The unparalleled specificity and activity of therapeutic proteins has reshaped many aspects of modern clinical practice, and aggressive development of new protein drugs promises a continued revolution in disease therapy. As a result of their biological origins, however, therapeutic proteins present unique design challenges for the biomolecular engineer. For example, protein drugs are subject to immune surveillance within the patient's body; this anti-drug immune response can compromise therapeutic efficacy and even threaten patient safety. Thus, there is a growing demand for broadly applicable protein deimmunization strategies. We have recently developed optimization algorithms that integrate computational prediction of T-cell epitopes and bioinformatics-based assessment of the structural and functional consequences of epitope-deleting mutations. Here, we describe the first experimental validation of our deimmunization algorithms using Enterobacter cloacae P99 β-lactamase, a component of antibody-directed enzyme prodrug cancer therapies. Compared with wild-type or a previously deimmunized variant, our computationally optimized sequences exhibited significantly less in vitro binding to human type II major histocompatibility complex immune molecules. At the same time, our globally optimal design exhibited wild-type catalytic proficiency. We conclude that our deimmunization algorithms guide the protein engineer towards promising immunoevasive candidates and thereby have the potential to streamline biotherapeutic development.