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Current Protein & Peptide
Science
ISSN: 1389-2042
Current Protein and Peptide
Science
Volume 7, Number 5, October 2006
Contents
Structure-Based Virtual Ligand Screening
Guest Editor: Bruno O. Villoutreix
Editorial Pp. 367
Bruno O. Villoutreix
Receptor-Based Computational Screening of Compound
Databases: The Main Docking-Scoring Engines Pp. 369-393
Olivier Sperandio, Maria A. Miteva, Francois Delfaud and
Bruno O. Villoutreix
[Abstract]
Methods for the Prediction of Protein-Ligand Binding
Sites for Structure-Based Drug Design and Virtual Ligand Screening
Pp. 395-406
Alasdair T.R. Laurie and Richard M. Jackson
[Abstract]
Scoring Functions for Protein-Ligand Docking Pp.
407-420
Ajay N. Jain
[Abstract]
eHiTS: An Innovative Approach to the Docking and
Scoring Function Problems Pp. 421-435
Zsolt Zsoldos, Darryl Reid, Aniko Simon, Bashir
S. Sadjad and A. Peter Johnson
[Abstract]
Structure Selection for Protein Kinase Docking
and Virtual Screening: Homology Models or Crystal Structures?
Pp.437-457
William M. Rockey and Adrian H. Elcock
[Abstract]
General Articles
Using Silico Methods Predicting Ligands for Orphan
GPCRs Pp. 459-464
Zhenran Jiang and Yanhong Zhou
[Abstract]
Latest Development in Drug Discovery on G Protein-coupled
Receptors Pp. 465-470
Kenneth Lundstrom
[Abstract]
Abstracts
[Back to top]
Editorial
Bruno O. Villoutreix
Structure-based Virtual Ligand Screening
The last two decades have witnessed the dawn of a new era
of “in silico-based” biology. These methods have
been playing a major role, from investigation of the genomes
to the design of new therapeutic compounds or prediction of
protein structures.
Lately, as computer technology has become cost-effective,
new ideas and concepts have emerged, such as in silico virtual
ligand screening with considerations of ligand and/or receptor
flexibility. These computer-assisted drug discovery methods
have been important in the past and are now part of most drug
discovery campaigns. There are many reasons for that, for
instance, Structural Genomics projects have enabled the determination
of many high quality protein structures, and several of them
are indeed potential drug targets. Further, parallel synthesis
allows for the production of millions of “drug-like”
molecules, thus potential drug candidates that could obstruct
active sites, impede macromolecular interactions or induce
conformational changes. In addition, Genome Projects have
identified over 10,000 targets, believed to be involved in
the pathogenesis of diseases, some of these targets should
be investigated rapidly as there obviously remains a significant
number of unmet clinical needs in many disease indications.
Many scientists around the World believe that, to use available
data most effectively for drug discovery projects, it is essential
to develop/apply reliable in silico high throughput screening
methods. Thus, in 2005, I thought that it could be interesting
to compile an issue about in silico screening methods based
on knowledge about the 3D structure of protein targets. I
am now delighted to present in this issue of Current Protein
and Peptide Science review papers pertaining to the continuously
evolving field of in silico screening and drug design.
The contributions cover a broad range of topics, from pocket
definition to docking, scoring and applications to homology
models. There should be something to interest everyone who
is involved with drug design. Because of the freedom of style
and subject matter afforded to the contributors, it was felt
necessary to open this issue with an introduction to the field.
Thus, in my laboratory, we wrote a review introducing structure-based
in silico screening and it is my hope that we have met this
requirement in full. Then, Laurie and Jackson provide a highly
readable introduction to pocket prediction while Jain describes
the science (art) of scoring including his new approach as
integrated in Surflex. Zsoldos et al. report for the first
time on the exciting use of eHiTS and compare their approach
with other tools. To the end of this issue, Rockey and Elcock
discuss the use of homology models for receptor-based in silico
screening with a special emphasis on the protein kinase family.
I believe that this special issue contains much valuable information
for protein scientists engaged in drug discovery campaigns.
Not all topics could be covered but the readers should be
able to find additional information in the references cited
in each review article.
I would like here again to thank the contributors for their
work and the reviewers for their suggestions. It has been
great pleasure to put this issue together.
I am grateful to Dr. Maria Miteva (Inserm, Paris, France),
Dr. Frederic Cazals (Inria, Sophia-Antipolis, France), Dr.
