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Chemoinformatics: Directions Toward Combating Neglected Diseases, 2012, 49-68 49 Antimalarial Agents: Homology Modeling Studies Tanos C.C. França 1,* and Magdalena N. Rennó 2 Teodorico C. Ramalho, Matheus P. Freitas and Elaine F. F. da Cunha (Eds) All rights reserved - © 2012 <strong>Bentham</strong> <strong>Science</strong> Publishers CHAPTER 3 1 Chemical Engineering Department, Military Institute of Engineering, Pça General Tibúrcio, 80 - Urca, 22290-270, Rio de Janeiro/RJ, Brazil and 2 Pharmacy Course, Federal University of Rio de Janeiro, Campus Macaé, Rua Aloísio da Silva Gomes, 50, Granja dos Cavaleiros, 27930-560, Macaé/RJ, Brazil Abstract: Homology modeling may be a very useful tool to obtain theoretical 3D structures of molecular targets when their experimental structures are still unknown. If 3D structures of the appropriate templates are available, it is possible to build a very consistent model using one of several softwares available today for this purpose and, further, use it to analyze the overall target structure, its active site residues and possible interactions with potential ligands. These models can also be used for further MD simulations, docking and QSAR studies in order to afford additional information toward the rational design of inhibitors to the molecular target in focus. Literature has reported some interesting studies using this approach on neglected diseases, especially malaria. Those studies have afforded very useful models for the design of more selective and powerful antimalarial agents. Keywords: Homology modeling, molecular dynamics, antimalarial agents. 1. INTRODUCTION 1.1. Building 3D Proteins Models by Homology Modeling According to Higgins [1], the genome sequencing facilities available today have doubled the number of protein sequences discovered each year, increasing quickly the gap between the amount of structural information in the protein databanks and the number of sequences waiting for elucidation of their 3D structures. This happens because experimental methods for 3D elucidation are much more time consuming, restricted and usually not automatic when compared to the genomic facilities. The development of practical methods for prediction of protein structures from their sequences is, therefore, very important in the biologic field [1]. Several different approaches have been applied to predict protein structures from their sequences, with varied degrees of success. Most of these methods are based on known experimental structures, used as templates, and are strongly dependent on the Identity Degree (ID), or the percentage of identical amino acid residues present among the target sequence and their templates [1,2]. When there is no template available with a significant ID to the target sequence, one can use ab initio methods for the prediction of 3D structures. These methods include theretical methodologies to calculate the coordinates of a protein from the first principles, i.e., with no reference to known structures. They have been of limited success and have produced more theory than really useful methodology [1]. If the ID between a target protein, whose structure is intended to be predicted, and its homologues are too low (< 25 %) for the identification of an unequivocal template, the best way to face the problem is the principle of fold recognition in order to find a protein with a more suitable folding for the modeling of the target protein [1- 6]. This method evaluates the compatibility sequence-structure by means of an empirical potential, like Boltzman, derived from a table of contacts of the residues observed in proteins with known structures [1, 7]. Despite not having the same precision of other homology modeling techniques, the fold recognition could eventually become a powerful tool for the prediction of protein structures [8] considering the fact that only 10 different foldings (the supercoils) contribute to 50% of all structural similarities known among the *Address correspondence to Tanos C.C. França: Chemical Engineering Department, Military Institute of Engineering, Pça General Tibúrcio, 80 - Urca, 22290-270, Rio de Janeiro/RJ, Brazil; Tel: +00552125467195; E-mail: tanos@ime.eb.br
50 Chemoinformatics: Directions Toward Combating Neglected Diseases França and Rennó protein super families [9]. So, instead of trying to find the correct structure for a protein among numerous possibilities of conformations for polypeptidic chains, a more practical approach would be to query first if the correct conformation has not been previously observed in another protein. The homology modeling, or comparative modeling, tries to predict the protein structure related to another known structure (the template) following the idea that similar sequences imply in similar structures. This method has been quite successful, but presents some constraints like dependence on the alignment quality among the target sequence and the templates and the need for a good similarity with templates (above 35 % identity) [1]. The protocol for model building by homology modeling today involves template identification, alignment of the sequences, generation of the model coordinates, optimization and validation of the model [1]. These steps are quickly discussed below. 1.1.1. Template Identification, Alignment of the Sequences and Generation of the Model Coordinates The homology modeling process starts with the identification of at least one protein with known 3D structure as template to the target protein [7]. Here, the protein family of the target protein can or can not be known. If this family is known, the search for the best template should be done in a unique protein family and the chances of major accuracy in the final model are increased [2]. If the goal is to model a Serine Hydroxymethyltransferase (SHMT), for example, the logical way would be to search for templates inside the group of SHMTs with 3D structures available in the Protein Data Bank (PDB) [10, 11] or another protein data bank. When the target protein family is not known, it is necessary to look for structures in the data banks of aminoacid sequences that could be compared to the target using the algorithms like BLAST [2, 12] and FastA [2, 13]. Aminoacid sequence data banks, like GenBank, [14], Protein Identification Resource (PIR) [15] and SWISS-PROT [16] can also be used [2]. The chosen templates must be aligned to the target sequence in order to establish the spatial correspondence between the target amino acid residues and those of the templates [2, 7]. This task can be performed by softwares like BLAST [12], FastA, [13], Multalign [17] and SWISSMODEL [18]. For sequences with more than 80 residues, if the sequential identity between the target and one of the templates is 25 %, the probability of their 3D structures being similar is high and, consequently, the 3D structure of the template could be used for modeling [2, 19]. In this case, most of the incertitude depends on the loop regions. Also, if the sequential identity is > 60%, the accuracy level of the resulting model is similar to the level of an experimental structure [2, 19]. When there are several potential templates, it is possible to choose between multiple and single alignments. The multiple alignments offer more options for modeling poorly aligned regions in the protein and afford a model reflecting the mean values among all templates. However, this necessarily implies in a deviation of the main protein chain from the more sequentially similar template. In the single alignment, on the other hand, despite the possibility of some regions being poorly modeled, the final model is more similar to the template with higher sequential identity. Once the templates and the type of alignment to be used are defined, the next step is to generate the model coordinates. For this, it is necessary to perform, in sequence, the modeling of the structurally conserved regions, the loops and the side chains. The alignment is of fundamental importance for the spatial location of most atoms in the target protein. In multiple alignments, each template contributes to the modeling of a specific segment in the target [7] according to the alignment obtained. The secondary structure (-helix and -sheets) model elements are usually defined according to the structure of the most similar template in the multiple alignment, similar to the single alignment. The crude model of the protein is obtained changing the different residues in the template for their corresponding residues in the target protein [2].
Page 2 and 3: Chemoinformatics: Directions Toward
Page 4 and 5: CONTENTS Foreword i Preface ii List
Page 6 and 7: ii PREFACE Low-income populations i
Page 10 and 11: New Approaches to the Development C
Page 12 and 13: 34 Chemoinformatics: Directions Tow
Page 16 and 17: Antimalarial Agents Chemoinformatic
Page 18 and 19: 70 Chemoinformatics: Directions Tow
Page 22 and 23: Antileishmaniasis Agents Chemoinfor
Page 24 and 25: 146 Chemoinformatics: Directions To
Page 30 and 31: Molecular Modeling of the Toxoplasm

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