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Current
Drug Targets
ISSN: 1389-4501
Current Drug Targets
Volume 9, Number 12, December 2008
Contents
Protein-Drug Interactions
Guest Editor: Walter Filgueira de Azevedo Jr.
Editorial Pp. 1030
[PMID:
19128211 PubMed - indexed for MEDLINE]
Computational Methods for Calculation of Ligand-Binding
Affinity Pp. 1031-1039
W.F. de Azevedo Jr. and R. Dias
[Abstract] [Purchase
Article] [PMID:
19128212 PubMed - indexed for MEDLINE]
Molecular Docking Algorithms Pp.
1040-1047
R. Dias and W.F. de Azevedo Jr.
[Abstract] [Purchase
Article] [PMID:
19128213 PubMed - indexed for MEDLINE]
Protein Crystallography in Drug Discovery
Pp. 1048-1053
F. Canduri and W.F. de Azevedo
Jr.
[Abstract] [Purchase
Article] [PMID:
19128214 PubMed - indexed for MEDLINE]
In Silico and In Vitro:
Identifying New Drugs Pp. 1054-1061
I. Pauli, L.F.S.M. Timmers, R.A. Caceres, M.B.P. Soares and W.F. de Azevedo Jr.
[Abstract] [Purchase
Article] [PMID:
19128215 PubMed - indexed for MEDLINE]
Evaluation of Molecular Docking Using
Polynomial Empirical Scoring Functions Pp.
1062-1070
R. Dias, L.F.S.M. Timmers, R.A. Caceres
and W.F. de Azevedo Jr.
[Abstract] [Purchase
Article] [PMID:
19128216 PubMed - indexed for MEDLINE]
Experimental Approaches to Evaluate the
Thermodynamics of Protein-Drug Interactions Pp.
1071-1076
W.F. de Azevedo Jr. and R. Dias
[Abstract] [Purchase
Article] [PMID:
19128217 PubMed - indexed for MEDLINE]
Molecular Recognition Models: A Challenge
to Overcome Pp. 1077-1083
R.A. Caceres, I. Pauli, L.F.S.M. Timmers
and W.F. de Azevedo Jr.
[Abstract] [Purchase
Article] [PMID:
19128218 PubMed - indexed for MEDLINE]
Molecular Modeling as a Tool for Drug
Discovery Pp. 1084-1091
G.B. Barcellos, I. Pauli, R.A. Caceres, L.F.S.M. Timmers,
R. Dias and W.F. de Azevedo Jr.
[Abstract] [Purchase
Article] [PMID:
19128219 PubMed - indexed for MEDLINE]
Drug-Binding Databases Pp.
1092-1099
L.F.S.M. Timmers, I. Pauli, R.A. Caceres
and W.F. de Azevedo Jr.
[Abstract] [Purchase
Article] [PMID:
19128220 PubMed - indexed for MEDLINE]
Linear Interaction Energy (LIE) Method
in Lead Discovery and Optimization Pp. 1100-1105
H.L.N. de Amorim, R.A. Caceres and
P.A. Netz
[Abstract] [Purchase
Article] [PMID:
19128221 PubMed - indexed for MEDLINE]
Abstracts
[Back to top] [PMID:
19128211 PubMed - indexed for MEDLINE]
Editorial:
The identification of novel inhibitors is a major
challenge in drug research and development. Structure-based
design is a fundamental approach in this endeavor and is now
an integral part of drug development. It has been shown for
a large number of drug targets that the detailed three-dimensional
structure of the protein, as obtained from X-ray crystallography,
can be used to design or modify the structure of a small molecule
docked to the target structure. Most of the drugs act via
non-covalent interactions, such as, intermolecular hydrogen
bonds, electrostatic interactions (charge-charge, charge-dipole,
charge-induced dipole), deformation, hydrophobic effect, and
others. Therefore, methods to evaluate these interactions
are necessary. Computational approach to this problem generated
methods of virtual screening and molecular docking simulations.
