Intrinsically disordered proteins (IDPs) can display a broad spectrum of binding modes and highly variable binding affinities when interacting with both biological and nonbiological materials. A quantitative model of such behavior is important for the better understanding of the function of IDPs when encountering inorganic nanomaterials with the potential to control their behavior in vivo and in vitro. Depending on their amino acid composition and chain length, binding properties can vary strongly between different IDPs. Moreover, due to differences in the physical chemical properties of clusters of amino acid residues along the IDP primary sequence, individual residues can adopt a wide range of bound state populations. Quantitative experimental binding affinities with synthetic silica nanoparticles (SNPs) at residue-level resolution, which were obtained for a set of IDPs by solution NMR relaxation experiments, are explained here by a first-principle analytical statistical mechanical model termed SILC. SILC quantitatively predicts residue-specific binding affinities to nanoparticles and it expresses binding cooperativity as the cumulative result of pairwise residue effects. The model, which was parametrized for anionic SNPs and applied to experimental data of four IDP systems with distinctive binding behavior, successfully predicts differences in overall binding affinities, fine details of IDP-SNP affinity profiles, and site-directed mutagenesis effects with a spatial resolution at the individual residue level. The SILC model provides an analytical description of such types of fuzzy IDP-SNP complexes and may help advance understanding nanotoxicity and in vivo targeting of IDPs by specifically designed nanomaterials.