High Resolution Mapping of Protein Sequence–Function Relationships

By Douglas M. Fowler, Carlos L. Araya, Sarel Jacob Fleishman, Elizabeth H. Kellogg, Jason J. Stephany, David Baker, Stanley Fields. 2010

543 citations recorded by the source corpus.

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 had 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.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein (2013, methodology): “We used a product interaction model previously applied to large-scale mutational data (Fowler et al. 2010; Araya et al. 2012) such that in the absence of epistasis, the observed enrichment score of the double mutant should equal the product of the two single mutants’ enrichment scores.”
Systematic Identification of H274Y Compensatory Mutations in Influenza A Virus Neuraminidase by High-Throughput Screening (2012, background): “A few recent studies have demonstrated the concept of highthroughput surveillance of a mutant library such as functional mapping for the human WW domain using a phage display system (35) and genome functional element screening using random transposon insertion (36, 37).”
Computational Design of Ligand Binding Proteins with High Affinity and Selectivity (2013, background): “…shell of the ligand and fall into two categories: (1) protein core substitutions tolerating mutation to chemically similar amino acids (for example, Leu105 and Cys 23), and (2) solvent-exposed loop residues having high sequence entropy (for example, His90 and Val92).”
Deep mutational scanning: assessing protein function on a massive scale (2011, methodology): “Maps can be generated from single-mutant data alone, or by averaging data from mutations occurring on multiply mutated backgrounds [24].”
Docking, scoring, and affinity prediction in CAPRI (2013, background): “39 In this approach, each residue of the HB36.”
Learning protein constitutive motifs from sequence data (2018, methodology): “(i/ii) Mutagenesis experiment showed the importance of sites 19,21,24,26 for binding speciﬁcity [40, 41].”
Massively Parallel Functional Analysis of BRCA1 RING Domain Variants (2015, methodology): “We obtained scores for 5154 of the 5757 possible single-amino-acid substitutions (Table S2).”