Protein–ligand docking with multiple flexible side chains

Abstract

In this work, we validate and analyze the results of previously published cross docking experiments and classify failed dockings based on the conformational changes observed in the receptors. We show that a majority of failed experiments (i.e. 25 out of 33, involving four different receptors: cAPK, CDK2, Ricin and HIVp) are due to conformational changes in side chains near the active site. For these cases, we identify the side chains to be made flexible during docking calculation by superimposing receptors and analyzing steric overlap between various ligands and receptor side chains. We demonstrate that allowing these side chains to assume rotameric conformations enables the successful cross docking of 19 complexes (ligand all atom RMSD < 2.0 Å) using our docking software FLIPDock. The number of side receptor side chains interacting with a ligand can vary according to the ligand's size and shape. Hence, when starting from a complex with a particular ligand one might have to extend the region of potential interacting side chains beyond the ones interacting with the known ligand. We discuss distance-based methods for selecting additional side chains in the neighborhood of the known active site. We show that while using the molecular surface to grow the neighborhood is more efficient than Euclidian-distance selection, the number of side chains selected by these methods often remains too large and additional methods for reducing their count are needed. Despite these difficulties, using geometric constraints obtained from the network of bonded and non-bonded interactions to rank residues and allowing the top ranked side chains to be flexible during docking makes 22 out of 25 complexes successful.

Q‐DockLHM: Low‐resolution refinement for ligand comparative modeling

“…Even moderate structural distortions of the modeled binding pockets that in principle should be tolerated as explained above (experimentally their binding pockets bind similar ligands, yet their RMSD is 2-3 Å), drastically interfere with the ability of the all-atom docking approaches to identify correct docking geometries. This problem might be alleviated by introducing flexibility into the receptor protein 64-66. However, inclusion of explicit receptor flexibility greatly increases the dimension of the conformational space and the simulation time 67,68; thus it is inapplicable in virtual screening experiments that typically involve docking a large collection of drug candidates with a computational effort of minutes per single compound.…”

Protein flexibility in docking and surface mapping

“…Other IFD methods for including protein flexibility include modeling flexibility for a limited number of receptor residues, Table 4a: GLIDE/PRIME (Sherman et al 2006a, b), SCaRE (Bottegoni et al 2008); discrete sampling, Table 4b: FlexX-Ensemble (Claussen et al 2001), a modified DOCK protocol (Wei et al 2004), Fleksy (Nabuurs et al 2007), FLIPDock (Zhao & Sanner, 2007, 2008), FITTED (Corbeil et al 2007, 2008; Corbeil & Moitessier, 2009), 4D Docking (Bottegoni et al 2009); and continuum sampling, Table 4c: F-DycoBlock (Zhu et al 2001), PC-RELAX (Zacharias, 2004, 2008), RosettaLigand (Davis & Baker, 2009), and implicit MD (Huang & Wong, 2009). …”

Protein Flexibility in Virtual Screening: The BACE-1 Case Study

“…12 Unfortunately, the main drawback of these methodologies is that they can be extremely time-intensive, hampering the screening of a chemically comprehensive database in a practical receptor-based virtual screening (RBVS). Nevertheless, such screens can be deeply impacted by the use of a rigid receptor structure since it can restrict the calculated binding pose of a specific ligand to a small fraction of the proper chemical space that could complement that receptor.…”