Current
Computer-Aided Drug Design
ISSN: 1573-4099
Current Computer-Aided
Drug Design
Volume 2, Number 3, September 2006
Contents
Molecular Dynamics Simulations and Analysis of
ABC Transporters Pp. 203-214
Anthony M. George and Peter M. Jones
[Abstract]
An Introduction to Molecular Modeling of G-Protein
Coupled Receptors Pp. 215-227
Minghua Wang, Lakshmi P. Kotra and David R. Hampson
[Abstract]
Molecular Modeling Studies of Estrogen Receptor Modulators
Pp. 229-253
Subhendu Mukherjee and Achintya Saha
[Abstract]
Recent Advances in Chemometric Methodologies for QSAR
Studies Pp. 255-266
Wei-Qi Lin, Jian-Hui Jiang,Hai-Long Wu, Guo-Li Shen and
Ru-Qin Yu
[Abstract]
Systems Biology and Computer-Aided Drug Discovery
Pp. 267-274
Timothy G. Lilburn and Yufeng Wang
[Abstract]
Analysis of Similarity/Dissimilarity of DNA Primary
Sequences Based on Condensed Matrices and Information Entropies
Pp. 275-285
Bo Liao and Wen Zhu
[Abstract]
Recent Advances in Free Energy Calculations with a
Combination of Molecular Mechanics and Continuum Models Pp.
287-306
Junmei Wang, Tingjun Hou and Xiaojie Xu
[Abstract]
Cancer and Aids: New Trends in Drug Design and Chemotherapy
Pp. 307-324
Carlton Anthony Taft and Carlos Henrique Tomich de Paula
da Silva
[Abstract]
Abstracts
[Back to top]
Molecular Dynamics Simulations and Analysis of ABC
Transporters
Anthony M. George and Peter M. Jones
The increasing availability of atomic-level protein structures
derived from X-ray crystallography and NMR spectroscopy, together
with advances in computational power, have ushered in a new
era of powerful theoretical approaches to study protein mechanisms
and, by extension, use a computer-aided structural approach
to drug design. Classical molecular dynamics calculations,
in which Newton's equations of motion are solved for all atoms
in the system, has emerged as an important tool for analysing
protein dynamics at physiologically relevant timescales, in
ways that are either very difficult or impossible to do experimentally.
Indeed, the computer is becoming a kind of virtual microscope
that can observe things not observable by any other means.
The availability of more sophisticated parallel computer clusters
and program suites will lead to simulations that will be capable
of examining entire processes such as polypeptide folding
pathways and reaction mechanisms. In this review, the incipient
application of molecular dynamics analysis of ABC (ATP-Binding
Cassette) transporters is surveyed and discussed, with particular
relevance to unresolved and controversial issues.
[Back to top]
An Introduction to Molecular Modeling of G-Protein
Coupled Receptors
Minghua Wang, Lakshmi P. Kotra and David R. Hampson
G-protein coupled receptors (GPCRs) are the largest single
family of signaling molecules in mammals and represent approximately
2-3% of all genes in the human genome. Estimates of the total
number of GPCR genes in the human genome range from about
750 to 1000. GPCRs mediate signaling by a wide variety of
ligands including amino acids, ions, biogenic amines, peptides,
glycoproteins, light, pheromones, and odorants. There are
presently only a handful of GPCRs whose structures have been
elucidated. Of these, the mGluR1 subtype of metabotropic glutamate
receptor (mGluR) and rhodopsin are the most widely used in
modeling GPCRs. In the case of mGluR1, the three-dimensional
structure of the extracellular ligand binding domain of the
molecule has been solved, while the crystallographic data
for rhodopsin encompasses the whole protein in the ground
state with bound 11-cis-retinal. In this review,
we discuss the use of homology modeling to investigate the
structures and functions of GPCRs. We illustrate the use of
homology modeling with a particular emphasis on ligand and
drug binding sites in the Family C subfamily of GPCRs.
[Back to top]
Molecular Modeling Studies of Estrogen Receptor Modulators
Subhendu Mukherjee and Achintya Saha
The dimensional expansion in the research domain of Selective
Estrogen Receptor (ER) Modulators (SERM) has been driven by
discovering molecules with improved endocrine profiles that
might be safer and valuable drug candidates for treating variety
of estrogen-linked pathologies. Desirable tissue selectivity
may result from the unique structural characteristics of a
ligand that take advantage of differences in diversities of
cell specific factors. The recent discovery of a second ER
has provoked the search for ligands which are selective for
either the classical ER or newer subtypes. Libraries of compounds,
both synthetic and natural are being screened globally for
finding ideal SERMs and investigating pharmacophore patterns
for apprehending tissue selective parameters. Diverse series
of selective synthetic analogs have been developed with high
relative binding affinities to the ER as comparable to 17β-estradiol
and extensive data sets of phytoestrogens have also been screened
for selective binding at the ER surfaces. The successful synthesis,
exploration of natural resources and biological testing of
SERMs are emerging as vital tools for apprehending the differences
in structure and biological functions of ER subtypes as well
as for deducing pharmacophore maps of estrogenic analogs through
application of virtual molecular modeling applications. Several
approaches in calculating ligand-binding affinities have been
used over the past decade, ranging from molecular field analysis
studies to protein-based methods using empirical scoring functions.
