Molecular simulations provide detailed insight into how biomolecules interact with nanomaterial surfaces. However, quantitative adsorption calculations are computationally demanding, and experimental measurements alone rarely reveal the molecular mechanism of binding. This type of molecular-level understanding is often essential for guiding the design of nanomaterials with improved safety and performance in technological and medical applications. This thesis combines atomistic molecular dynamics, enhanced sampling methods, machine learning, and quartz crystal microbalance with dissipation monitoring to develop a mechanistic and predictive description of adsorption at nano-bio interfaces. Adsorption free energies were computed for a broad set of biomolecular building blocks on flat and curved ZnS nanostructures, both pristine and polymer-coated, showing how curvature, hydration structure, and polymer layers affect adsorption affinities. Building on this and on the results of previous studies, a consistently generated dataset of adsorption free energies for biomolecular fragments on various nanomaterial surfaces was analyzed using statistical machine-learning tools, revealing that biomolecules and nanomaterials cluster into a few chemically significant classes and that adsorption behavior can be reproduced using a reduced set of representative fragments in simple linear models. The thesis then shifts attention from individual fragments to short peptides, examining how the order of amino acids in a peptide sequence collectively governs adsorption behavior onto TiO₂. Extensive MD simulations of sequence permutations of the titanium-binding peptide (min-TBP-1) show how the spatial arrangement of charged and polar residues, together with ion-mediated interactions, determines adsorption affinity, conformational adaptation, and preferred sequence motifs. Finally, a subset of these peptides was studied using enhanced sampling and QCM-D experiments, providing quantitative adsorption free energies and complementary information on adsorption kinetics. The comparison between computed free energies and experimentally derived binding offers a more complete picture of sequence-specific peptide adsorption. Together, these studies advance the molecular understanding of nano-bio interfaces and outline practical strategies for predicting and tailoring biomolecular adsorption on nanomaterials.

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.

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.

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.