In Gram-negative bacteria, the import of essential micronutrients across the outer membrane requires a transporter, an electrochemical gradient of protons across the inner membrane, and an inner membrane protein complex (ExbB, ExbD, TonB) that couples the proton-motive force to the outer membrane transporter. The inner membrane protein TonB binds directly to a conserved region, called the Ton-box, of the transporter. We solved the structure of the cobalamin transporter BtuB in complex with the C-terminal domain of TonB. In contrast to its conformations in the absence of TonB, the Ton-box forms a beta strand that is recruited to the existing beta sheet of TonB, which is consistent with a mechanical pulling model of transport.

MLL (Mixed Lineage Leukemia) is the target of chromosomal translocations which cause leukemias with poor prognosis. All leukemogenic MLL fusion proteins retain the CXXC domain which binds to nonmethylated CpG DNA. We present the solution structure of the MLL CXXC domain in complex with DNA, showing for the first time how the CXXC domain distinguishes nonmethylated from methylated CpG DNA. Based on the structure, we designed point mutations which disrupt DNA binding. Introduction of these mutations into MLL-AF9 results in increased DNA methylation of specific CpG nucleotides in Hoxa9, increased H3K9 methylation, decreased expression of Hoxa9 locus transcripts, loss of immortalization potential, and inability to induce leukemia in mice. These results establish that DNA binding by the CXXC domain and protection against DNA methylation is essential for MLL fusion leukemia. They also provide support for this interaction as a potential target for therapeutic intervention.

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.

Protein design aims to identify new protein sequences of desirable structure and biological function. Most current de novo protein design methods rely on physics-based force fields to search for low free-energy states following Anfinsen’s thermodynamic hypothesis. A major obstacle of such approaches is the inaccuracy of the force field design, which cannot accurately describe the atomic interactions or distinguish correct folds. We developed a new web server, EvoDesign, to design optimal protein sequences of given scaffolds along with multiple sequence and structure-based features to assess the foldability and goodness of the designs. EvoDesign uses an evolution-profile–based Monte Carlo search with the profiles constructed from homologous structure families in the Protein Data Bank. A set of local structure features, including secondary structure, torsion angle and solvation, are predicted by single-sequence neural-network training and used to smooth the sequence motif and accommodate the physicochemical packing. The EvoDesign algorithm has been extensively tested in large-scale protein design experiments, which demonstrate enhanced foldability and structural stability of designed sequences compared with the physics-based designing methods. The EvoDesign server is freely available at http://zhanglab.ccmb.med.umich.edu/EvoDesign.