Many organisms produce complex, hierarchically structured, inorganic materials via protein-influenced crystal growth-a process known as biomineralization. Understanding this process would shed light on hard-tissue formation and guide efforts to develop biomaterials. We created and tested a computational method to design protein-biomineralization systems. The algorithm folds a protein from a fully extended structure and simultaneously optimizes the fold, orientation, and sequence of the protein adsorbed to a crystal surface. We used the algorithm to design peptides (16 residues) to modify calcite (CaCO3) crystallization. We chemically synthesized six peptides that were predicted to bind different states of a calcite growth plane. All six peptides dramatically affected calcite crystal growth (as observed by scanning electron microscopy), and the effects were dependent on the targeted state of the {001} growth plane. Additionally, we synthesized and assayed scrambled variants of all six designed peptides to distinguish cases where sequence composition determines the interactions versus cases where sequence order (and presumably structure) plays a role. Scrambled variants of negatively charged peptides also had dramatic effects on calcite crystallization; in contrast, scrambled variants of positively charged peptides had a variable effect on crystallization, ranging from dramatic to mild. Special emphasis is often placed on acidic protein residues in calcified tissue mineralization; the work presented here suggests an important role for basic residues as well. In particular, this work implicates a potential role for basic residues in sequence-order specificity for peptide-mineral interactions.