FlexAID: Revisiting docking on non native-complex structures

Small-molecule protein docking is an essential tool in drug design and to understand molecular recognition. In the present work we introduce FlexAID, a small-molecule docking algorithm that accounts for target side-chain flexibility and utilizes a soft scoring function, i.e. one that is not highly dependent on specific geometric criteria, based on surface complementarity. The pairwise energy parameters were derived from a large dataset of true positive poses and negative decoys from the PDBbind dataset through an iterative process using Monte Carlo simulations. The prediction of binding poses is tested using the independent Astex dataset while performance in virtual screening is evaluated using a subset of the DUD dataset. We compare FlexAID to AutoDock Vina, FlexX, and rDock in an extensive number of scenarios to understand the strengths and limitations of the different programs as well as to reported results for Glide, GOLD and DOCK6 where applicable. The most relevant among these scenarios is that of docking on flexible non native-complex structures where as is the case in reality, the target conformation in the bound form is not known a priori. We demonstrate that FlexAID, unlike other programs, is robust against increasing structural variability. FlexAID obtains equivalent sampling success as GOLD and performs better than AutoDock Vina or FlexX in all scenarios against non native-complex structures. FlexAID is better than rDock when there is at least one critical side-chain movement required upon ligand binding. In virtual screening, FlexAID rescored results are comparable to those of AutoDock Vina and rDock. The higher accuracy in flexible targets where critical movements are required, intuitive PyMOL-integrated graphical user interface and free source code as well as pre-compiled executables for Windows, Linux and Mac OS make FlexAID a welcome addition to the arsenal of existing small-molecule protein docking methods. AUTHOR SUMMARY Protein ligand interactions are essential to understand biological processes such as enzymatic reactions, signalling pathways as well as in the development of new medicines. Docking algorithms permit to predict the structure of a ligand protein complex at the atomic level. Several docking algorithms were developed over the years with a tendency towards utilizing very specific and detailed (i.e., hard) descriptions of molecular interactions. In this work we present a new docking algorithm called FlexAID that utilizes a very general and superficial (i.e., soft) description of interactions based on atomic surface areas in contact. We demonstrate that FlexAID can achieve better accuracy in predicting the structure of ligand protein complexes than existing accessible widely used or state-of-the-art methods in real scenarios when using flexible targets harbouring structural differences with respect to the final protein structure present in the ligand protein complex. FlexAID and its PyMOL-integrated graphical user interface are free, easy to use and available for Windows, Linux and Mac OS.