About Author:
Jadhav Ramulu 1, P. Goverdhan2
1 Vaagdevi College of Pharmacy, Hanamkonda-Warangal (Affiliated to Kakatiya University), A.P-506002, INDIA
2 Head of the department, pharmacology, Vaagdevi College of Pharmacy, Hanamkonda-Warangal, A.P-506002, INDIA
1 Vaagdevi College of Pharmacy, Hanamkonda-Warangal (Affiliated to Kakatiya University), A.P-506002, INDIA
2 Head of the department, pharmacology, Vaagdevi College of Pharmacy, Hanamkonda-Warangal, A.P-506002, INDIA
Abstract
Computer aided drug design(CADD) is an emerging tool for research and drug development process as it reduce the time taken for the process of drug development and expense. Several new technologies have been developed and applied in drug R & D to shorten the research cycle and to reduce the expenses. In computer aided drug design process so many computational tools are used such as over viewing tools, homology modeling, and homology modeling programs, molecular dynamics, molecular docking and QSAR descriptors. This article provides a brief idea on computer aided drug design process and list of software used.
Computer aided drug design(CADD) is an emerging tool for research and drug development process as it reduce the time taken for the process of drug development and expense. Several new technologies have been developed and applied in drug R & D to shorten the research cycle and to reduce the expenses. In computer aided drug design process so many computational tools are used such as over viewing tools, homology modeling, and homology modeling programs, molecular dynamics, molecular docking and QSAR descriptors. This article provides a brief idea on computer aided drug design process and list of software used.
Introduction
The process of drug discovery is very complex and requires an interdisciplinary effort to design effective and commercially feasible drugs. The objective of drug design is to find a chemical compound that can fit to a specific cavity on a protein target both geometrically and chemically (1). It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues (2).The development of any potential drug begins with years of scientific study to determine the biochemistry behind a disease, for which pharmaceutical intervention is possible. The result is the determination of specific receptors (targets). In the post genomic era, computer-aided drug design (CADD) has considerably extended its range of applications, spanning almost all stages in the drug discovery pipeline, from target identification to lead discovery, from lead optimization to preclinical or clinical trials (3). The rapidly expanding literature on the computational study of drug structure and activity is important both for the insights it provides into our existing drugs and for the ideas it contributes to new drug discovery (4). To bring a new drug to the market is very costly, with the current price tag approximating US$800 million, according to data reported in a recent study. Therefore, it is not surprising that pharmaceutical companies are seeking ways to optimize costs associated with R&D, with the goal of increasing profit margins. One method that was quickly adopted by industry was the use of combinatorial chemistry and HTS. In HTS, large libraries of compounds are screened against drug targets to identify lead compounds that can modulate a particular outcome. However, setting up a combinatorial chemistry program and HTS is costly and not able to address the specific needs of many biological (drug target) systems.
The process of drug discovery is very complex and requires an interdisciplinary effort to design effective and commercially feasible drugs. The objective of drug design is to find a chemical compound that can fit to a specific cavity on a protein target both geometrically and chemically (1). It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues (2).The development of any potential drug begins with years of scientific study to determine the biochemistry behind a disease, for which pharmaceutical intervention is possible. The result is the determination of specific receptors (targets). In the post genomic era, computer-aided drug design (CADD) has considerably extended its range of applications, spanning almost all stages in the drug discovery pipeline, from target identification to lead discovery, from lead optimization to preclinical or clinical trials (3). The rapidly expanding literature on the computational study of drug structure and activity is important both for the insights it provides into our existing drugs and for the ideas it contributes to new drug discovery (4). To bring a new drug to the market is very costly, with the current price tag approximating US$800 million, according to data reported in a recent study. Therefore, it is not surprising that pharmaceutical companies are seeking ways to optimize costs associated with R&D, with the goal of increasing profit margins. One method that was quickly adopted by industry was the use of combinatorial chemistry and HTS. In HTS, large libraries of compounds are screened against drug targets to identify lead compounds that can modulate a particular outcome. However, setting up a combinatorial chemistry program and HTS is costly and not able to address the specific needs of many biological (drug target) systems.
Additionally,
compounds identified in such screenings are not always amenable to
further medicinal chemistry development, with poor ADME (absorption,
distribution metabolism and elimination) properties. Although these
methods have increased the rate at which lead compounds can be
identified, there has not been a commensurate increase in the rate of
introduction of new chemical entities (NCE) into the world drug market.
As an attractive alternative, in silico methods show promise in
identifying new lead compounds faster and at a fraction of the cost of
combinatorial approaches and HTS. The addition of computer aided drug
design technologies to the R&D approaches of a company, could lead
to a reduction in the cost of drug design and development by up to 50%
(5).
