The most common way to apply electronic structure theory to study enzymatic reactions is to use a hybrid quantum mechanics/molecular mechanics (QM/ MM) formalism, coupled to all-atom MD simulations. Setup of QM/MM calculations requires considerable care, and an underappreciated aspect is just how slowly thermochemical predictions converge with respect to the size of the QM region, typically requiring hundreds * herbert@chemistry.ohio-state.edu of QM atoms. Some progress has been made toward automated selection of QM model regions. An alternative to QM/MM-MD simulations to study mechanistic aspects of enzyme catalysis is to use limited "cluster" models of the active site. This approach neglects any atomistic description of the larger protein environment, and is thus unable to describe chemical transformations that are driven by conformational changes of the protein, but for a limited set of problems the QMcluster approach has an important advantage of simplicity. By eliminating the need for MD sampling (requiring a QM energy and gradient evaluation every 1-2 fs), the cluster approach becomes amenable to higher-level electronic structure calculations. That said, QM-cluster modeling faces its own challenges insofar as one must carefully select a model to mimic the active-site structure. Starting from a protein crystal structure, QM geometry relaxation is required in order to obtain bond lengths and bond angles that are consistent with the chosen level of electronic structure theory, but unconstrained relaxation in the absence of an extended protein scaffold typically results in a structural collapse. To avoid this, the C α carbons at the periphery of the model system (where the crystal structure is truncated) are typically fixed in space, in what has been called a "coordinate-lock" approach. We refer to these as fixed-atom constraints, and their use may engender artificial rigidity in small model systems. In principle, this problem ought to become less severe in larger model systems, yet larger models are susceptible to the emergence of multiple minima. This obviates some of the advantage of using an optimized geometry of a finite model system in lieu of MD simulations that might sample all the low-lying minima.