The spatial architecture of protein function and adaptation

By R. McLaughlin, F. J. Poelwijk, A. Raman, Walraj S. Gosal, Rama Ranganathan. 2012

455 citations recorded by the source corpus.

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 (PSD95pdz3) 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 PSD95pdz3 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.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, methodology): “ver the 20 top sites identified by ICOD (resp. SCA), we find that 85% (resp. 75%) of them are also among the 20 experimentally most important sites. Note that for SCA, we recover the result from Ref. [6]. The performance of ICOD is robust to varying the cutoff for removal of sites with a large propo”
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “In this way, the new approach manages to clearly outperform state-of-the-art approaches like SIFT [36] and PolyPhen-2 [35], which are based on independent-site models (even if, like in the case of PolyPhen-2, additional structural information is integrated into the prediction of mutational…”
Community‐wide evaluation of methods for predicting the effect of mutations on protein–protein interactions (2013, unknown): “29, Supporting Information Fig.”
Dissecting enzyme function with microfluidic-based deep mutational scanning (2015, unknown): “Proc Natl Acad Sci USA 107(9):4004–4009.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Evolutionary conservation patterns of amino acids in related protein structures identified protein sectors (Halabi et al., 2009; McLaughlin et al., 2012).”
Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations (2015, background): “[26]); (ii) Gene and protein copy numbers that are typically orders-of-magnitude higher than the natural ones; (iii) Growth environments that are less demanding than the natural ones; (iv) Proteins Fig 4.”
A model of substitution trajectories in sequence space and long-term protein evolution. (2015, background): “…many or long trajectories of substitutions using the full ﬁtness matrix may still be troublesome, although this has been done experimentally for a small number of proteins with very short trajectories (McLaughlin et al. 2012; Gong et al. 2013; Roscoe et al. 2013; Thyagarajan and Bloom 2014).”
Inferring fitness landscapes by regression produces biased estimates of epistasis (2014, unknown): “(An alternative approach, often used for measuring activities of an individual enzyme, is to introduce mutations randomly into a wild-type sequence [51, 52, 53, 54, 47]).”
Protein Sectors: Statistical Coupling Analysis versus Conservation (2014, unknown): “17”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “AC CE PT ED M AN US CR IP T than non-sector residues [65].”