Learning from evolution Protein sequences contain information specifying their three-dimensional structure and function, and statistical analysis of families of sequences has been used to predict these properties. Building from sequence data, Russ et al. used statistical models that take into account conservation at amino acid positions and correlations in the evolution of pairs of amino acids to predict new artificial sequences that will have the properties of the protein family. For the chorismate mutase family of metabolic enzymes, the authors demonstrate experimentally that the artificial sequences display natural-like catalytic function. Because the models access an enormous space of diverse sequences, such evolution-based statistical approaches may guide the search for functional proteins with altered chemical activities. Science , this issue p. 440