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Two alternative strategies are commonly used to study protein–protein interactions (PPIs) and to engineer protein-based inhibitors. In one approach, binders are selected experimentally from combinatorial libraries of protein mutants that are displayed on a cell surface. In the other approach, computational modeling is used to explore an astronomically large number of protein sequences to select a small number of sequences for experimental testing. While both approaches have some limitations, their combination produces superior results in various protein engineering applications. Such applications include the design of novel binders and inhibitors, the enhancement of affinity and specificity, and the mapping of binding epitopes. The combination of these approaches also aids in the understanding of the specificity profiles of various PPIs.
Novel protein binders to various targets can be engineered by first applying computational approaches and then optimizing the binder with yeast surface display (YSD). Computational methods can narrow down the choices of possible mutations and combinations of mutations, thereby enabling the construction of smaller, more focused libraries.
Together, combinatorial and computational techniques can be used to map binding epitopes of poorly characterized PPIs, thus identifying binding hot-spots and affinity-enhancing mutations.
Binding-specificity profiles can be mapped with a combination of combinatorial approaches and next-generation sequencing (NGS). Computational methods contribute to understanding the nature of specific PPIs, determining their binding specificity, and predicting ligands for homologous targets.