Deep mutational scanning: a new style of protein science

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Abstract

Mutagenesis provides insight into proteins, but only recently have assays that couple genotype to phenotype been used to assess the activities of as many as 1 million mutant versions of a protein in a single experiment. This approach-'deep mutational scanning'-yields large-scale data sets that can reveal intrinsic protein properties, protein behavior within cells and the consequences of human genetic variation. Deep mutational scanning is transforming the study of proteins, but many challenges must be tackled for it to fulfill its promise.

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High Resolution Mapping of Protein Sequence–Function Relationships

Douglas Fowler, Carlos L. Araya, Sarel J. Fleishman … (2010)

We present a large-scale approach to investigate the functional consequences of sequence variation in a protein. The approach entails the display of hundreds of thousands of protein variants, moderate selection for activity, and high throughput DNA sequencing to quantify the performance of each variant. Using this strategy, we tracked the performance of >600,000 variants of a human WW domain after three and six rounds of selection by phage display for binding to its peptide ligand. Binding properties of these variants defined a high-resolution map of mutational preference across the WW domain; each position possessed unique features that could not be captured by a few representative mutations. Our approach could be applied to many in vitro or in vivo protein assays, providing a general means for understanding how protein function relates to sequence.

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Richard N. McLaughlin, Frank Poelwijk, Arjun S Raman … (2012)

Statistical analysis of protein evolution suggests a design for natural proteins in which sparse networks of coevolving amino acids (termed sectors) comprise the essence of three-dimensional structure and function. However, proteins are also subject to pressures deriving from the dynamics of the evolutionary process itself--the ability to tolerate mutation and to be adaptive to changing selection pressures. To understand the relationship of the sector architecture to these properties, we developed a high-throughput quantitative method for a comprehensive single-mutation study in which every position is substituted individually to every other amino acid. Using a PDZ domain (PSD95(pdz3)) model system, we show that sector positions are functionally sensitive to mutation, whereas non-sector positions are more tolerant to substitution. In addition, we find that adaptation to a new binding specificity initiates exclusively through variation within sector residues. A combination of just two sector mutations located near and away from the ligand-binding site suffices to switch the binding specificity of PSD95(pdz3) quantitatively towards a class-switching ligand. The localization of functional constraint and adaptive variation within the sector has important implications for understanding and engineering proteins.

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Brian K. Shoichet, George Minasov, Xiaojun Wang (2002)

Pressured by antibiotic use, resistance enzymes have been evolving new activities. Does such evolution have a cost? To investigate this question at the molecular level, clinically isolated mutants of the beta-lactamase TEM-1 were studied. When purified, mutant enzymes had increased activity against cephalosporin antibiotics but lost both thermodynamic stability and kinetic activity against their ancestral targets, penicillins. The X-ray crystallographic structures of three mutant enzymes were determined. These structures suggest that activity gain and stability loss is related to an enlarged active site cavity in the mutant enzymes. In several clinically isolated mutant enzymes, a secondary substitution is observed far from the active site (Met182-->Thr). This substitution had little effect on enzyme activity but restored stability lost by substitutions near the active site. This regained stability conferred an advantage in vivo. This pattern of stability loss and restoration may be common in the evolution of new enzyme activity. (c) 2002 Elsevier Science Ltd.