Molecular docking is a computer simulation process where the
position for a ligand is estimated in a predicted or pre-defined
binding site in a drug target. The ligand may be a small molecule,
a protein, and a nucleic acid (DNA or RNA), and the receptor
may be a protein or a nucleic acid molecule. Molecular docking
simulation may be used for virtual screening of drugs, and
can be carried out with current computational power. Although
docking simulation and the related information technology
have advanced in recent years, it is difficult to identify
a suitable parameter set of docking simulations for accuracy
of simulation.
One approach to this problem is to employ empirical scoring
functions to identify the best docked structure. Most of the
algorithms and computational techniques used for molecular
docking simulation make use of an internal function to estimate
the interaction between ligand and receptor. The majority
of the empirical scoring functions in current use are based
on the model where binding affinity can be decomposed in terms
that reflect the various contributions to the binding. In
spite of many problems in the understanding of the structural
features important for binding affinity, most of the experimental
available data indicates that application of additive functions
for protein-ligand interactions is a good approach for the
development of empirical scoring functions.
The present volume brings reviews focused on protein-drug
interaction, including reviews on modern computational approaches
to molecular docking and evaluation of ligand binding affinity,
and also descriptions of experimental techniques such as protein
crystallography. Proteins accomplish their roles in the cell
by interacting with other proteins and/or ligands. Protein
structure can be determined experimentally by X-ray crystallography,
NMR, spectroscopy, cryo-electron microscopy or alternatively,
estimated by computational molecular modeling. However, X-ray
crystallography continues to be the standard method for high
resolution protein structure determination and accounts for
the vast majority of experimentally determined protein-drug
structures. Another biophysical technique reviewed in this
volume is isothermal titration calorimetry (ITC). When a drug
binds to a protein, heat is either absorbed (endothermic reaction)
or released (exothermic reaction). For a closed system, in
this case protein-drug system, at constant pressure the heat
absorbed is equal to the enthalpy change. ITC is a thermodynamic
technique that directly measures the heat released or absorbed
during the protein-drug complexation. In a typical ITC experiment
information about enthalpy, entropy, and dissociation constant
can be obtained.
The binding of a drug to a receptor is generally followed
by conformational changes of the receptor. In the induce-fit
model, the interaction between receptor and drug causes them
both to be submitted to some conformation change. A key question
yet to be answered is whether the drug induces the conformational
change (induced-fit), or rather choose and stabilizes a conformation
among an ensemble of pre-existing equilibrium of ground and
excited states of the protein (selected-fit). Molecular recognition
is at the foundation of the current revolution in molecular
biology. In recent years, advances in biological, chemical,
physical and computational methodologies have provided the
tools not only to identify and characterize interacting molecules,
but also to understand the general rules governing the phenomenon
of molecular recognition. Equally important, progress in our
understanding of the structural basis of interaction and our
ability to design new recognition molecules complement the
field of affinity technology. Many studies attempted to understand
the remarkable specificity and efficiency of the molecular
recognition process. These questions related to the molecular
recognition process will also be reviewed in this volume.
One of the most interesting and important challenges in the
so-called "Post- genomic Era" is the understanding
of protein networks. Until the 1980s, most of our knowledge
about drugs, drug mechanisms and drug receptors could fit
in a few encyclopedic books and a couple dozen schematic figures.
However, with the recent explosion in biological and chemical
knowledge, this is no longer the case. The development of
drug-binding databases plays an important role in the understanding
of protein-drug interaction and is reviewed in this volume.
Walter Filgueira de Azevedo Jr.
Faculdade de Biociências-PUCRS
Av. Ipiranga, 6681, CEP 90619-900
Porto Alegre, Rio Grande do Sul
Brazil
Tel: +55 51 33203500
E-mail: walter@azevedolab.net
[Back to top] [Purchase
Article] [PMID:
19128212 PubMed - indexed for MEDLINE]
Computational Methods for Calculation of Ligand-Binding Affinity
W.F. de Azevedo Jr. and R. Dias
Precise computational methods to determine ligand-binding
affinity are needed to accelerate the discovery of new drugs.
Assessing protein-ligand interaction is of great importance
for virtual screening initiatives. The affinity may be computational
evaluated using scoring functions involving terms for intermolecular
hydrogen bonds, contact surface, hydrophobic contacts, electrostatic
interactions and others. Empirical scoring functions have
been developed to evaluate ligand-binding affinity very rapidly.
