Current Drug Targets (v.9, #12)

Computational Methods for Calculation of Ligand-Binding Affinity by Walter de Azevedo Jr., Raquel Dias (1031-1039).
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.

Molecular Docking Algorithms by Raquel Dias, Walter de Azevedo Jr. (1040-1047).
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 moleculardocking 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.

Protein Crystallography in Drug Discovery by Fernanda Canduri, Walter de Azevedo Jr. (1048-1053).
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.

In Silico and In Vitro: Identifying New Drugs by Ivani Pauli, Luis Macedo Timmers, Rafael Caceres, Milena Pereira Soares, Walter de Azevedo Jr. (1054-1061).
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.

Evaluation of Molecular Docking Using Polynomial Empirical Scoring Functions by Raquel Dias, Luis Fernando Macedo Timmers, Rafael Caceres, Walter de Azevedo Jr. (1062-1070).
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.

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.

Molecular Recognition Models: A Challenge to Overcome by Rafael Caceres, Ivani Pauli, Luis Fernando Macedo Timmers, Walter de Azevedo Jr. (1077-1083).
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 proteinprotein, 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.

Molecular Modeling as a Tool for Drug Discovery by Guy Barcellos, Ivani Pauli, Rafael Caceres, Luis Fernando Macedo Timmers, Raquel Dias, Walter de Azevedo Jr (1084-1091).
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 30and#x25; 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.

Drug-Binding Databases by Luis Fernando Macedo Timmers, Ivani Pauli, Rafael Caceres, Walter de Azevedo Jr. (1092-1099).
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.

Linear Interaction Energy (LIE) Method in Lead Discovery and Optimization by Hermes Luis de Amorim, Rafael Caceres, Paulo Netz (1100-1105).
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.