Computational methods for controlling binding specificity.

Learning to control, protein-binding specificity is useful for both fundamental and applied biology. In fundamental research, better understanding of complicated signaling networks could be achieved through engineering of regulator proteins to bind to only a subset of their effector proteins. In applied research such as drug design, nonspecific binding remains a major reason for failure of many drug candidates. However, developing antibodies that simultaneously inhibit several disease-associated pathways are a rising trend in pharmaceutical industry. Binding specificity could be manipulated experimentally through various display technologies that allow us to select desired binders from a large pool of candidate protein sequences. We developed an alternative approach for controlling binding specificity based on computational protein design. We can enhance binding specificity of a protein by computationally optimizing its sequence for better interactions with one target and worse interaction with alternative target(s). Moreover, we can design multispecific proteins that simultaneously interact with a predefined set of proteins. Unlike combinatorial techniques, our computational methods for manipulating binding specificity are fast, low cost and in principle are able to consider an unlimited number of desired and undesired binding partners.