Chemistry Reference
In-Depth Information
are then connected, to the fragments and docked into the target receptor. Docking poses are assessed in terms of root
mean squared deviation from the known positions of the fragment molecules as well as docking scores, should
known inhibitors be available [96].
A recent work was presented assessing the role of polarization in docking. The strategy for including ligand and
protein polarization in docking is based on the conversion of induced dipoles to induced charges. These have a
distinct advantage in that they are readily implemented into a number of different computer programs, including
many docking programs and hybrid QM/MM programs. Induced changes are also more readily interpreted. In this
study the ligand was treated quantum mechanically to avoid parameterization issues and was polarized by the target
protein, which was treated as a set of point charges. The induced dipole at a given target atom, due to polarization by
the ligand and neighboring residues, was reformulated as induced charges at the given atom and its bonded
neighbors and these were allowed to repolarize the ligand in an iterative manner. The final set of polarized charges
was evaluated against the default empirical Gasteier charges, and against nonpolarized and partially polarized
potential-derived charges. Inclusion of polarization does not always lead to the lowest energy pose having a lower
RMSD. However, whenever an improvement in methodology, corresponding to a more thorough treatment of
polarization, resulted in an increased cluster size, then there was also a corresponding decrease in the RMSD. The
options for implementing polarization within a purely classical docking framework are discussed [91].
A Bayesian model averaging for ligand discovery was recently reported [100]. The Bayesian analysis of high-
dimensional descriptor data using Markov chain Monte Carlo (MCMC) simulations for learning classification trees
is a novel method for pharmacophore and ligand discovery. Experimentally determined binding affinity data is used
to assess model averaging algorithms and then applied to large databases. The main Bayesian algorithm, in addition
to achieving high specificity and sensitivity, also lends itself naturally to classifying test sets with missing data and
providing a ranking for the classified compounds. The approach has been used to select and rank potential
biologically active compound and could provide a powerful tool in compound testing.
A study was presented regarding identifying receptor-ligand interactions through an ab initio approach. A qualitative
relation was demonstrated between the electric characteristics and binding affinity of a complex receptor-ligand; a
large binding affinity correlates with a large charge transfer. This allows the analysis of binding interactions of any
complex using small computational resources with acceptable reliability of the results [402].
A novel scheme was proposed in which a panel of plausible pharmacophore hypothesis candidates were assembled
to construct a pharmacophore ensemble (PhE) which in turn was treated as input for regression analysis via support
vector machines (SVM). Each pharmacophore member in the PHE represents a protein conformation or a number of
protein conformations with closed spatial arrangements. Unlike any other analog-based modeling methods, this
PhE/SVM scheme can take into account protein plasticity, which is of critical importance to be addressed when the
target protein can adopt significantly various conformations to interact structurally with diverse ligands, by using
PhE in place of protein conformation ensemble [234].
MOLECULAR INTERACTION FIELDS
For characterizing the ability of a molecule to interact with other molecules, Molecular Interaction Fields (MIF) are
extremely useful. The Molecular Electrostatic Potentials can represent the energy of interaction of a molecule with a
positive charge located at the cartesian coordinates of the molecule neighborhood. A more complex chemical probe
representing any kind of functional group can be used to replace the positive charge, which can highlight graphically
areas of space with energetically favorable interactions produced by molecules holding a probelike group [444-451].
The Grid-inDEPENDENT Descriptors (GRIND) represent [444] a class of alignment-independent three-
dimensional molecular descriptors derived in such a way as to be highly relevant for describing biological properties
of compounds. Chemically interpretable and easy to compute GRIND was obtained starting from a set of molecular
interaction fields, computed by the first step, in which the fields are simplified, and a second step, in which the
results are encoded into alignment-independent variables using a particular type of autocorrelation transform. The
molecular descriptors so obtained can be used to obtain graphical diagrams called 'correlograms' and can be used in
different chemometric analyses, such as principal component analyses or partial least-squares. An important feature
