Current
Computer-Aided Drug Design
ISSN: 1573-4099
Upcoming Articles
Ligand-Based Approaches in Virtual Screening
Dominique Douguet*
[Abstract]
Pharmacophores of 5-HT4
Receptor Ligands: Experience of CERMN and Implications for
Drug Design
Ronan Bureau*, Thibault Varin, Alban Lepailleur,
Cyril Daveu, Stephane Lemaître, Jean-Charles Lancelot,
Aurelien Lesnard, Sabrina Butt-Gueulle, François Dauphin
and Sylvain Rault
[Abstract]
How to Measure the Similarity Between Protein Ligand-Binding
Sites?
Esther Kellenberger*, Claire Schalon and Didier Rognan
[Abstract]
Docking and High Throughput Docking: Successes
and the Challenge of Protein Flexibility
Claudio N. Cavasotto* and Narender Singh
[Abstract]
Collections of Compounds – How to Deal with
them?
Julie Dubois*,1,3, Stéphane
Bourg2, Christel Vrain3
and Luc Morin-Allory1
[Abstract]
Combining Ligand- and Structure-Based Methods in Drug
Design Projects
Olivier Sperandio, Maria A. Miteva and Bruno
O. Villoutreix*
[Abstract]
Calculating the Protonation States of Proteins and
Small Molecules: Implications to Ligand-Receptor Interactions
Rooplekha Mitra1,
Radhey Shyam1, Indranil Mitra1,2,
Maria A. Miteva1 and Emil
Alexov*,1
[Abstract]
ISIDA - Platform for Virtual Screening Based on Fragment
and Pharmacophoric Descriptors
Alexandre Varnek*,1,
Denis Fourches1, Dragos Horvath1,
Olga Klimchuk1, Cedric Gaudin1,2,
Philippe Vayer2, Vitaly Solov’ev1,3,
Frank Hoonakker1,5, Igor
V. Tetko1 and Gilles Marcou1
[Abstract]
Docking and Biomolecular Simulations on Computer Grids:
Status and Trends
Alexandru-Adrian Tantar1,
Sébastien Conilleau4,
Benjamin Parent2, Nouredine
Melab1, Lorraine Brillet3,
Sylvaine Roy3, El-Ghazali
Talbi1 and Dragos Horvath*,4
[Abstract]
Virtual Screening of Drugs: Score Functions, Docking,
and Drug Design
A. Breda1,2 ,L.A. Basso1,2
,D.S. Santos,*1,2 and W.F.
de Azevedo Jr. ,*1
[Abstract]
Comparative QSAR as a Cheminformatics Tool in the
Design of Dihydro-Pyranone Based HIV-1 Protease Inhibitors
B. Bhhatarai1
and R. Garg*,2,3
[Abstract]
Chirality Descriptors in QSAR
Gordon M. Crippen*
[Abstract]
Multimode Methods Applied on MIA Descriptors in QSAR
Matheus P. Freitas*,1,
Elaine F. F. da Cunha1, Teodorico
C. Ramalho1 and Mohammad
Goodarzi2,3
[Abstract]
Quantitative Sequence-Activity Model (QSAM): Applying
QSAR Strategy to Model and Predict Bioactivity and Function
of Peptides, Proteins and Nucleic acids
Peng Zhou1,2,
Feifei Tian2, Yuqian
Wu2, Zhiliang Li2
and Zhicai Shang*,1
[Abstract]
Abstracts
[Back to top]
Ligand-Based Approaches in Virtual Screening
Dominique Douguet*
Although there are many more receptor structures than there
were in the 1970s and 1980s, drug discovery remains dominated
by empirical screening and substrate-based drug design. Computer-aided
drug design methods have become value-adding disciplines that
now contribute to the early stage of the drug discovery process
[1, 2]. Computational methods encompass all aspects of drug
discovery from target assessment to lead optimization. The
computational strategy varies from case to case and can be
influenced by several situational variables: lead hunting
or lead optimization, requirement for a novel lead class,
type of biological assay, structural information available,
known classes of ligands, allocated chemistry resources…
Today, drug discovery is still a complex and approximate science.
