Soft Docking: This technique allows small conformational relaxations by treating
van der Waals which overlaps through a softened potential and is efficient in terms
of computational cost, but it can only account for smaller relaxation in receptor
structure during binding to ligand [78]. Ferrari et al. [78] applied this method using
two cavities of T4 lysozyme and drug-target aldose reductase which undergo large
conformation change during binding. Available Chemicals Directory (ACD) [78]
was screened against chosen targets for evaluating the method. They reported, with
single receptor conformation, soft potential was better in identifying known ligands,
while with multiple receptor conformations, it was poor in identifying leads than
hard function; this trend was similar for both receptor and more pronounced for
aldose reductase. Soft docking gives better score for ligands and decoys thereby
better scoring, but it misses true ligands [78]. Qualitatively, similar results were
reported by soft-docking studies of protein–protein [79] and antigen–antibody [80]
interaction studies.
Side chain rotation: Allowing side chains rotation of the binding site residues of the
receptor is computationally costlier than soft docking but offers better ways to
account flexibility of receptor through sampling side chain rotations of binding site
residues and overcome the limitations of soft docking, avoiding unphysical van der
Walls clashes in predicted poses [81]. Preliminary idea of incorporating side chain
flexibility into docking through usage of rotamer states of the binding site residues
with rigid ligand conformation by Leach et al. [82] has been carried forward and
adapted in several studies. For example, approach of rigid anchor and flexible
complementary growth of ligand in receptor-binding site is implemented in SLIDE
by Schnecke et al. [83] and used it to screen for potential ligands of progesterone
receptor, dihydrofolate reductase, and a DNA-repair enzyme from a dataset of
175,000 organic compounds. Another approach introduced by Dean and co-workers
[84] is applied to successfully reproduce experimental pose of ligand in binding site
by docking synthetic inhibitor RS-104966 to the S1’ pocket of the human collagenase
matrix metalloproteinase 1 (MMP-1) [84]. In this approach, an ensemble of binding
site conformations was generated using side chain rotamer states of the binding site
residues followed by identification of representative conformations combining principal component analysis and fuzzy clustering [84]. Frimurer et al. performed a study
attempting to assess the extent of impact of flexible side chain conformations of
binding site residues on predicted binding poses and affinity [85]. They chose protein,
phosphatase tyrosine 1B co-crystalized with non-peptide inhibitors, and docked ligands to parent receptor structure, resulting correct poses to correlate with low predicted binding energy[85]. In the process, an ensemble of structures was generated
using rotameric states of subset of binding site residues (Asp48, Lys120, and
Phe182), and ligands were docked to each structure; correlation of binding affinity
with predicted scores improved for correct poses [85]. The importance of considering
side chain flexibility in docking is also highlighted in study of Gaudreault et al. They
created a curated non-redundant dataset of 188 proteins where unbound- and boundboth structures were already crystallized. In their study, they found that 90% binding
sites and side chain rotation were accounting the flexibility in it, and 30% of them
were essential side chain rotation and only 10% binding sites are rigid [86].
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S. K. Panday and I. Ghosh
van der Waals which overlaps through a softened potential and is efficient in terms
of computational cost, but it can only account for smaller relaxation in receptor
structure during binding to ligand [78]. Ferrari et al. [78] applied this method using
two cavities of T4 lysozyme and drug-target aldose reductase which undergo large
conformation change during binding. Available Chemicals Directory (ACD) [78]
was screened against chosen targets for evaluating the method. They reported, with
single receptor conformation, soft potential was better in identifying known ligands,
while with multiple receptor conformations, it was poor in identifying leads than
hard function; this trend was similar for both receptor and more pronounced for
aldose reductase. Soft docking gives better score for ligands and decoys thereby
better scoring, but it misses true ligands [78]. Qualitatively, similar results were
reported by soft-docking studies of protein–protein [79] and antigen–antibody [80]
interaction studies.
Side chain rotation: Allowing side chains rotation of the binding site residues of the
receptor is computationally costlier than soft docking but offers better ways to
account flexibility of receptor through sampling side chain rotations of binding site
residues and overcome the limitations of soft docking, avoiding unphysical van der
Walls clashes in predicted poses [81]. Preliminary idea of incorporating side chain
flexibility into docking through usage of rotamer states of the binding site residues
with rigid ligand conformation by Leach et al. [82] has been carried forward and
adapted in several studies. For example, approach of rigid anchor and flexible
complementary growth of ligand in receptor-binding site is implemented in SLIDE
by Schnecke et al. [83] and used it to screen for potential ligands of progesterone
receptor, dihydrofolate reductase, and a DNA-repair enzyme from a dataset of
175,000 organic compounds. Another approach introduced by Dean and co-workers
[84] is applied to successfully reproduce experimental pose of ligand in binding site
by docking synthetic inhibitor RS-104966 to the S1’ pocket of the human collagenase
matrix metalloproteinase 1 (MMP-1) [84]. In this approach, an ensemble of binding
site conformations was generated using side chain rotamer states of the binding site
residues followed by identification of representative conformations combining principal component analysis and fuzzy clustering [84]. Frimurer et al. performed a study
attempting to assess the extent of impact of flexible side chain conformations of
binding site residues on predicted binding poses and affinity [85]. They chose protein,
phosphatase tyrosine 1B co-crystalized with non-peptide inhibitors, and docked ligands to parent receptor structure, resulting correct poses to correlate with low predicted binding energy[85]. In the process, an ensemble of structures was generated
using rotameric states of subset of binding site residues (Asp48, Lys120, and
Phe182), and ligands were docked to each structure; correlation of binding affinity
with predicted scores improved for correct poses [85]. The importance of considering
side chain flexibility in docking is also highlighted in study of Gaudreault et al. They
created a curated non-redundant dataset of 188 proteins where unbound- and boundboth structures were already crystallized. In their study, they found that 90% binding
sites and side chain rotation were accounting the flexibility in it, and 30% of them
were essential side chain rotation and only 10% binding sites are rigid [86].
124
S. K. Panday and I. Ghosh