Ajay Jain (San Francisco, USA), Dr. Xueliang Fang (Ann Arbor,
USA), Dr. Wen Lee (Oxford, UK) and Dr. Anthony Nicholls (OpenEye,
USA) for comments about this special issue. Finally, last
but not least, I would like to give special thanks to Dr.
Ben Dunn and Mr. Ilyas (Senior Manager Publications, Bentham
Science Publishers Ltd.) for helping me bringing this special
issue to completion.
Bruno O. Villoutreix
Guest Editor
Current Protein & Peptide Science
INSERM U648, University Paris V
45 rue des Sts Peres, 75006 Paris,
France
E-mail: bruno.villoutreix@univ-paris5.fr
[Back to top]
Receptor-Based Computational Screening
of Compound Databases: The Main Docking-Scoring Engines
Olivier Sperandio, Maria A. Miteva, Francois
Delfaud and Bruno O. Villoutreix
The processes used by academic and industrial scientists
to discover new drugs have recently experienced a true renaissance
with many new and exciting techniques. The number of protein
structures and/or chemical ligands is constantly growing,
through the use of parallel chemistry, X-ray crystallography,
NMR or homology modeling methods and so is the theoretical
understanding of protein-ligand interactions. As such, structure-based
approaches to drug-design and in silico screening are becoming
routine part of most modern lead discovery programs. Prioritization
of compound libraries is an extremely important task that
aims at the rapid identification of tight-binding ligands
and ultimately new therapeutic compounds. These in silico
approaches combined with other experimental methods facilitate
the design of new medicines to treat cardiovascular, degenerative,
infectious, and neoplastic diseases, among others. Here, we
review key concepts and specific features of several selected
ligand–receptor docking/scoring methods while several
other topics pertaining to the field of in silico screening
are reviewed in the following articles of this special issue
of Current Protein and Peptide Science.
[Back to top]
Methods for the Prediction of Protein-Ligand
Binding Sites for Structure-Based Drug Design and Virtual
Ligand Screening
Alasdair T.R. Laurie and Richard M. Jackson
Structure Based Drug Design (SBDD) is a computational
approach to lead discovery that uses the three-dimensional
structure of a protein to fit drug-like molecules into a ligand
binding site to modulate function. Identifying the location
of the binding site is therefore a vital first step in this
process, restricting the search space for SBDD or virtual
screening studies. The detection and characterisation of functional
sites on proteins has increasingly become an area of interest.
Structural genomics projects are increasingly yielding protein
structures with unknown functions and binding sites. Binding
site prediction was pioneered by pocket detection, since the
binding site is often found in the largest pocket. More recent
methods involve phylogenetic analysis, identifying structural
similarity with proteins of known function and identifying
regions on the protein surface with a potential for high binding
affinity. Binding site prediction has been used in several
SBDD projects and has been incorporated into several docking
tools. We discuss different methods of ligand binding site
prediction, their strengths and weaknesses, and how they have
been used in SBDD.
[Back to top]
Scoring Functions for Protein-Ligand Docking
Ajay N. Jain
Virtual screening by molecular docking has become
established as a method for drug lead discovery and optimization.
All docking algorithms make use of a scoring function in combination
with a method of search. Two theoretical aspects of scoring
function performance dominate operational performance. The
first is the degree to which a scoring function has a global
extremum within the ligand pose landscape at the proper location.
The second is the degree to which the magnitude of
the function at the extremum is accurate. Presuming adequate
search strategies, a scoring function’s location performance
will dominate behavior with respect to docking accuracy: the
degree to which a predicted pose of a ligand matches experimental
observation. A scoring function’s magnitude performance
will dominate behavior with respect to screening utility:
enrichment of true ligands over non-ligands. Magnitude estimation
also controls pure scoring accuracy: the degree to which bona
fide ligands of a particular protein may be correctly
ranked. Approaches to the development of scoring functions
have varied widely, with a number of functions yielding similarly
high levels of performance relating to the location issue.
However, even among functions performing equally well on location,
widely varying performance is observed on the question of
magnitude. In many cases, performance is good enough to yield
high enrichments of true ligands versus non-ligands in screening
across a wide variety of protein types. Generally, performance
is not good enough to correctly rank among true ligands.
Strategies for improvement are discussed.