Thus, incorporating knowledge-based approaches like ligand-based
screenings may bias the process towards success. This review
describes these strategies with practical applications and
presents future perspectives of ligand-based screening.
[Back to top]
Pharmacophores of 5-HT4
Receptor Ligands: Experience of CERMN and Implications for
Drug Design
Ronan Bureau*, Thibault Varin, Alban Lepailleur,
Cyril Daveu, Stephane Lemaître, Jean-Charles Lancelot,
Aurelien Lesnard, Sabrina Butt-Gueulle, François Dauphin
and Sylvain Rault
The definitions of pharmacophores for 5-HT4
receptor agonists and antagonists are described in this review.
These pharmacophores were keys in the design of new selective
ligands for this receptor, starting generally from 5-HT3
receptor ligands. Our laboratory has defined two series of
5-HT4 receptor ligands through
comparative analysis of pharmacophores associated with partial
agonists at 5-HT3 receptors
and antagonists at 5-HT4
receptors. For 5-HT4 receptor
agonists, a new 3D-QSAR analysis was carried out leading to
a pharmacophore which we compared to previous data. One of
the main challenges for the study of 5-HT4
receptors is to obtain a clear definition of the pharmacological
profile associated with the ligand derivatives. This is discussed
in terms of the conformational space associated with the receptor
as well as data from site-directed mutagenesis studies. Finally,
all these results allow a more precise description of the
pharmacophores and give interesting insights into the structural
modifications that appear to be of pivotal importance for
the activity of 5-HT4 receptor
ligands.
[Back to top]
How to Measure the Similarity Between Protein Ligand-Binding
Sites?
Esther Kellenberger*, Claire Schalon and Didier Rognan
Quantification of local similarity between protein 3D structures
is a promising tool in computer-aided drug design and prediction
of biological function. Over the last ten years, several computational
methods were proposed, mostly based on geometrical comparisons.
This review summarizes the recent literature and gives an
overview of available programs.
A particular interest is given to the underlying methodologies.
Our analysis points out strengths and weaknesses of the various
approaches. If all described methods work relatively well
when two binding sites obviously resemble each other, scoring
potential solutions remains a difficult issue, especially
if the similarity is low. The other challenging question is
the protein flexibility, which is indeed difficult to evaluate
from a static representation. Last, most of recently developed
techniques are fast and can be applied to large amounts of
data.
Examples were carefully chosen to illustrate the wide applicability
domain of the most popular methods: detection of common structural
motifs, identification of secondary targets for a drug-like
compound, comparison of binding sites across a functional
family, comparison of homology models, database screening.
[Back to top]
Docking and High Throughput Docking: Successes and
the Challenge of Protein Flexibility
Claudio N. Cavasotto* and Narender Singh
Protein structure-based approaches in lead optimization
and in silico screening of chemical libraries based on ligand
docking have increasingly become part of many drug discovery
projects, mainly due to the technical improvements in crystallography,
the support of modern software, and the ever increasing computational
power. The use of three-dimensional structural information
of therapeutic targets has long been recognized to initiate
and accelerate many drug design programs in the past, since
it offers the possibility of finding novel scaffolds, different
from the existing active compounds. In spite of its many successes,
structure-based virtual screening or high throughput docking
still has several limitations at the methodological level,
not the least of which is protein flexibility, ignored by
most of the docking programs, which treat the receptor as
a rigid entity. This may impact the accuracy of virtual screening
at the docking and at the scoring level. The authors will
first present the latest successful stories in high throughput
docking. After reviewing the concepts of protein dynamics
and binding, and its impact in ligand docking, the current
approaches to incorporate protein mobility in docking-based
virtual screening will be presented and discussed.
[Back to top]
Collections of Compounds – How to Deal
with them?
Julie Dubois*,1,3, Stéphane
Bourg2, Christel Vrain3
and Luc Morin-Allory1
Chemical libraries or databases are collections of compounds
which can be screened (virtually or experimentally) in order
to discover drug candidates. These libraries are very variable
in their content (description of structures, molecular descriptors,
literature links...) and their size (number of compounds).
