Computational mutation scanning and drug resistance mechanisms of HIV-1 protease inhibitors

Abstract

The drug resistance of various clinically available HIV-1 protease inhibitors has been studied using a new computational protocol, that is, computational mutation scanning (CMS), leading to valuable insights into the resistance mechanisms and structure-resistance correction of the HIV-1 protease inhibitors associated with a variety of active site and nonactive site mutations. By using the CMS method, the calculated mutation-caused shifts of the binding free energies linearly correlate very well with those derived from the corresponding experimental data, suggesting that the CMS protocol may be used as a generalized approach to predict drug resistance associated with amino acid mutations. Because it is essentially important for understanding the structure-resistance correlation and for structure-based drug design to develop an effective computational protocol for drug resistance prediction, the reasonable and computationally efficient CMS protocol for drug resistance prediction should be valuable for future structure-based design and discovery of antiresistance drugs in various therapeutic areas.

Scheme 1

Molecular structures of HIV-1 protease inhibitors used in the present study

Figure 1

Structural distribution of the identified mutation sites (residues) of the HIV-1 protease (active site residues in red and non-active site residues in blue).

Figure 2

Workflow of the computational mutation scanning.

Figure 3

Six possible mechanisms for drug resistance: decrease in the enthalpy contribution to the binding affinity (A-type), decrease in the entropic contribution to the binding affinity (B-type), decrease in both the enthalpy and entropic contributions (C-type), no significant change in the enthalpy and entropic contribution (D-type), decrease in the enthalpy contribution compensated with increase in the entropic contribution (E-type), decrease in the entropic contribution compensated with increase in the enthalpy contribution (F-type). Enthalpy and entropy changes reflect different types of interactions. Thus these signals provide valuable clues for the rational design of anti-resistance drugs.

Figure 4

Linear correlation between the calculated and experimental binding free energy shifts for each of the six drugs in clinical use.

Figure 5

Thermodynamic representation of the drug resistance mechanism for each drug associated with each mutant of HIV-1 protease.

Figure 6

Superimposition of the MD-simulated molecular structures of APV, IDV, and SQV with the corresponding structures obtained from the CMS calculations. The superimposition was carried out on the backbone atoms of the protein. The structures obtained from the MD simulations are shown as colored sticks, whereas those from the CMS calculations are shown in deep blue. The color shown in the bottom bar refers to the magnitude of the RMSD.

Figure 7

The overall linear correlation between the calculated and experimental binding free energy shifts for all of the six clinical available drugs with various mutants of the HIV-1 protease.