Global analysis of multi-mutants to improveprotein function

Abstract

The identification of amino acid substitutions that both enhance the stability and function of a protein is a key challenge in protein engineering. Technological advances have enabled assaying thousands of protein variants in a single high-throughput experiment, and more recent studies use such data in protein engineering. We present a Global Multi-Mutant Analysis (GMMA) that exploits the presence of multiply-substituted variants to identify individual amino acid substitutions that are beneficial for the stability and function across a large library of protein variants. We have applied GMMA to >54,000 variants of green fluorescent protein (GFP), each with known fluorescence output, and each carrying 1-15 amino acid substitutions. The GMMA method achieves a good fit to the data while being analytically transparent. The six top-ranking substitutions are demonstrated to progressively enhance GFP and in general, our analysis recovers nearly all the substitutions previously reported to be beneficial for GFP folding and function, only using a single experiment as input. Significance StatementProtein engineering is carried out to improve proteins for practical applications by changing one or more amino acid residues in a protein. We present a method termed global multi-mutant analysis (GMMA) that helps solve two problems in protein engineering. First, because many proteins are already highly optimized, it can be difficult to identify individual variants that further improve function. Second, while it is possible to combine variants with small individual effects, such approaches may be hampered by non-additivity. GMMA identifies combinable effects of single substitutions from a large set of variants each carrying multiple substitutions. We demonstrate the approach on a set of 54,000 variants of green fluorescent protein and identify many enhancing single-substitutions from a single experiment.