point complementarity methods, distance geometry methods, tabu searches, and
systematic searches. They briefly presented algorithms and validations of models
and techniques using test cases as examples. The study has concluded that hybrids
of various types of algorithms employing novel search for appropriate poses and
consensus scoring are better for large-scale docking [12]. It has been observed that
rigid receptor and flexible ligand models achieved success rates of 70–80%. It can
be influenced by the fact that programs implementing these algorithms were well
established at that time [12]. However, they pointed out that possible reason for
failure is underestimation of conformational sampling of receptor flexibility [12]. In
spite of great success of docking methods in discriminating ligands as good and
bad, predicting the binding on the basis of their affinity towards cognate receptor is
poor. Moreover, in certain cases, docking shows inability to reproduce experimental
binding pose and it is a great concern in the technical aspects of the docking
methodology and its current progress, so need to review time to time. In 2010,
Huang et al. [13] have discussed currently practiced docking techniques, delineating the ways for ligand sampling, accounting protein flexibility and specific
scoring functions.
During a docking study, one has to do many sequences of tasks/steps which
influence the final outcome of the study and its success [14]. First and the foremost
thing is to search for the potential binding sites on the receptor and characterize
them; however, sometimes when binding site is not known blind docking can be
done. Several cavity detection algorithms and software were built to help this. In
parallel, right selection of the receptor structure is crucial [14]; thus, the quality of the
structure and experimental conditions used for resolving the structure has to be taken
care of, and structure resolved with experimental conditions closest to the actual
functioning condition should be preferred if available [15]. Most often, hydrogen
atoms are missing in the structure; thus, protonation states of the titratable receptor
residues have to be fixed, and usually, it is borrowed from predictions made using
different protonation state prediction tools [16, 17]. Apart from the protonation states
of titratable residues of the receptor, ionization states of ligands to be docked have
influence on correct model of binding [16, 18]. Scoring functions also greatly
influence the final outcome of the docking studies, and there are many scoring
functions available; some may be suitable to study the specific type of protein active
site but less effective in other cases [19]. Inherent demand of fast evaluation of poses
during docking enforces the scoring functions to adopt approximations and
parameterization, which compromises predictivity [19]. Thus, it is tough to guess
which scoring will be suitable for which kind of active site. However, chemical
intuition and consensus scoring protocols can be adopted to get better results.
Although the correctness of ranking and order of predicted affinity more often
fail to provide significant correlation with experimental ranking and observed pose
[20], such limitation of the in silico high-throughput screening can be partially
attributed to the multifaceted problems in current practices, e.g., selection of
appropriate binding theory, selection of appropriate modeling data, and limited
knowledge about the reaction mechanism. Many such challenges are discussed in
the present article.
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
111
systematic searches. They briefly presented algorithms and validations of models
and techniques using test cases as examples. The study has concluded that hybrids
of various types of algorithms employing novel search for appropriate poses and
consensus scoring are better for large-scale docking [12]. It has been observed that
rigid receptor and flexible ligand models achieved success rates of 70–80%. It can
be influenced by the fact that programs implementing these algorithms were well
established at that time [12]. However, they pointed out that possible reason for
failure is underestimation of conformational sampling of receptor flexibility [12]. In
spite of great success of docking methods in discriminating ligands as good and
bad, predicting the binding on the basis of their affinity towards cognate receptor is
poor. Moreover, in certain cases, docking shows inability to reproduce experimental
binding pose and it is a great concern in the technical aspects of the docking
methodology and its current progress, so need to review time to time. In 2010,
Huang et al. [13] have discussed currently practiced docking techniques, delineating the ways for ligand sampling, accounting protein flexibility and specific
scoring functions.
During a docking study, one has to do many sequences of tasks/steps which
influence the final outcome of the study and its success [14]. First and the foremost
thing is to search for the potential binding sites on the receptor and characterize
them; however, sometimes when binding site is not known blind docking can be
done. Several cavity detection algorithms and software were built to help this. In
parallel, right selection of the receptor structure is crucial [14]; thus, the quality of the
structure and experimental conditions used for resolving the structure has to be taken
care of, and structure resolved with experimental conditions closest to the actual
functioning condition should be preferred if available [15]. Most often, hydrogen
atoms are missing in the structure; thus, protonation states of the titratable receptor
residues have to be fixed, and usually, it is borrowed from predictions made using
different protonation state prediction tools [16, 17]. Apart from the protonation states
of titratable residues of the receptor, ionization states of ligands to be docked have
influence on correct model of binding [16, 18]. Scoring functions also greatly
influence the final outcome of the docking studies, and there are many scoring
functions available; some may be suitable to study the specific type of protein active
site but less effective in other cases [19]. Inherent demand of fast evaluation of poses
during docking enforces the scoring functions to adopt approximations and
parameterization, which compromises predictivity [19]. Thus, it is tough to guess
which scoring will be suitable for which kind of active site. However, chemical
intuition and consensus scoring protocols can be adopted to get better results.
Although the correctness of ranking and order of predicted affinity more often
fail to provide significant correlation with experimental ranking and observed pose
[20], such limitation of the in silico high-throughput screening can be partially
attributed to the multifaceted problems in current practices, e.g., selection of
appropriate binding theory, selection of appropriate modeling data, and limited
knowledge about the reaction mechanism. Many such challenges are discussed in
the present article.
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
111