One of the most promising areas in present day computational
chemistry that has further aided the understanding of mechanistic
aspects of estrogenic activity, is the characterization of
molecular properties and bio-activities by means of structure-based
descriptors generated from theoretical improvement and computational
applications that eventually lead to construction of quantitative
SAR related to molecular features by statistical procedures.
Consequently, this paper overviews the properties investigated
towards explaining tissue selectivity of estrogens and structural
homology patterns of active analogs on precision based In
silico approaches. This review also takes into account
some our ongoing research efforts in this area that have contributed
significant findings.
[Back to top]
Recent Advances in Chemometric Methodologies for QSAR
Studies
Wei-Qi Lin, Jian-Hui Jiang,Hai-Long Wu, Guo-Li Shen and
Ru-Qin Yu
In recent years chemometrics has been undergoing an exciting
development both in the applied areas and in the theoretical
and methodological aspects. This review is focused on recent
advances in chemometric methodologies for quantitative structure-activity
relationship (QSAR) studies, and it covers multiple applications.
QSAR is one of the tools for the computer-aided drug design;
it is also an important branch of chemometrics. The feature
or variable selection is an important aspect in QSAR studies.
Basic requirements and different algorithms for feature or
variable selection are briefly discussed. Moreover, an overview
of the state-of-the-art chemometric methods developed to combat
the shortcomings of conventional algorithms and their applications
in QSAR is given. A survey of innovative chemometric approaches
in QSAR model construction is also presented. Some remarks
and outlook about QSAR studies applied to the computer-aided
drug design have also been discussed.
[Back to top]
Systems Biology and Computer-Aided Drug Discovery
Timothy G. Lilburn and Yufeng Wang
With the advent of the post-genomic era, systems biology has
set the stage for a quantum leap in our understanding of the
fundamental cellular processes, phenotypic variations, and
disease mechanisms. By integrating the vast amount data from
genomics, transcriptomics, and proteomics analyses, systems
biology seeks a holistic view of organisms and the modules
that compose them. The approaches to a systems level view
of biology can be broadly classified as either deterministic
or probabilistic. The former approach generates highly detailed
views that are mechanistic and often quantitative, while the
latter type of approach generates high level views that have
usually been qualitative. An ability to see and describe the
systems-level processes in the organism and thereby identify
potential vulnerabilities could expedite the process of drug
discovery and development. A systems biology approach will
aid in at least four of the major stages of this process:
target identification, target validation, preclinical testing
and clinical trials. We give an overview of systems biology
and describe, with examples, how it is being used for drug
discovery and development.
[Back to top]
Analysis of Similarity/Dissimilarity of DNA Primary
Sequences Based on Condensed Matrices and Information Entropies
Bo Liao and Wen Zhu
The primary sequence of DNA is a sequence of nucleotides over
the four-letters alphabet {A, C, G, T}. Characteristic sequences
of a DNA sequence are given in term of classification of bases
of nucleotides. Using the characteristic sequences, we construct
a set of 3 x 8 matrices and a set of 2 x 2 matrices to represent
DNA primary sequences and define the information entropy,
which is based on counting all triplets of characteristic
sequences. Similarity and dissimilarity analysis based on
the condensed matrices and the information entropies are given
for the first exon of beta-globin genes sequences belonging
to eleven different species.
[Back to top]
Recent Advances in Free Energy Calculations with a
Combination of Molecular Mechanics and Continuum Models
Junmei Wang, Tingjun Hou and Xiaojie Xu
Recently, the combination of state-of-the-art molecular mechanical
force fields with continuum solvation models enables us to
make relatively accurate predictions of both structures and
free energies for macromolecules from molecular dynamics trajectories.
The first part of this review is focused on the history and
basic theory of free energy calculations based on physically
effective energy functions. The second part illustrates the
applications of free energy calculations on many biological
systems, including proteins, DNA, RNA, protein-ligand, protein-protein,
protein-nucleic acid complexes, etc. Finally, the prospective
and possible strategies to improve the techniques of MM-PBSA
and MM-GBSA is discussed.
[Back to top]
Cancer and Aids: New Trends in Drug Design and Chemotherapy
Carlton Anthony Taft and Carlos Henrique Tomich de Paula
da Silva
We present a comprehensive but not exhaustive review of new
trends in drug discovery and targets for cancer and AIDS which
begins by highlighting the different historical stages of
drug discovery from clinical diagnosis, natural compounds,
serendipity and chemotherapy to the present age of modern
experimental and computational techniques. The current state
of the art of computer-aided drug design and selected recent
applications to Cancer and AIDS are discussed. Novel targets
and drugs in Cancer and AIDS are analyzed. In conclusion,
future perspectives for drug discovery, design and therapeutics
in cancer and AIDS are presented.
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