Computational Studies of E. coli DHFR: Drug Design, Dynamics, and Method Development.

Abstract

Dihydrofolate reductase (DHFR) catalyzes the NADPH-dependent reduction of dihydrofolate to tetrahydrofolate. As the only source of tetrahydrofolate (an important precursor in the biosynthesis of purines, thymidylate, and several amino acids), it has been a long-standing anti-cancer target and a classic system for structure-based drug design (SBDD). Escherichia coli DHFR (ecDHFR) is a canonical system for studying enzyme structure, dynamics, and catalysis. Protein flexibility and dynamics are of utmost importance in understanding the structure and mechanism of DHFR. This has been well investigated computationally and experimentally. The conformation of the M20 loop is particularly important to the catalytic cycle, as its three major conformations (open, closed, and occluded) are known to regulate ligand affinity and turnover. In addition to these static conformational differences, correlated dynamics are known to be of primary importance, showing distinct changes during different stages of the catalytic cycle. The dynamics have been used to explain the effects of distal mutations. We have performed two 10-ns molecular dynamics simulations of the ecDHFR•NADPH complex. We discovered transient, sub-nanosecond, correlated dynamics that correspond to correlations found in the catalytically active state. These dynamics involve both the protein and the cofactor. We found conformational changes that clearly indicate preorganization of the binding site related to folate binding. We have also discovered a potential new allosteric site, supported by extensive computational work as well as by crystallographic and mutagenesis results in the literature.

Traditional SBDD techniques focus on static structures. In 1999, Carlson and coworkers introduced the MPS (multiple protein structure) method as a way of incorporating protein flexibility into SBDD. The extreme importance of flexibility for DHFR makes the MPS method particularly appropriate. To improve the method, we developed new techniques for flooding and automatically clustering the solvent-mapping probes used in the procedure. We generated models from simulations starting with the M20 loop in both open and closed conformations. The MPS models preferentially identified high-affinity inhibitors over drug-like non-inhibitors.