De novo protein design: towards fully automated sequence selection

Abstract

Several groups have applied and experimentally tested systematic, quantitative methods to protein design with the goal of developing general design algorithms. We have sought to expand the range of computational protein design by developing quantitative design methods for residues of all parts of a protein: the buried core, the solvent exposed surface, and the boundary between core and surface. Our goal is an objective, quantitative design algorithm that is based on the physical properties that determine protein structure and stability and which is not limited to specific folds or motifs. We chose the betabetaalpha motif typified by the zinc finger DNA binding module to test our design methodology. Using previously published sequence scoring functions developed with a combined experimental and computational approach and the Dead-End Elimination theorem to search for the optimal sequence, we designed 20 out of 28 positions in the test motif. The resulting sequence has less than 40% homology to any known sequence and does not contain any metal binding sites or cysteine residues. The resulting peptide, pda8d, is highly soluble and monomeric and circular dichroism measurements showed it to be folded with a weakly cooperative thermal unfolding transition. The NMR solution structure of pda8d was solved and shows that it is well-defined with a backbone ensemble rms deviation of 0. 55 A. Pda8d folds into the desired betabetaalpha motif with well-defined elements of secondary structure and tertiary organization. Superposition of the pda8d backbone to the design target is excellent, with an atomic rms deviation of 1.04 A.