Atomistic mechanisms and predictive modeling of biomolecular adsorption on nanomaterial surfaces

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.