In Silico Development of Quorum‐Sensing Inhibitors

Abstract

Quorum sensing (QS) is a chemical communication between bacteria, with which bacteria sense the population of their own species. Autoinducer‐2 (AI‐2) is a class of universal quorum‐sensing molecules, which is used by both Gram‐negative and Gram‐positive bacteria. The inhibition of AI‐2‐mediated QS has various practical applications, including the prevention of the formation of biofilm in dental gums. In this work, we develop a computational protocol for developing AI‐2 inhibitors. A challenging aspect of such an endeavor is that the receptor undergoes a large conformational change upon ligand binding. We combine several methods such as molecular docking with multiple conformations, molecular dynamics simulations, and molecular mechanics Poisson–Boltzmann computation, in order to estimate binding affinity of candidate molecules to a quorum‐sensing receptor. We apply our method to rank the substances in a chemical library. We indeed find a molecule that has a higher affinity than previously known ligands, thus showing the feasibility of the protocol for the development of quorum‐sensing inhibitors.

In Silico Screening of Quorum Sensing Inhibitor Candidates Obtained by Chemical Similarity Search

“…Previously, AI2 inhibiting capability of D-galactose has been found by a method purely based on bioinformatics, where the sequences of known quorum sensing receptors, LuxP of V. harveyi , LsrB of S. typhimurium , and RbsB of A. actinomycetemcomitans were searched against protein sequences of F. nucleatum to find that the D-galactose binding protein have a sequence similar to these proteins and hence may function as a quorum-sensing receptor [ 15 ]. An alternate approach of using molecular docking and MD simulation have been employed, where a molecule with a high activity as AI-2 inhibitor has been found [ 22 ]. The current method is somewhat intermediate between these methods, because although we use molecular docking and MD simulations, we first select the initial candidates based on molecular similarity with known quorum-sensing inhibitors in order to increase the chance of finding a molecule with AI2 inhibiting capabilities.…”

“…The current method is also different from that of ref. [ 22 ], where intermediate conformations between open and closed forms of the quorum-sensing receptors were generated and used for molecular docking and subsequent MD simulations. Only the closed form of LuxP is used in this work for simplicity, which might lead to some bias towards relatively small molecules with a size similar to AI-2.…”

“…Nonetheless, it is still desirable to find AI2 receptor inhibiting molecules which are effective in small doses, easy to produce, and are not toxic. In this respect, it is particularly useful to look for QS activities of well-known substances in chemical databases [ 15 , 22 ]. Finding a hitherto unknown QS activities of such substances can lead to the development of novel QS inhibitors by drug-repositioning [ 23 ].…”

LuxS quorum sensing system and biofilm formation of oral microflora: A short review article

“…Even with all the compounds and molecules that have been identified to interfere with biofilm formation, it expected that not only biofilm in these organisms would be affected, but rather a compounded effect is expected to be seen upon deeper investigations. With the development of current computational technology, many scientists have taken the rout to use in silico approaches to identify molecules that inhibit biofilm formation ( Byeon et al, 2017 ). Such techniques are inexpensive and provide a lot of information that will allow scientist to work more comfortably before laboratory experimentation as the potential for these molecules have already been explored beforehand.…”

Plant-Derived Inhibitors of AHL-Mediated Quorum Sensing in Bacteria: Modes of Action

“…The in silico approach, which is used in medicinal chemistry for the discovery of enzyme inhibitors, is often applied to discover anti-QS compounds and evaluation of their bioactivity mechanisms. This approach involves combining several computational methods, including molecular docking with multiple conformations and molecular dynamic simulations, in order to estimate binding affinity of the investigated plant-derived molecules to QS-system-related proteins [25]. In some cases, the interaction between plant-derived molecules and target proteins is confirmed by direct protein–ligand interaction studies [26].…”