Over the last decade, a large number of papers have been published
on the subject. In this review, we summarize these studies
by introducing different types of compound collections and
reviewing the main kinds of software used to manipulate them.
We present the descriptors which have a fundamental role in
the characterisation of the molecules, and describe how they
are used to define the molecular filters applied before screening,
in order to obtain both a representation of chemical spaces
and selections of subsets by diversity or similarity.
[Back to top]
Combining Ligand- and Structure-Based Methods
in Drug Design Projects
Olivier Sperandio, Maria A. Miteva and Bruno
O. Villoutreix*
In today’s drug discovery projects, the use of
virtual screening tools, either ligand-based or structure-based
techniques, is gaining momentum. Taken separately, these techniques
obviously present some genuine advantages on some specific
tasks, for example and for a given target, it is possible
to explore the local variation of the chemical space in terms
of structure activity relationship (SAR), or to sample the
pharmaco-topological profile of potential hit molecules in
a target structure driven manner (docking). Thus, while inherent
limits are associated with each of these screening techniques
that are not about to be easily solved, their combination
in a hybrid protocol can help to balance these limitations
and capitalize on their mutual strengths. Here we review some
recent studies integrating ligand- and structure-based screening
protocols, and show how this concept directly benefits the
quality of the hit molecules.
[Back to top]
Calculating the Protonation States of Proteins
and Small Molecules: Implications to Ligand-Receptor Interactions
Rooplekha Mitra1,
Radhey Shyam1, Indranil Mitra1,2,
Maria A. Miteva1 and Emil
Alexov*,1
Ionized groups carry net charge and thus play a major
role in the electrostatic interactions between the ligand
and receptor. However, their ionization states depend on such
factors as the pH of the water phase, the interactions with
other charges and with water molecules. Therefore, the ionization
states must be predicted prior to the application of in silico
screening or docking protocols. A typical virtual screening
protocol searches for new heat compounds by testing hundreds
of thousands or millions of small molecules against a particular
target receptor. Differences in the size of receptors and
the ligand, and the large number of small molecules to screen,
require different computational approaches in predicting pKa’s
of ionizable groups. On the receptor side, while the computational
protocol does not have to be fast, it must account for shape
of the receptor and the long range interactions of all ionizable
groups within. Conversely, while the calculations of the ionization
states of the ligand must be fast, they do not have to consider
many long range interactions because of the small size of
the ligand. These requirements resulted in the development
of different protocols for computing pKa’s of the receptor
and the ligand. The advantages and disadvantages of both are
outlined in this paper. In addition, the formation of the
receptor-ligand complex could dramatically change the electrostatic
environment of the ionizable groups and cause proton uptake/release.
Accounting for such phenomena can be critical for obtaining
correct docking solutions.
[Back to top]
ISIDA - Platform for Virtual Screening Based
on Fragment and Pharmacophoric Descriptors
Alexandre Varnek*,1,
Denis Fourches1, Dragos Horvath1,
Olga Klimchuk1, Cedric Gaudin1,2,
Philippe Vayer2, Vitaly Solov’ev1,3,
Frank Hoonakker1,5, Igor
V. Tetko1 and Gilles Marcou1
In this paper we illustrate the application of the ISIDA
(In SIlico design and Data Analysis)
software to perform virtual screening of large databases of
compounds and reactions and to assess some ADME/Tox properties.
ISIDA represents an ensemble of tools allowing users to store,
search and analyze the data, to perform similarity searches
in large databases of molecules and reactions, to build and
validate QSAR models, and to generate and screen virtual combinatorial
libraries. It uses its own descriptors (substructural molecular
fragments and fuzzy pharmacophore triplets). Workflow can
be easily organized by combining different ISIDA modules.
