ABSTRACT
Enzymes have important implications for understanding many human diseases as well as for developing new
medicines and therapies. Design of small molecule drugs without side-effects targeting enzymes and designer
enzymes as biotherapeutics are widely pursued in the pharmaceutical industry. However, these endeavors are
hindered, among other aspects, by the lack of fundamental understanding of enzyme function including the
factors that enable enzymes to achieve high catalytic efficiencies. For more than a century, an immense wealth
of information has been accumulated based on experimental and computational investigations. Collectively,
the biochemical model of enzyme catalysis has revealed the vital roles of active-site residues and other
secondary structure elements. However, clear understanding of the roles of: (1) the functionally important
conformational sub-states (or rare intermediates); (2) the distal regions including the conserved residues and
surface loops; and (3) the surrounding solvent, in enzyme catalysis still remain elusive. For close to two
decades, Agarwal lab has been working on using joint computational-experimental approaches for obtaining
answers to several important questions about enzymes. Investigations of >20 different enzyme systems have
enabled us to contribute to building a biophysical model of enzyme catalysis, which is improving our knowledge
of these highly efficient molecular machines. We have discovered conserved network of residues linking
surface loop regions to the active-site in several medically important enzyme systems, and successfully
developed and validated quasi-anharmonic analysis (QAA) method for identification of conformational sub-
states. In this proposal, we describe computational investigations of several enzymes including human
ribonucleases, dihydrofolate reductase and biliverdin reductase. Using previously developed and new
approaches, the following key questions will be answered: (1) What roles do conformational sub-states play in
enzyme catalysis? Specifically, functionally important higher energy sub-states and their linkage to kinetics of
the rate-limiting step in enzyme cycle will be quantitatively characterized; (2) Energy flow within preferential
pathways (or network channels) formed by conserved residues will be characterized as the biophysical
mechanism for long-distance coupling; (3) Thermodynamical coupling between the surrounding environment
(solvent) and the enzyme structure and catalyzed reaction will be characterized. A combination of molecular
dynamics (MD) and new theoretical analysis methods will be used. We have and continue to work with a
number of experimental laboratories to validate our models and their outputs. Experimental data from NMR,
enzyme kinetics, X-ray and other techniques on wild type and mutant versions of enzyme systems will be used
to iteratively refine our models. These investigations will provide new insights into mechanism of long-distance
effects and insights into factors that contribute to the catalytic efficiency of enzymes. The developed software
will continue to be made available to the community and we will support a wide variety of labs in their
investigations of enzymes. Over long-term these efforts will lead to designing of better allosteric modulators
and designer enzymes for biotherapies.
