Protein structure predication is a field of computational molecular modeling with an enormous potential for improvement. Side-chain geometry prediction is a critical component of this process that is crucial for computational protein structure predication as well as crystallographers in refining experimentally determined protein crystal structures. The cornerstone of side-chain geometry prediction are side-chain rotamer libraries, usually obtained through exhaustive statistical analysis of existing protein structures. Little is known, however, about the driving forces leading to the preference or suitability of one rotamer over another. Construction of 3D hydropathic interaction maps for nearly 30,000 tyrosines extracted from the PDB reveals their environments, in terms of hydrophobic and polar (collectively “hydropathic”) interactions. Using a unique 3D similarity metric, these environments were clustered with k-means. In the ϕ, ψ region (–200° < ϕ < –155°; –205° < ψ < –160°) representing 631 tyrosines, clustering reduced the set to 14 unique hydropathic environments, with most diversity arising from favorable hydrophobic interactions. Polar interactions for tyrosine include ubiquitous hydrogen bonding with the phenolic OH and a handful of unique environments surrounding the backbone. The memberships of all but one of the 14 environments are dominated by a single χ1/χ2 rotamer. Each tyrosine residue attempts to fulfill its hydropathic valence. Structural water molecules are thus used in a variety of roles throughout protein structure. A second project involves elucidating the 3D structure of CRIP1a, a cannabinoid 1 receptor (CB1R) binding protein that could provide information for designing small molecules targeting the CRIP1a-CB1R interaction. The CRIP1a protein was produced in high purity. Crystallization experiments failed, both with and without the last 9 or 12 amino acid peptide of the CB1R C-terminus. Attempts were made to use NMR for structure determination; however, the protein precipitated out during data acquisition. A model was thus built computationally to which the CB1R C-terminus peptide was docked. HINT was used in selecting optimum models and analyzing interactions involved in the CRIP1a-CB1R complex. The final model demonstrated key putative interactions between CRIP1a and CB1R while also predicting highly flexible areas of the CRIP1a possibly contributing to the difficulties faced during crystallization.