COMPUTER AIDED DRUG DESIGN PROCESS
Computer aided drug design is the process which facilitate computational methods and resources that are used in design and discovery of new therapeutic solutions .Several new technologies have been developed and applied in drug R & D to shorten the research cycle and to reduce the expenses. Computer-aided drug design (CADD) is one of such evolutionary technologies (6). CADD technologies including molecular modeling and simulation have become promising in drug discovery. Recently, CADD has even been used in designing highly selective ligands for a certain target that shares very similar structures with many proteins, which is difficult to be done by other methods. One such example is the rational design of selective inhibitors of p90 ribosomal protein S6 kinase (7). In the postgenomic era, owing to the dramatic increase of small molecule and biomacromolecule information, CADD tools have been applied in almost every stage of drug R & D, greatly changing the strategy and pipeline for drug discovery (6). CADD, from its traditional application of lead discovery and optimization, has extended toward two directions: upstream for target identification and validation, and downstream for preclinical study (ADMET prediction). Target identification and validation is the first key stage in the drug discovery pipeline. However, identification and validation of druggable targets from among thousands of candidate macromolecules is still a challenging task (8). Numerous technologies for addressing the targets have been developed recently. Genomic and proteomic approaches are the major tools for target identification. For example, a proteomic approach for identification of binding proteins for a given small molecule involves comparison of the protein expression profiles for a given cell or tissue in the presence or absence of the given molecule. This method has not been proved very successful in target discovery because it is laborious and time-consuming (9). Therefore, complementary to the experimental methods, a series of computational (insilico) tools have also been developed for target identification. They can be cataloged into sequence-based approach and structure-based approaches. Hence computational approaches to drug design fall into two general categories: those that do not assume information on the structure of the target macromolecule, and the structure-based approaches that do make use of such information (4). Structure based approaches are not yet applicable because the structure of the target macromolecule is unknown; in these cases, quantitative structure-activity relationship (QSAR) techniques provide the best approach to rational drug design. Traditional (two-dimensional) QSAR methods attempt to correlate biological activity with local features of atoms, whole molecular properties (e.g. charge) and substituent effects (e.g. fragment hydrophobicity indices). New developments in traditional QSAR continue to appear in the literature (e.g. the OASIS program) (10). Most interest in this field, however, now focuses on three-dimensional QSAR. Recent examples of molecules to which this approach has been applied include HIV-1 protease (11) and the cholecystokinin-A receptor (12).
Computer aided drug design is the process which facilitate computational methods and resources that are used in design and discovery of new therapeutic solutions .Several new technologies have been developed and applied in drug R & D to shorten the research cycle and to reduce the expenses. Computer-aided drug design (CADD) is one of such evolutionary technologies (6). CADD technologies including molecular modeling and simulation have become promising in drug discovery. Recently, CADD has even been used in designing highly selective ligands for a certain target that shares very similar structures with many proteins, which is difficult to be done by other methods. One such example is the rational design of selective inhibitors of p90 ribosomal protein S6 kinase (7). In the postgenomic era, owing to the dramatic increase of small molecule and biomacromolecule information, CADD tools have been applied in almost every stage of drug R & D, greatly changing the strategy and pipeline for drug discovery (6). CADD, from its traditional application of lead discovery and optimization, has extended toward two directions: upstream for target identification and validation, and downstream for preclinical study (ADMET prediction). Target identification and validation is the first key stage in the drug discovery pipeline. However, identification and validation of druggable targets from among thousands of candidate macromolecules is still a challenging task (8). Numerous technologies for addressing the targets have been developed recently. Genomic and proteomic approaches are the major tools for target identification. For example, a proteomic approach for identification of binding proteins for a given small molecule involves comparison of the protein expression profiles for a given cell or tissue in the presence or absence of the given molecule. This method has not been proved very successful in target discovery because it is laborious and time-consuming (9). Therefore, complementary to the experimental methods, a series of computational (insilico) tools have also been developed for target identification. They can be cataloged into sequence-based approach and structure-based approaches. Hence computational approaches to drug design fall into two general categories: those that do not assume information on the structure of the target macromolecule, and the structure-based approaches that do make use of such information (4). Structure based approaches are not yet applicable because the structure of the target macromolecule is unknown; in these cases, quantitative structure-activity relationship (QSAR) techniques provide the best approach to rational drug design. Traditional (two-dimensional) QSAR methods attempt to correlate biological activity with local features of atoms, whole molecular properties (e.g. charge) and substituent effects (e.g. fragment hydrophobicity indices). New developments in traditional QSAR continue to appear in the literature (e.g. the OASIS program) (10). Most interest in this field, however, now focuses on three-dimensional QSAR. Recent examples of molecules to which this approach has been applied include HIV-1 protease (11) and the cholecystokinin-A receptor (12).