Several examples of ISIDA applications (similarity search
of potent benzodiazepine ligands with FPT, QSAR modeling of
aqueous solubility, aquatic toxicity, tissue-air partition
coefficients, anti-HIV activity, and screening of the “Chimiothèque
Nationale” Database), are discussed. Particular attention
is paid to mining reaction databases using Condensed Reaction
Graphs approach.
[Back to top]
Docking and Biomolecular Simulations on Computer
Grids: Status and Trends
Alexandru-Adrian Tantar1,
Sébastien Conilleau4,
Benjamin Parent2, Nouredine
Melab1, Lorraine Brillet3,
Sylvaine Roy3, El-Ghazali
Talbi1 and Dragos
Horvath*,4
This article outlines the recent developments
in the field of large-scale parallel computing applied to
molecular simulations, also including some original, preliminary
contributions of the authors. It is not meant to be an exhaustive
review paper, but rather an introductive material aimed at
narrowing the “cultural gap” between the developers
and users of molecular simulations (chemists, medicinal chemists
and biologists – typical workstation users) and the
informatics experts in massively parallel computing. The article
starts with a brief overview of the existing molecular simulation
techniques, in emphasizing the weaknesses of present approaches
and the need for more computer-intensive methods. Docking
procedures are the most discussed, given the high importance
of this application in computer-aided drug design. An introduction
to computer grids is logically pursued with the presentation
of some of the most promising large-scale parallel molecular
simulations already performed. Eventually, the author’s
own research program, Docking@Grid, is briefly discussed.
[Back to top]
Virtual Screening of Drugs: Score Functions,
Docking, and Drug Design
A. Breda1,2 ,L.A. Basso1,2
,D.S. Santos,*1,2 and W.F.
de Azevedo Jr. ,*1
The computational approach for new drug
design and/or identification, was initially proposed in mid
70’s. The virtual screening of chemical libraries against
a biological target has proven its reliability on structure-based
drug design, for instance, for many HIV virus protein inhibitors
and for the development of Cyclin-Dependent Kinase inhibitors.
Target-based virtual screening, allied to docking studies,
enables searches on larger data set of probable ligands, with
less costs than the traditional experimental screening. The
increasing availability of small molecules databases and its
free on-line distribution is now allowing not only pharmaceutical
industries, but independent research labs as well, to apply
this methodology on early stages of drug discovery. When the
protein target structure is available, and a chemical virtual
library is accessible, following questions need to be answered:
how the target and the ligand interact and how these interactions
may be evaluated? Several docking algorithms for the identification
of the molecular features responsible for binding specificity
are available. While such algorithms are very robust and accurate,
the scoring functions remain more questionable in the sense
of what parameters should be considered when defining protein-ligand
binding affinity when ranking candidates pointed-out by the
virtual screening to the next step on drug testing. Aside
conformational and chemical information, pharmacokinetics
properties should be considered as well when selecting potential
new drugs. Along with structural well-match, appropriate molecular
features that define desired kinetics characteristics should
be consistently addressed for usefulness of virtual screening
results. The present review is focused on these questions
and their implication for virtual screening.
[Back to top]
Comparative QSAR as a Cheminformatics Tool in
the Design of Dihydro-Pyranone Based HIV-1 Protease Inhibitors
B. Bhhatarai1
and R. Garg*,2,3
Protease is a key viral enzyme in the life cycle of retrovirus
HIV-1. Different drug cocktails of HIV-1 protease inhibitors
(HIV-PI) along with other drugs have improved the survival
rate for HIV-1 infected patients. However, the difficult treatment
schedule, adverse side effects & emergence of drug-resistant
viral mutations make these drugs less efficient and have led
to therapeutic failures. Quantitative Structure Activity Relationship
(QSAR) studies are successfully used in drug design and development.
In the present study, a cheminformatics analysis of simple
QSAR models laterally validated via comparative QSAR approach
is used to understand the inherent relationships between the
dihydro-pyranone based HIV-1 protease inhibitors (HIV-PI)
and their biological activity. This work focuses on the development
history of more than 500 dihydro-pyranones based HIV-PIs from
available literature to extract vital information. This cumulative
quantitative study of the various substituents that were studied
in this scaffold provides valuable mechanistic insight regarding
its substituents’ interaction with protease enzyme binding
pockets. We hope this study will contribute significantly
in understanding the binding patterns of drug-resistant mutants
with Tipranavir, a recently FDA approved dihydro-pyranone
based HIV-PI. In addition, it will be useful in the design
of new inhibitors.
