Enabling the future of computational enzyme design.

The rational design of artificial biocatalysts with catalytic activities equaling those of natural enzymes is an unsolved problem of fundamental and pervasive importance to the entire field of biology. Computational approaches to enzyme design are beset with considerable difficulties because an enzyme is a large, complex system with numerous degrees of freedom, and yet the calculation of chemical reactivity requires accurate prediction of structures and energetics. In the course of its development to date, the field of enzyme design has typically focused on addressing the larger length scales computationally, while relying solely on experimental techniques for the optimization of chemical function, usually resulting in catalytic activities orders of magnitude below those of natural enzymes.

At PMC-AT, we are devoting considerable effort toward developing various components of a high-resolution approach to enzyme design that aims to improve catalytic activities by substantially reducing the emphasis on combinatorial screening in favor of computational sampling. The essential difference between our approach to enzyme design and others is its emphasis on protein structure refinement ‚ including attention to the physics of aqueous solvation, hydrogen-bonding, and electrostatic interactions ‚ and the quantum chemical details of reactive chemistry. Our methods have been cited as representing the state-of-the-art in the field today.

Selected publications:

R. Chakrabarti, A. M. Klibanov, and R. A. Friesner, "Computational prediction of native protein-ligand binding and enzyme active site sequences," Proc. Natl. Acad. Sci. USA 102: 10153-10158 (2005).

Highlighted in: "Progress in computational protein design," Curr. Opin. Biotechnol. 18: 1-7 (2007).

R. Chakrabarti, A. M. Klibanov, and R. A. Friesner, "Sequence optimization and designability of enzyme active sites," Proc. Natl. Acad. Sci. USA 102: 12035-12040 (2005).

Highlighted in: "Do-it yourself enzymes," Nature Chemical Biology 4, 273 - 275 (2008).

R. Chakrabarti, H. Rabitz, S. Springs, and G. McLendon, "Mutagenic evidence for the optimal control of evolutionary dynamics," Phys. Rev. Lett. 100:258103 (2008).

Princeton University has drafted a press release on this work.