Design of Multi-Specificity in Protein Interfaces

Interactions in protein networks may place constraints on protein interface sequences to maintain correct and avoid unwanted interactions. Here we describe a “multi-constraint” protein design protocol to predict sequences optimized for multiple criteria, such as maintaining sets of interactions, and apply it to characterize the mechanism and extent to which 20 multi-specific proteins are constrained by binding to multiple partners. We find that multi-specific binding is accommodated by at least two distinct patterns. In the simplest case, all partners share key interactions, and sequences optimized for binding to either single or multiple partners recover only a subset of native amino acid residues as optimal. More interestingly, for signaling interfaces functioning as network “hubs,” we identify a different, “multi-faceted” mode, where each binding partner prefers its own subset of wild-type residues within the promiscuous binding site. Here, integration of preferences across all partners results in sequences much more “native-like” than seen in optimization for any single binding partner alone, suggesting these interfaces are substantially optimized for multi-specificity. The two strategies make distinct predictions for interface evolution and design. Shared interfaces may be better small molecule targets, whereas multi-faceted interactions may be more “designable” for altered specificity patterns. The computational methodology presented here is generalizable for examining how naturally occurring protein sequences have been selected to satisfy a variety of positive and negative constraints, as well as for rationally designing proteins to have desired patterns of altered specificity.: Computational methods have recently led to remarkable successes in the design of molecules with novel functions. These approaches offer great promise for creating highly selective molecules to accurately control biological processes. However, to reach these goals modeling procedures are needed that are able to define the optimal “fitness” of a protein to function correctly within complex biological networks and in the context of many possible interaction partners. To make progress toward these goals, we describe a computational design procedure that predicts protein sequences optimized to bind not only to a single protein but also to a set of target interaction partners. Application of the method to characterize “hub” proteins in cellular interaction networks gives insights into the mechanisms nature has used to tune protein surfaces to recognize multiple correct partner proteins. Our study also provides a starting point to engineer designer molecules that could modulate or replace naturally occurring protein interaction networks to combat misregulation in disease or to build new sets of protein interactions for synthetic biology.