T-cell receptors (TCRs) are proteins that recognize peptides from foreign proteins bound to the major histocompatibility complex (MHC) on the surface of an antigen-presenting cell. This interaction enables the T cells to initiate a cell-mediated immune response to terminate cells displaying the foreign peptide on their MHC. Naturally occurring TCRs have high specificity but low affinity toward the peptide-MHC (pepMHC) complex. This prevents the usage of solubilized TCRs for diagnosis and treatment of viral infections or cancers. Efforts to enhance the binding affinity of several TCRs have been reported in recent years, through randomized libraries and in vitro selection. However, there have been no reported efforts to enhance the affinity via structure-based design, which allows more control and understanding of the mechanism of improvement. Here, we have applied structure-based design to a human TCR to improve its pepMHC binding. Our design method evolved based on iterative steps of prediction, testing, and generating more predictions based on the new data. The final design function, named ZAFFI, has a correlation of 0.77 and average error of 0.35 kcal/mol with the binding free energies of 26 point mutations for this system that we measured by surface plasmon resonance (SPR). Applying the filter that we developed to remove nonbinding predictions, this correlation increases to 0.85, and the average error decreases to 0.3 kcal/mol. Using this algorithm, we predicted and tested several point mutations that improved binding, with one giving over sixfold binding improvement. Four of the point mutations that improved binding were then combined to give a mutant TCR that binds the pepMHC 99 times more strongly than the wild-type TCR.