5.5 Selection of Suitable Scoring Function
Whether to select just a scoring function or a consensus scoring function? A
suitable scoring function has important role to play to extract correct poses while
docking. Poses should be evaluated by the docking score or the ranks are better for
evaluation of docking; these are critical aspects influencing the final outcome of the
docking results. None of the available scoring functions appears to be fit in all cases
[206]. James B. Matthew and co-workers performed a study to evaluate performance of four individual scoring functions DOCK, GOLD, PMF, and FlexX and
several forms of consensus scores (CScore) derived from them, over a dataset of
twelve HIV protease and nine thermolysin complexes with known crystal structure
and experimental binding affinity [199]. Since DOCK and GOLD scoring functions
were not available in FlexX, they implemented these scoring functions according to
their open descriptions in the literature and will be referred by D-SCORE and
G-Score. They found that none of the considered scoring functions was consistently
good for all active sites [206], but the CScore (consensus score) was better than all
individual scoring function [199]. Secondly, they studied these scoring functions
for scoring candidate ligand configurations over a set of five known receptor ligand
complexes (2-MQPA or NAPAP into thrombin (1ETR and 1DWD), l–
3-phenyllactic acid into carboxypeptidase A (2CTC), 1-deoxynojirimycin into
glucoamylase (1DOG), and DANA into neuraminidase (1NSD) each of the ligand
was docked to cognate receptor, and top thirty configurations with most favorable
FlexX scores were chosen for further study, each of these configurations were
scored using D-SCORE, G-SCORE, PMF, rank-score, deprecated rank-sum
(rank-sum after leaving out worst rank), worst-best and CScore methods. They
found that average scores from several methods are better than individual score
[199]. Apart from this, their study highlighted that there could be alternate poses for
NAPAP binding in thrombin and DANA in neuraminidase as predicted by FlexX
along with crystal structure poses reproduced in Fig. 10a, b respectively.
Table 10 Typical physicochemical properties which are used to filter the chemical databases
Properties
Lead-likeness
Molecular weight (MW)
200–500
Lipophilicity (cLogP)
−4/4.2
H-bond donor
5
H-bond acceptor
10
Polar surface area (PSA)
170 Å
2
Number of rotatable bonds
10
CACO-2 membrane permeability
! 100
Solubility in water (log S)
−5/0.5
Others
Absence of both toxic and reactive fragments
148
S. K. Panday and I. Ghosh
Whether to select just a scoring function or a consensus scoring function? A
suitable scoring function has important role to play to extract correct poses while
docking. Poses should be evaluated by the docking score or the ranks are better for
evaluation of docking; these are critical aspects influencing the final outcome of the
docking results. None of the available scoring functions appears to be fit in all cases
[206]. James B. Matthew and co-workers performed a study to evaluate performance of four individual scoring functions DOCK, GOLD, PMF, and FlexX and
several forms of consensus scores (CScore) derived from them, over a dataset of
twelve HIV protease and nine thermolysin complexes with known crystal structure
and experimental binding affinity [199]. Since DOCK and GOLD scoring functions
were not available in FlexX, they implemented these scoring functions according to
their open descriptions in the literature and will be referred by D-SCORE and
G-Score. They found that none of the considered scoring functions was consistently
good for all active sites [206], but the CScore (consensus score) was better than all
individual scoring function [199]. Secondly, they studied these scoring functions
for scoring candidate ligand configurations over a set of five known receptor ligand
complexes (2-MQPA or NAPAP into thrombin (1ETR and 1DWD), l–
3-phenyllactic acid into carboxypeptidase A (2CTC), 1-deoxynojirimycin into
glucoamylase (1DOG), and DANA into neuraminidase (1NSD) each of the ligand
was docked to cognate receptor, and top thirty configurations with most favorable
FlexX scores were chosen for further study, each of these configurations were
scored using D-SCORE, G-SCORE, PMF, rank-score, deprecated rank-sum
(rank-sum after leaving out worst rank), worst-best and CScore methods. They
found that average scores from several methods are better than individual score
[199]. Apart from this, their study highlighted that there could be alternate poses for
NAPAP binding in thrombin and DANA in neuraminidase as predicted by FlexX
along with crystal structure poses reproduced in Fig. 10a, b respectively.
Table 10 Typical physicochemical properties which are used to filter the chemical databases
Properties
Lead-likeness
Molecular weight (MW)
200–500
Lipophilicity (cLogP)
−4/4.2
H-bond donor
5
H-bond acceptor
10
Polar surface area (PSA)
170 Å
2
Number of rotatable bonds
10
CACO-2 membrane permeability
! 100
Solubility in water (log S)
−5/0.5
Others
Absence of both toxic and reactive fragments
148
S. K. Panday and I. Ghosh