[Back to top]
Chirality Descriptors in QSAR
Gordon M. Crippen*
Chirality is an important concept in medicinal chemistry,
since many biochemical reactions and processes are stereospecific,
including the recognition of some drugs by their receptors.
Quantitative structure-activity relations (QSAR) are mathematical
models that relate chemical structure and/or molecular properties
to biological activity, and incorporating stereospecificity
into QSAR can be essential for some studies. Starting with
the mathematical concept of oriented volume and with the notational
viewpoint of the Cahn-Ingold-Prelog rules, qualitative chirality
descriptors can be built into covalent connectivity graph
invariants several ways, some of which emphasize properties
other than atomic number and may involve multiple chiral centers.
Alternatively, there are several ways to define chirality
quantitatively. One approach is to measure the van der Waals
volume overlap when optimally superimposing a molecule and
its mirror image. Another way is to measure the degree of
distortion required to convert the molecule or subsets of
its atoms into a structure having a desired symmetry, such
as mirror symmetry. The third quantitative approach involves
translating and rotating the molecule to a standard position
associated with symmetry axes based on various atomic properties.
The advantages and disadvantages of all these methods are
discussed, and key equations are presented.
[Back to top]
Multimode Methods Applied on MIA Descriptors
in QSAR
Matheus P. Freitas*,1,
Elaine F. F. da Cunha1, Teodorico
C. Ramalho1 and Mohammad
Goodarzi2,3
Since the introduction of physicochemical descriptors
to derive useful QSAR (quantitative structure-activity relationship)
models, some regression methods have been applied to linearly
correlate dependent (bioactivities) and independent variables.
Multiple linear regression (MLR) has been widely used when
the number of samples (rows) exceed the amount of descriptors
(columns), whilst partial least squares (PLS) is the most
commonly applied regression method in 3D QSAR (e.g. CoMFA
and related methods), where a large number of descriptors
are generated. The recently implemented MIA-QSAR (Multivariate
Image Analysis applied to QSAR) method is a especial (not
only) case in which the descriptors (pixels) for each active
compound result in a three-way array after grouping samples
to give a data set. Such array may be properly treated by
using N-way methods, such as multilinear PLS (N-PLS) and parallel
factor analysis (PARAFAC). However, these methods have not
been appropriately explored in QSAR studies, despite their
supposed advantages over well established methods. Thus, this
review formally details the MIA-QSAR approach prior to presenting
two promising multimode methods to be applied on MIA descriptors,
namely N-PLS and PARAFAC. Also, the suitability of such methods
is discussed in terms of application to a case study (a series
of anti-HIV compounds) and comparison to traditional (bilinear)
PLS and docking studies.
[Back to top]
Quantitative Sequence-Activity Model (QSAM):
Applying QSAR Strategy to Model and Predict Bioactivity and
Function of Peptides, Proteins and Nucleic acids
Peng Zhou1,2,
Feifei Tian2, Yuqian
Wu2, Zhiliang Li2
and Zhicai Shang*,1
Traditional quantitative structure-activity relationship
(QSAR) is a term describing a variety of approaches that are
of substantial interest for chemistry. Quantitative sequence-activity
model (QSAM), applying QSAR strategy to explore sequence-activity/function
relationship for biosystems, is greatly meaningful but meanwhile
extremely difficult. For biomolecules, high molecular weight,
diverse structural morphology and intricate interaction network
all bring in traditional QSAR methodologies unprecedented
challenges. This article comprehensively reviewed developing
process, current state and future perspective of QSAM, concerning
its applications into fields of pharmacy, food science, immunology
and molecular biology. Besides, discipline-crossing and amalgamation
of QSAM with QSAR, bioinformatics and computational biology
were also discussed.
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