Abstract
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.
Highlights
Proteins have evolved to operate within the context of crowded cellular milieus and complex functional networks [1]
To make progress toward these goals, we describe a computational design procedure that predicts protein sequences optimized to bind to a single protein and to a set of target interaction partners
Our study 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
Summary
Proteins have evolved to operate within the context of crowded cellular milieus and complex functional networks [1] It is not well understood how and to what extent protein sequences and structures are optimized for multiple and likely interdependent properties such as stability and efficiency of folding, low propensity for aggregation, and functional characteristics. Protein design methodologies have generally focused on choosing an amino acid sequence optimal for a specific criterion, such as protein stability or interaction energy with a single binding partner These computational design techniques have led to several accomplishments, including the pioneering design of a complete protein [4], of novel protein folds [5,6], the engineering of catalytic activity into an uncatalytic scaffold [7,8], and the redesign of protein–protein interfaces [9,10,11,12]. If we wish to rationally design new proteins that can be expressed and function correctly in a cellular environment and in the context of many possible interaction partners, it is likely that we will need modeling procedures that are able to consider a variety of requirements defining optimal protein ‘‘fitness.’’
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