LIGAND BASED DRUG DESIGN
Ligand based approach contributes to the processes of target identification by providing functional information about target candidates and positioning information to biological networks. For those diseases caused by external pathogens such as bacteria and viruses, unique targets might be found in the pathogens by comparing functional genomics from humans with the corresponding genomics from pathogens (13). For example, Dutta et al. used a subtractive genomic method to analyze the completed genome of Helicobacter pylori (H. pylori) and identified a set of genes that are likely to be essential to the pathogen but are absent in humans (14). In theory, it is possible to recognize all the targets in the pathogen in this way; whereas for endogenous diseases, targets could be discovered by analyzing the differences of genomics between normal and abnormal tissues. A good example of this issue is that several novel steroid targets were identified by combinative use of bioinformatics and functional analysis of hormone
response elements (15). Traditionally, the first consideration before embarking on a computer-aided drug design (CADD) project is whether the detailed three-dimensional structure of the drug target is known. This determines whether a ligand-based (QSAR, CoMFA, pharmacophore) or a structure-based approach (docking, de novo ligand design) is undertaken to generate new lead compounds, which are then evaluated in an iterative process (16). This methodology proceeds to the selection of a small number of the best candidates, which are synthesized or purchased and tested for activity at the target. The results are then fed back into the CADD process. The strict separation of ligand- and structure-based CADD methods has numerous drawbacks. Most ligand based strategies propose and evaluate potential lead compounds so as to conserve the three-dimensional arrangement of functional groups on a scaffold believed to be most important in the activity of existing ligands. This precludes the discovery of novel ligands, which undertake different interactions with the target protein. However, docking methods, where a potential new ligand is placed into the binding site of the target and its ‘fit’ evaluated are computationally expensive, especially if induced fit of both ligand and protein are evaluated. Conformational changes, especially large scale changes, in the protein upon ligand binding are often ignored in these studies. Structure-based methods are also limited by the availability of detailed structures of the target, ideally in different conformations, with and without ligands complexed to it. We propose that integration between ligand- and structure-based CADD methodologies which model separate facets of the natural system will allow us to use all available information in a particular drug design project in a quantitative and objective way. Other such combinations of computational tools have been utilized by different groups to augment the capabilities of the individual tools: 3D QSAR and receptor modeling (17), pharmacophores and molecular docking (18), (19), pharmacophores and receptor modeling (20), pharmacophores and pseudo receptor modeling (21) and pharmacophores and 3D QSAR with excluded volumes from crystallographic protein structures (22).
Ligand based approach contributes to the processes of target identification by providing functional information about target candidates and positioning information to biological networks. For those diseases caused by external pathogens such as bacteria and viruses, unique targets might be found in the pathogens by comparing functional genomics from humans with the corresponding genomics from pathogens (13). For example, Dutta et al. used a subtractive genomic method to analyze the completed genome of Helicobacter pylori (H. pylori) and identified a set of genes that are likely to be essential to the pathogen but are absent in humans (14). In theory, it is possible to recognize all the targets in the pathogen in this way; whereas for endogenous diseases, targets could be discovered by analyzing the differences of genomics between normal and abnormal tissues. A good example of this issue is that several novel steroid targets were identified by combinative use of bioinformatics and functional analysis of hormone
response elements (15). Traditionally, the first consideration before embarking on a computer-aided drug design (CADD) project is whether the detailed three-dimensional structure of the drug target is known. This determines whether a ligand-based (QSAR, CoMFA, pharmacophore) or a structure-based approach (docking, de novo ligand design) is undertaken to generate new lead compounds, which are then evaluated in an iterative process (16). This methodology proceeds to the selection of a small number of the best candidates, which are synthesized or purchased and tested for activity at the target. The results are then fed back into the CADD process. The strict separation of ligand- and structure-based CADD methods has numerous drawbacks. Most ligand based strategies propose and evaluate potential lead compounds so as to conserve the three-dimensional arrangement of functional groups on a scaffold believed to be most important in the activity of existing ligands. This precludes the discovery of novel ligands, which undertake different interactions with the target protein. However, docking methods, where a potential new ligand is placed into the binding site of the target and its ‘fit’ evaluated are computationally expensive, especially if induced fit of both ligand and protein are evaluated. Conformational changes, especially large scale changes, in the protein upon ligand binding are often ignored in these studies. Structure-based methods are also limited by the availability of detailed structures of the target, ideally in different conformations, with and without ligands complexed to it. We propose that integration between ligand- and structure-based CADD methodologies which model separate facets of the natural system will allow us to use all available information in a particular drug design project in a quantitative and objective way. Other such combinations of computational tools have been utilized by different groups to augment the capabilities of the individual tools: 3D QSAR and receptor modeling (17), pharmacophores and molecular docking (18), (19), pharmacophores and receptor modeling (20), pharmacophores and pseudo receptor modeling (21) and pharmacophores and 3D QSAR with excluded volumes from crystallographic protein structures (22).
