Browsing by Subject "Amino Acids"
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Item A Global Experimental Analysis of Protein Function: A Case Study in the PDZ Domain(2011-02-01) McLaughlin, Richard Noel, Jr.; Ranganathan, RamaA complete understanding of the energetic architecture of a protein can be achieved only with a comprehensive description of the interaction of every amino acid with every other amino acid. Many efforts to understand the apparent complexity of protein function have attempted to address this problem with limited mutagenesis studies. A global computational description of amino acid interactions, Statistical Coupling Analysis has shown the existence of a contiguous subset of positions within a protein that displays significant co-evolution, termed protein sectors. Limited mutagenesis studies have shown sectors to be networks of higher-order interaction crucial for protein function; however, a theory of such global scope requires validation with a global experiment. Here, we design an assay system that measures the cellular function of a PDZ domain in a high-throughput and quantitative manner. We perform a comprehensive single amino acid mutagenesis experiment to show that most positions in the protein are robust to most mutations, and the set of positions that shows sensitivity to mutation is enriched for sector positions. Further, we perform a global pairwise epistasis experiment in which we measure the way in which every amino acid mutation in the PDZ domain feels the effect of a second mutation at a key specificity and affinity determining position in the peptide ligand of the PDZ domain. We find that those positions that show strong non-additivity in the context of the peptide mutation are all contained within the PDZ sector. Further, these sector positions that display strong non-additivity all display the property of rapidly changing specificity upon mutation. That is, any mutation at these sector positions has a negative functional effect in the context of the endogenous peptide. However, these positions appear to be spring-loaded for change since these same mutations enhance function in the context of an alternative peptide. We hypothesize that proteins are robust as shown by their insensitivity to general mutation. However, proteins are simultaneously fragile as shown by their sensitivity to specific mutagenesis at sector positions. This fragility, however, is strongly coupled to evolvability as shown by the enhancement of alternative function endowed by these endogenously detrimental mutations.Item Hierarchy of Interactions in Protein Evolution(2016-06-28) Salinas, Victor H.; Yu, Hongtao; Ranganathan, Rama; Takahashi, Joseph; Tu, BenjaminDeciphering the relationship between genotype and phenotype is complicated by the sheer number of possible cooperative interactions amongst the parts that make up biological systems. For even small systems such as individual protein domains, it has been difficult to comprehensively obtain high quality empirical data of amino acid interactions to distinguish different models for the global pattern of cooperativity. The statistical coupling analysis (SCA) - one approach for studying the co-evolution of amino acid positions in homologous sequences - provides a model for this pattern that is distinct from spatial proximity in tertiary structure, positional conservation, or even other forms of co-evolution. Here, we use an extension of deep mutational scanning to analyze nearly 50,000 single and double mutations in several homologs of a model protein - the PDZ family of protein interaction domains. Across the domains queried experimentally, the distributions of couplings between pairs of positions from all possible double mutants are well-approximated by unimodal distributions such that their average provides an estimate of the intrinsic coupling between them. Importantly, the SCA provides the best representation of this experimental pattern of couplings conserved among the homologs. These results highlight the heterogeneous pattern of couplings in protein structures and motivate the re-focus of efforts to understand protein folding and function toward the study of the origin of the co-evolving network of amino acids.