In addition to predict affinity, these scoring functions have
been employed to identify the best results obtained from docking
simulations. This review describes several computational methods,
employed to estimate ligand-binding affinity and discuss their
development and main applications.
[Back to top] [Purchase
Article] [PMID:
19128213 PubMed - indexed for MEDLINE]
Molecular Docking Algorithms
R. Dias and W.F. de Azevedo Jr.
By means of virtual screening of small molecules databases
it is possible to identify new potential inhibitors against
a target of interest. Molecular docking is a computer simulation
procedure to predict the conformation of a receptor-ligand
complex. Each docking program makes use of one or more specific
search algorithms, which are the methods used to predict the
possible conformations of a binary complex. In the present
review we describe several molecular-docking search algorithms,
and the programs which apply such methodologies. We also discuss
how virtual screening can be optimized, describing methods
that may increase accuracy of the simulation process, with
relatively fast docking algorithms.
[Back to top] [Purchase
Article] [PMID:
19128214 PubMed - indexed for MEDLINE]
Protein Crystallography in Drug Discovery
F. Canduri and W.F. de Azevedo
Jr.
Protein crystallography is the main technique used to
obtain three-dimensional information for binary complexes
involving protein and drugs. Once a protein target has its
three-dimensional structure elucidated, the next natural step
is the solving of the structure complexed either with its
natural substrate, or any ligand or even an inhibitor. Such
information is of pivotal importance to understand the structural
basis for inhibition of an enzyme. The relevant features,
for application of protein crystallography to drug discovery,
are discussed in this review.
[Back to top] [Purchase
Article] [PMID:
19128215 PubMed - indexed for MEDLINE]
In Silico and In Vitro: Identifying New
Drugs
I. Pauli, L.F.S.M. Timmers, R.A. Caceres, M.B.P. Soares and W.F. de Azevedo Jr.
Drug development is a high cost and laborious process,
requiring a number of tests until a drug is made available
in the market. Therefore, the use of methods to screen large
number of molecules with less cost is crucial for faster identification
of hits and leads. One strategy to identify drug-like molecules
is the search for molecules able to interfere with a protein
function, since protein interactions control most biological
processes. Ideally the use of in silico screenings
would make drug development faster and less expensive. Currently,
however, the confirmation of biological activity is still
needed. Due to the complexity of the task of drug discovery,
an integrated and multi-disciplinary approach is ultimately
required. Here we discuss examples of drugs developed through
a combination of in silico and in vitro strategies.
The potential use of these methodologies for the identification
of active compounds as well as for early toxicity and bioavailability
is also reviewed.
[Back to top] [Purchase
Article] [PMID:
19128216 PubMed - indexed for MEDLINE]
Evaluation of Molecular Docking Using Polynomial Empirical
Scoring Functions
R. Dias, L.F.S.M. Timmers, R.A. Caceres
and W.F. de Azevedo Jr.
Molecular docking simulations are of pivotal importance
for analysis of protein-ligand interactions and also an essential
resource for virtual-screening initiatives. In molecular docking
simulations several possible docked structures are generated,
which create an ensemble of structures representing binary
complexes. Therefore, it is crucial to find the best solution
for the simulation. One approach to this problem is to employ
empirical scoring function to identify the best docked structure.
It is expected that scoring functions show a descriptive funnel-shaped
energy surface without many false minima to impair the efficiency
of conformational sampling. We employed this methodology against
a test set with 300 docked structures. Docking simulations
of these ligands against enzyme binding pocket indicated a
funnel-shaped behavior of the complexation for this system.
This review compares a set of recently proposed polynomial
empirical scoring functions, implemented in a program called
POLSCORE, with two popular scoring function programs (XSCORE
and DrugScore). Overall comparison indicated that POLSCORE
works better to predict the correct docked position, for the
ensemble of docked structures analyzed in the present work.
[Back to top] [Purchase
Article] [PMID:
19128217 PubMed - indexed for MEDLINE]
Experimental Approaches to Evaluate the Thermodynamics of
Protein-Drug Interactions
W.F. de Azevedo Jr. and R. Dias
Precise experimental methods to determine ligand-binding
affinity are needed to accelerate the discovery of new drugs.
