Dynamics of trimethoprim bound to dihydrofolate reductase--a deuterium NMR study

Increased substrate affinity in the Escherichia coli L28R dihydrofolate reductase mutant causes trimethoprim resistance

Dihydrofolate reductase (DHFR) is a ubiquitous enzyme with an essential role in cell metabolism. DHFR catalyzes the reduction of dihydrofolate to tetrahydrofolate, which is a precursor for purine and thymidylate synthesis. Several DHFR targeting antifolate drugs including trimethoprim, a competitive antibacterial inhibitor, have therefore been developed and are clinically used. Evolution of resistance against antifolates is a common public health problem rendering these drugs ineffective. To combat the resistance problem, it is important to understand resistance-conferring changes in the DHFR structure and accordingly develop alternative strategies. Here, we structurally and dynamically characterize Escherichia coli DHFR in its wild type (WT) and trimethoprim resistant L28R mutant forms in the presence of the substrate and its inhibitor trimethoprim. We use molecular dynamics simulations to determine the conformational space, loop dynamics and hydrogen bond distributions at the active site of DHFR for the WT and the L28R mutant. We also report their experimental kcat, Km, and Ki values, accompanied by isothermal titration calorimetry measurements of DHFR that distinguish enthalpic and entropic contributions to trimethoprim binding. Although mutations that confer resistance to competitive inhibitors typically make enzymes more promiscuous and decrease affinity to both the substrate and the inhibitor, strikingly, we find that the L28R mutant has a unique resistance mechanism. While the binding affinity differences between the WT and the mutant for the inhibitor and the substrate are small, the newly formed extra hydrogen bonds with the aminobenzoyl glutamate tail of DHF in the L28R mutant leads to increased barriers for the dissociation of the substrate and the product. Therefore, the L28R mutant indirectly gains resistance by enjoying prolonged binding times in the enzyme-substrate complex. While this also leads to slower product release and decreases the catalytic rate of the L28R mutant, the overall effect is the maintenance of a sufficient product formation rate. Finally, the experimental and computational analyses together reveal the changes that occur in the energetic landscape of DHFR upon the resistance-conferring L28R mutation. We show that the negative entropy associated with the binding of trimethoprim in WT DHFR is due to water organization at the binding interface. Our study lays the framework to study structural changes in other trimethoprim resistant DHFR mutants.

Structure, dynamics, and catalytic function of dihydrofolate reductase

Molecular motions are widely regarded as contributing factors in many aspects of protein function. The enzyme dihydrofolate reductase (DHFR), and particularly that from Escherichia coli, has become an important system for investigating the linkage between protein dynamics and catalytic function, both because of the location and timescales of the motions observed and because of the availability of a large amount of structural and mechanistic data that provides a detailed context within which the motions can be interpreted. Changes in protein dynamics in response to ligand binding, conformational change, and mutagenesis have been probed using numerous experimental and theoretical approaches, including X-ray crystallography, fluorescence, nuclear magnetic resonance (NMR), molecular dynamics simulations, and hybrid quantum/classical dynamics methods. These studies provide a detailed map of changes in conformation and dynamics throughout the catalytic cycle of DHFR and give new insights into the role of protein motions in the catalytic activity of this enzyme.

Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies

The application of an adaptive neuro-fuzzy inference system (ANFIS) has been developed for obtaining sufficient quantitative structure-activity relationships (QSAR) with high accuracy. To this end, a data set of 68 pyrimidines derivatives as DHFR inhibitors, described first in the excellent independent studies of Hansch et al. (J. Med. Chem. 1982, 25, 777-784 and J. Med. Chem. 1991, 34, 46-54) and later by So and Richards (J. Med. Chem. 1992, 35, 3201-3207), was examined. The ANFIS system, first time applied in the literature to QSAR studies, was trained using a hybrid algorithm consisting of back-propagation and least-squares estimation while the optimum number and shape of membership functions were obtained through the subtractive clustering algorithm. Prior to the development and evaluation of the ANFIS system, geometry optimization of the examined compounds was performed, deriving a series of diverse descriptors from which the best subset was selected by using a hybrid genetic algorithm system. The predictive abilities of the resulting models compared to those produced from classical multivariate regression such as linear and nonlinear (quadratic) partial least squares regression (PLS and QPLS, respectively). The ANFIS method outperformed both the PLS models as well as the published results, leading to substantial gain in both the prediction ability and the computation speed (almost instant training).