Computational design of specific binding proteins using Leave-One-Out

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): The goal of this research is to be able to design a receptor protein for any protein target. Furthermore, we propose that the binding event will be signaled by the appearance of enzyme activity or fluorescence. The novel binding proteins will be able to sense and report the presence of a specific protein or peptide in a mixture of others, allowing the detection of disease agents or other proteins of interest both in vivo and in vitro. The new approach takes advantage of protein folding pathways. When proteins fold, they do so in a specified order of events, and the events can be predicted based on the structure of the protein. When a protein finishes folding, its activity is immediately turned on. If we leave out one small piece of the protein so that the folding cannot finish, then the protein sits in an inactive state until the missing piece appears. Using this Leave-One- Out strategy, partially folded proteins become sensors for their missing pieces. Using computational design algorithms, a new amino acid sequence can be substituted for the left out piece. This new sequence can be from anthrax, avian flu, a cancer cell marker or any other protein. Computational substitution of the amino acids surrounding this new sequence is made possible using massively parallel computing clusters, and by dividing the design task into numerous smaller tasks based on what is known about protein folding and energy calculations. The final design is a protein that wraps around the target peptide, specifically identifying it by its complementary shape. Green fluorescent protein has been the subject of the first leave-one-out design studies and has yielded specific binding proteins that glow only when the target peptide is present. The first target was a peptide from the deadly H5N1 strain of avian flu. When the designed Leave-One-Out GFP encounters its target peptide, it finishes folding and becomes fluorescent. PUBLIC HEALTH RELEVANCE: The results of this research could revolutionize protein diagnostics, replacing monoclonal antibodies as the current best means of specific protein identification. Computationally designed specific binding proteins could be used as protein therapeutics, biosensors, proteomic arrays, fluorescent probes, protein purification affinity agents, and many other applications.