[Back to top]
eHiTS: An Innovative Approach to the Docking
and Scoring Function Problems
Zsolt Zsoldos, Darryl Reid, Aniko Simon, Bashir
S. Sadjad and A. Peter Johnson
Virtual Ligand Screening (VLS) has become an integral
part of the drug design process for many pharmaceutical companies.
In protein structure based VLS the aim is to find a ligand
that has a high binding affinity to the target receptor whose
3D structure is known. This review will describe the docking
tool eHiTS. eHiTS is an exhaustive and systematic docking
tool which contains many automated features that simplify
the drug design workflow. A description of the unique docking
algorithm and novel approach to scoring used within eHiTS
is presented. In addition a validation study is presented
that demonstrates the accuracy and wide applicability of eHiTS
in re-docking bound ligands into their receptors.
[Back to top]
Structure Selection for Protein Kinase Docking
and Virtual Screening: Homology Models or Crystal Structures?
William M. Rockey and Adrian H. Elcock
There is currently far more sequence information
than structural information available, and the ability to
use homology models for virtual screening applications is
desirable in many cases where structures have not yet been
solved. This review focuses on the application of protein
kinase homology models for virtual screening use. In addition
to reviewing previous cases in which kinase homology models
have been used in inhibitor design, we present new data –
useful for template selection in homology modeling applications
– indicating that the template structure with the highest
sequence or structural similarity with the target structure
may not always be the best choice. This new work explored
the simple hypothesis that better results might be obtained
for docking a ligand to a target receptor using a homology
model of the target created from a different kinase template
co-crystallized with the ligand, than from a crystal structure
of the actual kinase target that is unliganded or bound to
an unrelated ligand. This hypothesis was tested in docking
studies of stauro-sporine with eight different kinases: AutoDock
was used to dock staurosporine to homology models of each
kinase created from staurosporine-bound template structures,
and the results were compared with docking staurosporine to
crystal structures of the target kinase that were obtained
in complex with a non-staurosporine ligand or no ligand. It
was found that the homology models performed as well as or
better than the crystal structures, suggesting that using
a homology model created from a template crystallized with
a representative ligand may in some cases be a preferred approach,
especially in virtual screening experiments that focus on
enriching for members of a particular inhibitor class.
[Back to top]
Using Silico Methods Predicting Ligands
for Orphan GPCRs
Zhenran Jiang and Yanhong Zhou
The G-protein coupled receptor (GPCR) superfamily
is one of the most important drug target classes for the pharmaceutical
industry. The completion of the human genome project has revealed
that there are more than 300 potential GPCR targets of interest.
The identification of their natural ligands can gain significant
insights into regulatory mechanisms of cellular signaling
networks and provide unprecedented opportunities for drug
discovery. Much effort has been directed towards the GPCR
ligand discovery study by both academic institutions and pharmaceutical
industries. However, the endogenous ligands still remain unknown
for about 150 GPCRs in the human genome. It is necessary to
develop new strategies to predict candidate ligands for these
so-called orphan receptors. Computational techniques are playing
an increasingly important role in finding and validating novel
ligands for orphan GPCRs (oGPCRs). In this paper, we focus
on recent development in applying bioinformatics approaches
for the discovery of GPCR ligands. In addition, some of the
data resources for ligand identification are also provided.
[Back to top]
Latest Development in Drug Discovery on
G Protein-coupled Receptors
Kenneth Lundstrom
G protein-coupled receptors (GPCRs) represent
the family of proteins with the highest impact from social,
therapeutic and economic point of view. Today, more than 50%
of drug targets are based on GPCRs and the annual worldwide
sales exceeds $50 billion. GPCRs are involved in all major
disease areas such as cardiovascular, metabolic, neurodegenerative,
psychiatric, cancer and infectious diseases. The classical
drug discovery process has relied on screening compounds,
which interact favorably with the GPCR of interest followed
by further chemical engineering as a mean of improving efficacy
and selectivity. In this review, methods for sophisticated
chemical library screening procedures will be presented. Furthermore,
development of cell-based assays for functional coupling of
GPCRs to G proteins will be discussed. Finally, the possibility
of applying structure-based drug design will be summarized.
This includes the application of bioinformatics knowledge
and molecular modeling approaches in drug development programs.
The major efforts established through large networks of structural
genomics on GPCRs, where recombinantly expressed GPCRs are
subjected to purification and crystallization attempts with
the intention of obtaining high-resolution structures, are
presented as a prom-ising future approach for tailor-made
drug development.
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