Rational Modulation of the Induced-Fit Conformational Change for Slow-Onset Inhibition in Mycobacterium tuberculosis InhA.

Slow-onset enzyme inhibitors are the subject of considerable interest as an approach to increasing the potency of pharmaceutical compounds by extending the residence time of the inhibitor on the target (the lifetime of the drug-receptor complex). However, rational modulation of residence time presents significant challenges because it requires additional mechanistic insight, such as the nature of the transition state for postbinding isomerization. Our previous work, based on X-ray crystallography, enzyme kinetics, and molecular dynamics simulation, suggested that the slow step in inhibition of the Mycobacterium tuberculosis enoyl-ACP reductase InhA involves a change in the conformation of the substrate binding loop from an open state in the initial enzyme-inhibitor complex to a closed state in the final enzyme-inhibitor complex. Here, we use multidimensional free energy landscapes for loop isomerization to obtain a computational model for the transition state. The results suggest that slow-onset inhibitors crowd key side chains on helices that slide past each other during isomerization, resulting in a steric clash. The landscapes become significantly flatter when residues involved in the steric clash are replaced with alanine. Importantly, this lower barrier can be increased by rational inhibitor redesign to restore the steric clash. Crystallographic studies and enzyme kinetics confirm the predicted effects on loop structure and flexibility, as well as inhibitor residence time. These loss and regain of function studies validate our mechanistic hypothesis for interactions controlling substrate binding loop isomerization, providing a platform for the future design of inhibitors with longer residence times and better in vivo potency. Similar opportunities for slow-onset inhibition via the same mechanism are identified in other pathogens.

Folding simulations for proteins with diverse topologies are accessible in days with a physics-based force field and implicit solvent.

The millisecond time scale needed for molecular dynamics simulations to approach the quantitative study of protein folding is not yet routine. One approach to extend the simulation time scale is to perform long simulations on specialized and expensive supercomputers such as Anton. Ideally, however, folding simulations would be more economical while retaining reasonable accuracy, and provide feedback on structure, stability and function rapidly enough if partnered directly with experiment. Approaches to this problem typically involve varied compromises between accuracy, precision, and cost; the goal here is to address whether simple implicit solvent models have become sufficiently accurate for their weaknesses to be offset by their ability to rapidly provide much more precise conformational data as compared to explicit solvent. We demonstrate that our recently developed physics-based model performs well on this challenge, enabling accurate all-atom simulated folding for 16 of 17 proteins with a variety of sizes, secondary structure, and topologies. The simulations were carried out using the Amber software on inexpensive GPUs, providing ∼1 µs/day per GPU, and >2.5 ms data presented here. We also show that native conformations are preferred over misfolded structures for 14 of the 17 proteins. For the other 3, misfolded structures are thermodynamically preferred, suggesting opportunities for further improvement.