Humanization is an essential step in developing animal-derived antibodies into therapeutics, and approximately 40% of FDA-approved antibodies have been humanized. Conventional humanization approaches graft the complementarity-determining regions (CDRs) of the animal antibody onto several homologous human frameworks. This process, however, often drastically lowers stability and antigen binding, demanding iterative mutational fine-tuning to recover the original antibody properties. Here, we present Computational hUMan AntiBody design (CUMAb), a web-accessible method that starts from an experimental or model antibody structure, systematically grafts the animal CDRs on thousands of human frameworks, and uses Rosetta atomistic simulations to rank the designs by energy and structural integrity (http://CUMAb.weizmann.ac.il). CUMAb designs of three independent antibodies exhibit similar affinities to the parental animal antibody, and some designs show marked improvement in stability. Surprisingly, nonhomologous frameworks are often preferred to the highest-homology ones, and several CUMAb designs relying on different human frameworks and encoding dozens of mutations from one another are functionally equivalent. Thus, CUMAb presents a general and streamlined approach to optimizing antibody stability and expressibility while increasing humanness.