The self-assembly and self-organization of small molecules on the surface of nanoparticles constitute a potential route toward the preparation of advanced proteinlike nanosystems. However, their structural characterization, critical to the design of bionanomaterials with well-defined biophysical and biochemical properties, remains highly challenging. Here, a computational model for peptide-capped gold nanoparticles (GNPs) is developed using experimentally characterized Cys-Ala-Leu-Asn-Asn (CALNN)- and Cys-PheGly-Ala-Ele-Leu-Ser-Ser (CFGAILSS)-capped GNPs as a benchmark. The structure of CALNN and CFGAILSS monolayers is investigated using both structural biology techniques and molecular dynamics simulations. The calculations reproduce the experimentally observed dependence of the monolayer secondary structure on the peptide capping density and on the nanoparticle size, thus giving us confidence in the model. Furthermore, the computational results reveal a number of new features of peptide-capped monolayers, including the importance of sulfur movement for the formation of secondary structure motifs, the presence of water close to the gold surface even in tightly packed peptide monolayers, and the existence of extended 2D parallel beta-sheet domains in CFGAILSS monolayers. The model developed here provides a predictive tool that may assist in the design of further bionanomaterials.