STRUCTURE BASED DRUG DESIGN
Structure guided methods are an integral part of drug development for known 3D structure of potential drug binding sites, which are the active sites. In structure guided drug design, a known 3D structure of a target bound to its natural ligand or a drug is determined either by X-ray crystallography or by NMR to identify its binding site, the so called active site. For a lead discovery, this is the starting point of structure guided drug design for a known target. Once the ligand bound 3D structure is known, a virtual screening of large collections of chemical compounds, such as ZINC, can be performed. Such screening enables the identification of potential new drugs by performing docking experiment of this collection of molecules. To enhance binding and hence to improve binding affinity/specificity, a group of molecules with similar docking scores is generally used for potency determination; this is High-Throughput Screening (HTS). After the determination of biological potency, several properties such as relationships (QSAR, QSPR, between potency and docking scores) including statistical analysis can be performed to ascertain the potential molecule(s) for lead drug discovery. Before optimization, the lead molecules could be examined further to understand the ADME and reactivity. Investigation of reactivity (examination of electrophilic, nucleophilic or radicals attack) and spectra such as UV Visible of large molecules can be performed applying Gaussian, a powerful quantum mechanical procedure. Instead of virtual screening of a collection of small molecules, a virtual screening of a collection of targets against a single potent drug whose target is unknown could be performed. Such screening would help in the identification of a potential target for the potent drug. At the end a target identified by this docking technique must be verified experimentally. To our knowledge, this approach of identification of target for a potent drug has not been applied. This approach of target identification for a potent drug with unknown target offers a unique opportunity for lead discovery (23). Peptidases are perhaps the largest class of enzyme to be used as targets for structure-based drug design. Among the most successful applications to date are drugs against HIV protease and human rennin that stop viral replication and regulate blood pressure, respectively (24). The matrix metalloproteases (MMPs) are a family of about a dozen zinc-containing enzymes that cleave structural proteins such as collagens, gelatins and proteoglycans. These enzymes are involved in tissue rearrangement during embryonic development and wound healing, and also in the process of new blood vessel formation (angiogenesis). Experimental studies have shown that MMP inhibitors block the invasive and metastatic activity of tumors, as well as inhibiting angiogenesis. It is believed that MMP inhibitors should also prevent or delay the resorption of cartilage and bone that occurs around arthritic joints. The first-generation MMP inhibitors, however, are rather non-selective inhibitors of all members of the MMP family; thus, there is great interest in exploring more specific inhibitors of, for example, fibroblast collagenase or stromelysin-1. Several recently published structures open up the prospect of designing such compounds by using CADD. Three studies (25) report human fibroblast collagenase structures, one of which contains a bound peptide inhibitor (26) and another autoinhibitory propeptide sequence (27). A structure of the catalytic domain of human neutrophil collagenase is also available (28). Browner et al. (29) have reported the structure of human matrilysin, the smallest known member of the MMP family. Of particular interest for drug design, this study compares structures complexed with three inhibitors, whose binding affinity covers a >100-fold range. The inhibitors are identical except for their zinc-chelating groups, which are hydroxamate (most potent), carboxylate, and sulfodiimine. The above studies provide information that should facilitate the design of novel chemotherapeutics that circumvent mechanisms of resistance to current agents. Finally, among other new structures of potential pharmaceutical interest is the binding domain of the thyroid hormone receptor complexed to its natural ligand, triiodothyronine (30). Niwas et al. (31) have published the fifth in a series of papers on structure-based design of purine nucleoside phosphorylase (PNP) inhibitors; in this instance, they describe the design of 9-deazahypoxanthines. Other PNP inhibitors from the same group are currently in clinical trials as immunosuppressive agents and anticancer agents. Verlinde and Hol (32) have recently reviewed methods used in structure-based drug design. The present discussion illustrates some recent developments with a few examples. Gehlhaar et al. (33) have described a de novo ligand designer that generates structures in an active site. The program, MCDNLG, starts by filling the active site with a close-packed array of carbon atoms. The use of in silico drug design has led to the discovery of indinavir, the HIV protease inhibitor (34), and the identification of haloperidol as a lead compound in a structure-based design study for nonpeptide inhibitors of HIV (35). Structure-based approach that has shown promise in recent years is to use computational methods to find putative binding proteins for a given compound from either genomic or protein databases, and to subsequently use experimental procedures to validate the computational result (36). One such computational approach, which is the reverse of docking a set of ligands into a given target, is to dock a compound with a known biological activity into the binding sites of all the three-dimensional (3D) structures in a given protein database. Protein ‘hits’ identified in this manner can then serve as potential candidates for experimental validation. Accordingly, this approach is referred to as reverse docking (or inverse docking) (37).
