Deciphering the Second Secret of Life: Discovering and manipulating allostery for enzymatic control

Deciphering the Second Secret of Life: Discovering and manipulating allostery for enzymatic control Deciphering the Second Secret of Life: Discovering and manipulating allostery for enzymatic control The principle of allostery the regulation of active sites or ligand binding sites within a protein by distant residues was discovered in 1904, when Christian Bohr discovered that the binding of one molecule (carbon dioxide) affects the binding affinity of another molecule (oxygen) to a protein (haemoglobin)1. Since Bohrs discovery, multiple equations have been developed to describe cooperative binding of ligands to distinct protein sites, and Monod considered his contribution on allostery2 even more significant than the operon model, claiming he had discovered the second secret of life. However, although allostery has long been modeled and accepted as a key phenomenon regulating activity within biological systems, we do not yet understand the mechanisms by which distant residues communicate with the active site to drive allostery and we remain unable to harness allostery for tunable control of enzyme function. This lack of mechanistic understanding stems at least in part from significant experimental and computational challenges. Experimentally, probing energetic couplings between residues requires mutant cycles in which the effects of mutations alone and in combination are quantitatively compared to determine if the residues contribute to function independently, cooperatively, or anti-cooperatively. Probing allostery further requires repeating these measurements in the presence or absence of allosteric effectors. It is exceedingly rare for an experimental study to report even one double mutant cycle, much less the number of mutant cycles needed to map out entire allosteric pathways for a given enzyme. This rarity results from practical considerations mutant cycle analysis requires expression and purification of 2N constructs to probe N residues, rendering tests involving more than four or five residues prohibitively laborious. Computationally, recent models of allostery (e.g. Cooper and Dryden3) established that allosteric regulation can result simply from changes in the frequency and amplitude of thermal fluctuations in a protein upon ligand binding, with no large-scale conformational change required, limiting insights possible from traditional structural approaches. Applying computational models towards understanding and predicting allostery therefore requires the ability to predict not only the overall structure of a particular enzyme, but also measure conformational dynamics and dynamic couplings between residues over a variety of timescales a significantly larger challenge. As a result of these computational and experimental challenges, the number of proteins known to be allosteric remains small. Even in well-characterized allosteric enzymes, the mechanisms by which distal residues communicate with the active site often remain unclear or ambiguous. Here, we leverage high-throughput experimental approach (HT-MEK, for High-Throughput Microfluidic Enzyme Kinetic) developed in the Fordye Lab cutting-edge MD simulations from the Ozkan lab), and recent advances in GPU architectures that enhance calculation scalability and speed to iterate over closely linked cycles of computational predictions and experimental tests to map, visualize, predict allostery within enzyme systems at unprecedented scale This allows us to specifically explore: (i) Is allostery a rare phenomenon, or is allostery ubiquitous across the proteome but difficult to detect? (ii) Do enzymes within the same superfamily share similar degrees and mechanisms of allostery, or does allostery vary widely even amongst closely related enzymes? (iii) How can we wire, rewire, and manipulate allosteric interaction networks within enzymes to modulate their activity?