Antibiotic resistance has emerged as a major threat to public health. In Gram-negative bacteria, efflux complexes consist of an inner-membrane pump, a periplasmic adaptor protein and an outer-membrane channel. Drug efflux protein complexes confer multidrug resistance on bacteria by transporting a wide spectrum of structurally diverse antibiotics. A new molecular challenge in combat to this transport is the search for new molecules to block efflux and restore drug susceptibility to resistant clinical strains. In the first step, the outer and inner membrane of efflux pumps sequences were aligned. In the following, 3D flexible alignment was performed to identify similar conformational structures. 3D screening technologies were used to specifically identify cavities in protein structures and pharmacophore screening of chemical libraries. The pore channels structure and function were analyzed with steered molecular dynamics (AMBER program). MD simulation was performed to find which part of the efflux pumps is essential for initial binding to antibiotics. Common pharmacophore features were selected via Ligandscout on the basis of the sequences similarity and amino acids conformational arrangement of the protein. After selection of the best compounds using pharmacophore filters, we used infiniSee (REALspace, GalaXi space) to screen for lead molecules. The dataset was filtered with Mona using Lipinski’s rule of 5. Subsequently, lead compounds were docked in SeeSAR’s Docking mode. The molecules which had the best scores were selected for ADMET filtering. Based on ADMET in the OPTIBRIUM module, molecules that do not have toxicity were selected to be edited via SeeSAR‘s Molecule Editor mode, which gives us a distinct advantage to modify ligands. Then, the best-configured ligands were re-docked in SeeSAR. This step was done to amend small molecules affinity which improved binding intensity to entry gate of efflux pump. MD simulation analysis (RMSD, RMSF, MM-PBSA, MM-GBSA) over final edited ligands exhibited that modified small molecules are entirely stable in the entry gate, and the efflux pump cannot throw them out. Ten compounds that have the best RMSD, RMSF, MM-PBSA and MM-GBSA were selected for synthesis. Among these ten synthesized small molecules, four had good results in experimental tests. On the other hand, primers were designed for efflux pumps. The mutation was investigated by PCR, and sequencing. Subsequently, we conducted a homology modeling and MD simulation to identify mutation in efflux pumps in clinical isolations. As a result, we identified four mutations in the efflux pump proteins of 4 clinical isolations. Finally, it seems like, at least in the experimental study, a way was found for recursive evolution of bacteria to the age of the genesis antibiotic.
After 1 year, Azhar has achieved the following goals:
- We used the dataset to identify key residues of efflux pump proteins. After 3D flexible alignment, and MD simulation, conserved crucial residues has been identified in different efflux pump families. Amino acids sequence similarity and conformational arrangement on the proteins aided to find common pharmacophore features via Ligandscout software. In addition, pharmacophore modeling was done to the crucial conserved residues.
- In the following, pharmacophore-based virtual screening was used to identify small molecules. These small molecules were used to generate datasets using infiniSee. The obtained datasets of small molecules were docked, analyzed and edited in SeeSAR. Based upon these analysis, we have synthesized a set of ten compounds to undergo biological evaluation. Four of the ten synthesized small molecules could completely restore drug susceptibility to resistant clinical strains. These compounds sensitized bacteria to all antibiotics which are thrown out by efflux pumps.
- During evolution, efflux pumps had point mutations in their sequences. By the use of homology modeling and MD simulation, we identified four mutations in the efflux pump proteins of 4 clinical isolations. In the present study, an algorithm was created to predict the possibility of future point mutations of efflux pumps sequences.