Protein sectors: evolutionary units of three-dimensional structure

By Najeeb M. Halabi, Olivier Rivoire, Stanislas Leibler, Rama Ranganathan. 2009

722 citations recorded by the source corpus.

Proteins display a hierarchy of structural features at primary, secondary, tertiary, and higher-order levels, an organization that guides our current understanding of their biological properties and evolutionary origins. Here, we reveal a structural organization distinct from this traditional hierarchy by statistical analysis of correlated evolution between amino acids. Applied to the S1A serine proteases, the analysis indicates a decomposition of the protein into three quasi-independent groups of correlated amino acids that we term “protein sectors”. Each sector is physically connected in the tertiary structure, has a distinct functional role, and constitutes an independent mode of sequence divergence in the protein family. Functionally relevant sectors are evident in other protein families as well, suggesting that they may be general features of proteins. We propose that sectors represent a structural organization of proteins that reflects their evolutionary histories.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, methodology): “However, the fundamental link we propose between functional sectors and small-eigenvalue modes of the covariance matrix is important, since large-eigenvalue modes of the covariance matrix also contain confounding information about subfamily-specific residues [56] and phylogeny [57], and consistently”
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, neutral): “coevolving residues termed sectors (Halabi et al. 2009):”
Protein structure prediction from sequence variation (2012, neutral): “…principle by demonstrating that the predicted active sites of trypsin and Ras were particularly accurate relative to the accuracy of the rest of the protein when compared with the crystal structures, following the spirit of earlier work that used a weighted local mutual information method 50,51 .”
Exploring protein fitness landscapes by directed evolution (2009, background): “Phylogenetic analyses suggest that nature might separately optimize other, structurally non-obvious subunits, or 'sectors”
The spatial architecture of protein function and adaptation (2012, methodology): “The method involves three components: (1) a bacterial two-hybrid (B2H) system modified from a previous study(21) in which the ability of PSD95 to bind its cognate ligand (-TKNYKQTSV-COOH, derived from the cysteine-rich interactor of PDZ (CRIPT)(22)) is quantitatively linked to the expression of enha”
The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing (2014, neutral): “In analogy with past studies on the economic markets and enzymes [13–15], we termed these groups of residues ‘‘sectors’’.”
The genetic landscape of a physical interaction (2018, background): “These interactions are enriched between physically contacting and proximal residues, but can also involve some long-range indirect interactions (Halabi et al., 2009).”
Emerging methods in protein co-evolution (2013, background): “The groups of positions identified by SCAnew were called ‘protein sectors’ 62 , and in several cases they have been shown to form structurally independent clusters of amino acids with distinct roles.”
Evolution-Based Functional Decomposition of Proteins (2015, positive): “2B-C) (10; 37; 38).”
A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase (2011, neutral): “Because protein structure and epistasis are interrelate”
A new ensemble coevolution system for detecting HIV-1 protein coevolution (2015, methodology): “Table 1 Summary of 27 sequence-based methods in our ensemble coevolution system Methods* Statistical methodology Updated Ref ASC/APC Mutual information 2007 [34] BN Bayesian network 2007 [35] CTMP Continuous-time Markov model, phylogenetic tree 2007 [36] CoMap Compensation coefficient, phylogenetic ”
Protein stability by number: high-throughput and statistical approaches to one of protein science’s most difficult problems (2011, neutral): “devised a new kind of SCA calculation and has used it to identify independent clusters of co-evolving residues in protein families (sectors) [69].”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “Early work looked at the correlations as a measure of proximity [87, 92, 129, 207].”
Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins (2017, background): “In other words, the unit of protein structure evolution could be smaller than a domain 27–29 , 56 .”