High-throughput and quantitative biochemical approaches will be required to develop predictive models of cell function and regulation, and to understand systems as complex as enzymes. I will describe two such approaches and the insights attained to date. RNA-MaP developed by the Greenleaf lab at Stanford allows us to determine thermodynamic and kinetic rules for RNA binding by RNA binding proteins, and provides testable models for cellular RNA/protein interactions and additional biophysical and evolutionary insights. HT-MEK (High-throughput Mechanistic Enzyme Kinetics), a new microfluidics methodology developed by the Fordyce lab at Stanford, allows us to obtain quantitative kinetic and thermodynamic data for thousands of enzyme variants, in a small fraction of the time and at a minute fraction of the cost of traditional biochemical approaches. Our initial studies on an Alkaline Phosphatase superfamily member provide the first comprehensive functional landscape for an enzyme, delineating function throughout an enzyme scaffold. Studies on this and additional systems are needed to understand enzyme function, to reveal the action of drugs and allosteric effectors, and to develop rules to engineer new enzymes and pathways at will. Most generally, quantitative, high-throughput biochemical methodologies will usher in a post-genomic era in biology that is grounded in biochemical understanding and powered by quantitative physical models.
Daniel Herschlag (Stanford University)