The impact of conformational entropy on the accuracy of the molecular docking software FlexAID in binding mode prediction
Creators: name: Louis-Philippe Morency
name: Najmanovich, Rafael
Publisher: name: F1000Research
Year: 2017

description: Here we show the newest implementation of Flexible Artificial Intelligence Docking (FlexAID) allowing its scoring function to consider the conformational entropy of ligand and biomolecules complexes. The higher accuracy of FlexAID1 on complex cases, the addition of novel features, i.e. the conformational entropy, its accessibility and its easy-to-use graphical user interface place FlexAID in an interesting position to tackle biologically and pharmacologically relevant situations currently ignored by other methods.<br><br>FlexAID<sup>1</sup> is available as a command-line pre-compiled executable (available at http://bcb.med.usherbrooke.ca/flexaid for Windows, macOS &amp; Linux) or through the NRGsuite, a PyMOL integrated user interface allowing the user to use FlexAID in an intuitive manner with real time visualization. Both the NRGsuite<sup>2</sup> and FlexAID<sup>1</sup> are distributed as open-source software.<br><br>Introduction<br>A major goal of molecular docking is to predict the experimentally observed binding mode between a biomolecule, i.e. a polymer of amino or nucleic acids, and a ligand— e.g. small molecules, peptides or nucleic acids. This computational method is used to study the structure of the molecular interactions involved in cell’s essential biological functions. Actual molecular docking methods are developed to evaluate the molecular interactions of a single conformation, a single pose at a time, and they are trained to estimate the enthalpic fraction of the binding free energy. Consequently, most current molecular docking methods fail to efficiently model the entropic contributions, especially those of conformational nature, who are fundamental in molecular recognition events. <br><br>Our research group develops FlexAID<sup>1</sup>, an accessible and competitive ligand and biomolecule molecular docking software whose focus is on molecular flexibility. Here we introduce FlexAID’s newest feature that allows its scoring function to estimate the conformational entropy by redefining the static binding mode usually predicted in molecular docking into a dynamic collection of similar poses evaluated altogether.<br><br>Methods<br>We implemented the new scoring function in FlexAID, allowing its genetic algorithm to select less favourable, but frequently observed binding modes, with a probability following a Boltzmann distribution. This implementation allows FlexAID to consider conformational entropy of the complexes during the molecular docking simulation. The core of the implementation of the conformational entropy in FlexAID resides within an unsupervised and density-based molecular classification algorithm charged to group similar poses together into binding modes, thus redefining a binding mode as a dynamic collection of poses scored altogether as it an be seen in Figure 1.<br><br><br>Results &amp; Conclusions<br>We present the impact of FlexAID’s newest feature on its accuracy in binding mode prediction using three increasingly complex scenarios: the Astex Diverse Set<sup>4</sup>, the Astex Non Native Set<sup>5 </sup>and the HAP2<sup>6</sup> dataset. We show that FlexAID outperforms other open-source molecular docking methods when molecular flexibility is crucial. Furthermore, FlexAID now outputs multiple conformations per binding mode, a novelty that allows the user to visualize the dynamics of the complex studied. We believe that its higher accuracy in complex scenarios, the addition of novel features, e.g. the conformational entropy, its accessibility and its easy-to-use graphical user interface, the NRGsuite<sup>2</sup>, place FlexAID in an interesting position to tackle biologically and pharmacologically relevant situations currently ignored by other molecular docking methods.<br><br>References<br>1. J. Chem. Inf. Model. 55: 1323–1336 (2015).<br>2. Bioinformatics btv458 (2015). <br>3. PLoS Comput Biol 10, e1003569 (2014).<br>4. J. Med. Chem. 50, 726–741 (2007).<br>5. J. Chem. Inf. Model. 48, 2214–2225 (2008).<br>6. Bioinformatics 28, i423–i430 (2012).