Bill Russ (UTSW Dallas). Biophysics seminar ENS-ESPCI.

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23 June 2017 13:00 » 14:00 — ENS, Room L374/L376, 3rd floor

Evolutionary principles of enzyme design

The central properties of proteins – folding, biochemical activity, adaptive capacity – arise from a complex pattern of energetic interactions between amino acid residues. However, due to the subtlety of the structure-energy relationship and the possibility of high-order cooperativity between residues, this pattern is difficult to deduce from even high-resolution protein structures; that is, we do not “see” energy in structures. The statistical coupling analysis (SCA) provides a method for inferring important amino acid interactions through studying the correlated conservation of amino acid positions over the long-term evolutionary record of a protein family. SCA indicates sparse, physically contiguous networks of amino acids (termed “sectors”) distributed through the protein core – a novel model for the design of natural proteins. Analyses in many protein families indicate that sectors link known functional regions of proteins, including ligand binding sites, allosteric sites, and active sites in enzymes.
What is the extent of the information encoded in the statistical coupling pattern of a protein family? To test this, we are employing protein design as a rigorous and thorough test of the sector model in the context of a core metabolic enzyme, chorismate mutase (CM). CM variants are designed using a MMCSA algorithm that recreates the natural pattern of conservation and of statistical constraints between sites, without using any direct information from three-dimensional structure or from physics-based potentials. We find that the SCA correlations are necessary for the design of functional CM enzymes. Furthermore, the data suggest that proteins are made of two distinct materials, comprising one set of positions that act independently and another with cooperative contributions to function.

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