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

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

Titanium binding peptides are useful tools for material functionalization in both biomedical and nanotechnology applications because of their ability to attach selectively to titanium surfaces. In this work, we investigate the adsorption behavior of a series of 360 six amino acids long peptides obtained by permutations of titanium binding peptide residues, RKLPDA, on hydroxylated anatase TiO₂ (101) surfaces using extensive atomistic molecular dynamics (MD) simulations, with the purpose identifying sequences with stronger adsorption affinity to titanium. Our results show that small changes in amino acid order can significantly affect both binding strength and structural conformations. Peptides with arginine at the N-terminus and lysine or aspartic acid near the C-terminus tended to exhibit more stable adsorption. The clustering and radial distribution function (RDF) analyzes revealed different binding modes and key atomic interactions, with nitrogen-containing groups and, in some cases, Na⁺ ions playing a significant role in the anchoring of peptides to the surface. These findings suggest a detailed sequence-level understanding of peptide-TiO₂ interactions and can guide the design of improved peptides for titanium functionalization.