li\u003e\n\u003cli\u003eValentini M, Pieroni E, Concas A, Pisu M (2022). Docking and Molecular Dynamics Simulations Reveal a Possible Site of Interaction of Olive Leaf Extract Hydroxytyrosol with Polyunsaturated Fatty Acid 5-Lipoxygenase Human Enzyme\u0026quot;. European Journal of Biology and Biotechnology, 3(2): 6-11.\u003c/li\u003e\n\u003cli\u003eEwald PP (1921). Die berechnung optischer und electrostatischer gitterpotentiale, Ann. Phys. 64:253-287; translated by A. Cornell, Atomics International Library, 1964.\u003c/li\u003e\n\u003cli\u003eMichaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O (2011). MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. J. Comput. Chem. 32: 2319\u0026ndash;2327. doi:10.1002/jcc.21787.\u003c/li\u003e\n\u003cli\u003eGavagnin E (2018). Pair correlation functions for identifying spatial correlation in discrete domains. Physical Review E. 97(1):062104. https://doi.org/10.1103/PhysRevE.97.062104.\u003c/li\u003e\n\u003cli\u003eYeh YL, and Chung-Yuan Mou CY (1999). Orientational Relaxation Dynamics of Liquid Water Studied by Molecular Dynamics Simulation, J. Phys. Chem. B, 103:3699-3705.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bionanoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnsc","sideBox":"Learn more about [BioNanoScience](http://link.springer.com/journal/12668)","snPcode":"12668","submissionUrl":"https://submission.nature.com/new-submission/12668/3","title":"BioNanoScience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Titanium Dioxide Lattice, Molecular Dynamics, Collagene, L-hydroxyproline, Nanomaterials for Biomedicine and Biomedical Applications, Nanomaterial Functionalization","lastPublishedDoi":"10.21203/rs.3.rs-4400232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4400232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the interaction between L-hydroxyproline (LHP), a key component of collagen, and a hydrated titanium dioxide (TiO\u003csub\u003e2\u003c/sub\u003e) lattice at various LHP concentrations. It represents the first step toward a broader project aimed at recycling agri-food wastes and byproducts, particularly mussel byssus, to enhance existing nano-coatings and design new ones. We performed gas chromatography-mass spectrometry analysis of byssus, which revealed 22 metabolites, confirming glycine, L-proline, and particularly LHP as key biomolecules. Subsequently, molecular dynamics (MD) simulations provided insights into LHP-lattice interaction mechanisms, revealing the TiO\u003csub\u003e2\u003c/sub\u003e lattice's ability to align LHP rings near-perpendicular to the lattice surface and near-parallel to each other, facilitated by the LHP tail functional group. This indicates optimal LHP packing, particularly close to the surface, and the formation of durable bonds between LHPs and lattice atoms. The analysis, particularly radial distribution functions, indicates that lattice-driven organizing interactions extend from the surface region to the bulk liquid phase thanks to the LHP\u0026ndash; and water\u0026ndash;mediated contributions. Overall, the simulation provides a chemical-physics rationale to explain improved collagen adhesion to the TiO\u003csub\u003e2\u003c/sub\u003e lattice, contributing to understanding collagen-TiO\u003csub\u003e2\u003c/sub\u003e interactions, and offering valuable insights for nanomaterials, biomaterials, tissue engineering, and biomedical applications.\u003c/p\u003e","manuscriptTitle":"Modeling the Interaction of L-Hydroxyproline, a Constituent of Collagen, with a Hydrated TiO2 lattice at Varied Concentrations: Examining Surface and Long-Range Effects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 14:27:45","doi":"10.21203/rs.3.rs-4400232/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-13T08:29:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-13T05:21:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244501187343747503684955085526425281873","date":"2024-06-08T15:04:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-08T11:11:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24173152091332207132921439372628004329","date":"2024-06-02T23:54:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-14T09:51:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-14T09:48:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-14T09:00:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioNanoScience","date":"2024-05-10T10:54:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bionanoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnsc","sideBox":"Learn more about [BioNanoScience](http://link.springer.com/journal/12668)","snPcode":"12668","submissionUrl":"https://submission.nature.com/new-submission/12668/3","title":"BioNanoScience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f911d158-4fe7-4226-8458-81a7f132c212","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:02:53+00:00","versionOfRecord":{"articleIdentity":"rs-4400232","link":"https://doi.org/10.1007/s12668-024-01559-x","journal":{"identity":"bionanoscience","isVorOnly":false,"title":"BioNanoScience"},"publishedOn":"2024-07-30 15:57:33","publishedOnDateReadable":"July 30th, 2024"},"versionCreatedAt":"2024-05-23 14:27:45","video":"","vorDoi":"10.1007/s12668-024-01559-x","vorDoiUrl":"https://doi.org/10.1007/s12668-024-01559-x","workflowStages":[]},"version":"v1","identity":"rs-4400232","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4400232","identity":"rs-4400232","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}