Assessing protein-ligand interaction is of great importance
for drug development. One of the techniques that may be used
to evaluate ligand-binding affinitty is isothermal titration
calorimetry (ITC). This experimental methodology may be used
to measure the heat of binding of a ligand to a protein. Furthermore,
the development of new empirical scoring functions to assess
evaluation protein-ligand interaction lack abundance of experimental
information to be used to generate reliable scores. ITC technique
may be used to fill this gap. Here we describe the application
of this technique to ligand-binding affinity determination,
and discuss the synergetic relationship between ITC data and
the development of a new generation of empirical scoring functions.
[Back to top] [Purchase
Article] [PMID:
19128218 PubMed - indexed for MEDLINE]
Molecular Recognition Models: A Challenge to Overcome
R.A. Caceres, I. Pauli, L.F.S.M. Timmers
and W.F. de Azevedo Jr.
Molecular recognition process describes the interaction
involving two molecules. In the case of biomolecules, these
pairs of molecules could be protein-protein, protein-ligand
or protein-nucleic acid. The first model to capture the essential
features, behind the molecular recognition problem, was the
lock-and-key paradigm. The overall analysis protein-protein,
protein-nucleic acid and protein-ligand interaction based
on the three-dimensional structures and physicochemical parameters,
such as binding affinity, opened the possibility to provide
further insights in this basic phenomenon. The main ideas
behind the molecular recognition are discussed in the present
review.
[Back to top] [Purchase
Article] [PMID:
19128219 PubMed - indexed for MEDLINE]
Molecular Modeling as a Tool for Drug Discovery
G.B. Barcellos, I. Pauli, R.A. Caceres, L.F.S.M. Timmers,
R. Dias and W.F. de Azevedo Jr.
With the progression of structural genomics projects,
comparative modeling remains an increasingly important method
of choice to obtain 3D structure of proteins. It helps to
bridge the gap between the available sequence and structure
information by providing reliable and accurate protein models.
Comparative modeling based on more than 30% sequence identity
is now approaching its natural template-based limits and further
improvements require the development of effective refinement
techniques capable of driving models toward native structure.
For difficult targets, for which the most significant progress
in recent years has been observed, optimal template selection
and alignment accuracy are still the major problems. The past
year has seen a maturation of molecular modeling, with an
increasing number of comparative studies between established
methods becoming possible, together with an explosion of new
works especially in the areas of combinatorial chemistry and
molecular diversity. To achieve this, knowledge about three-dimensional
protein structures is crucial for the understanding of their
functional mechanisms, and for a rational drug design. This
review described recent progress in molecular modeling methodology.
[Back to top]
[Purchase
Article] [PMID:
19128220 PubMed - indexed for MEDLINE]
Drug-Binding Databases
L.F.S.M. Timmers, I. Pauli, R.A. Caceres
and W.F. de Azevedo Jr.
Recent developments in computer power and chemoinformatics
methodology make possible that a huge amount of data become
available through internet. These databases are devoted to
a wide spectrum of scientific fields. Here we are concerned
with databases related to protein-drug interactions. More
specifically, databases where potential new molecules could
be accessed to be used in virtual screening initiatives. In
the past decade several databases have been developed where
molecules to be used in the virtual screening could be easily
identified, downloaded and even purchased. This review describes
and summarizes the recent advances in the development of these
databases, and also the main applications related to virtual
screening projects.
[Back to top]
[Purchase
Article] [PMID:
19128221 PubMed - indexed for MEDLINE]
Linear Interaction Energy (LIE) Method in Lead Discovery and
Optimization
H.L.N. de Amorim, R.A. Caceres and
P.A. Netz
Currently, in order to accelerate the process of drug
development and also reduce costs, many of the experimental
assays related to lead discovery and lead optimization processes
are being replaced by computational, in silico, methods.
In this context, the LIE (linear interaction energy) method
has been used to calculate binding free energies for widely
different compounds by averaging interaction energies obtained
from molecular dynamics (MD) or Monte Carlo (MC) simulations.
In particular, the combination of docking and affinity predictions
with the LIE method can thus save valuable resources in lead
discovery and optimization projects. This review presents
a description of LIE methodology and some recent studies that
illustrate the importance and utility of the method in the
field of pharmaceutical research.
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