Structure guided methods are an integral part of drug development for known 3D structure of potential drug binding sites, which are the active sites. In structure guided drug design, a known 3D structure of a target bound to its natural ligand or a drug is determined either by X-ray crystallography or by NMR to identify its binding site, the so called active site. For a lead discovery, this is the starting point of structure guided drug design for a known target. Once the ligand bound 3D structure is known, a virtual screening of large collections of chemical compounds, such as ZINC, can be performed. Such screening enables the identification of potential new drugs by performing docking experiment of this collection of molecules. To enhance binding and hence to improve binding affinity/specificity, a group of molecules with similar docking scores is generally used for potency determination; this is High-Throughput Screening (HTS). After the determination of biological potency, several properties such as relationships (QSAR, QSPR, between potency and docking scores) including statistical analysis can be performed to ascertain the potential molecule(s) for lead drug discovery. Before optimization, the lead molecules could be examined further to understand the ADME and reactivity. Investigation of reactivity (examination of electrophilic, nucleophilic or radicals attack) and spectra such as UV Visible of large molecules can be performed applying Gaussian, a powerful quantum mechanical procedure. Instead of virtual screening of a collection of small molecules, a virtual screening of a collection of targets against a single potent drug whose target is unknown could be performed. Such screening would help in the identification of a potential target for the potent drug. At the end a target identified by this docking technique must be verified experimentally. To our knowledge, this approach of identification of target for a potent drug has not been applied. This approach of target identification for a potent drug with unknown target offers a unique opportunity for lead discovery (23). Peptidases are perhaps the largest class of enzyme to be used as targets for structure-based drug design. Among the most successful applications to date are drugs against HIV protease and human rennin that stop viral replication and regulate blood pressure, respectively (24). The matrix metalloproteases (MMPs) are a family of about a dozen zinc-containing enzymes that cleave structural proteins such as collagens, gelatins and proteoglycans. These enzymes are involved in tissue rearrangement during embryonic development and wound healing, and also in the process of new blood vessel formation (angiogenesis). Experimental studies have shown that MMP inhibitors block the invasive and metastatic activity of tumors, as well as inhibiting angiogenesis. It is believed that MMP inhibitors should also prevent or delay the resorption of cartilage and bone that occurs around arthritic joints. The first-generation MMP inhibitors, however, are rather non-selective inhibitors of all members of the MMP family; thus, there is great interest in exploring more specific inhibitors of, for example, fibroblast collagenase or stromelysin-1. Several recently published structures open up the prospect of designing such compounds by using CADD. Three studies (25) report human fibroblast collagenase structures, one of which contains a bound peptide inhibitor (26) and another autoinhibitory propeptide sequence (27). A structure of the catalytic domain of human neutrophil collagenase is also available (28). Browner et al. (29) have reported the structure of human matrilysin, the smallest known member of the MMP family. Of particular interest for drug design, this study compares structures complexed with three inhibitors, whose binding affinity covers a >100-fold range. The inhibitors are identical except for their zinc-chelating groups, which are hydroxamate (most potent), carboxylate, and sulfodiimine. The above studies provide information that should facilitate the design of novel chemotherapeutics that circumvent mechanisms of resistance to current agents. Finally, among other new structures of potential pharmaceutical interest is the binding domain of the thyroid hormone receptor complexed to its natural ligand, triiodothyronine (30). Niwas et al. (31) have published the fifth in a series of papers on structure-based design of purine nucleoside phosphorylase (PNP) inhibitors; in this instance, they describe the design of 9-deazahypoxanthines. Other PNP inhibitors from the same group are currently in clinical trials as immunosuppressive agents and anticancer agents. Verlinde and Hol (32) have recently reviewed methods used in structure-based drug design. The present discussion illustrates some recent developments with a few examples. Gehlhaar et al. (33) have described a de novo ligand designer that generates structures in an active site. The program, MCDNLG, starts by filling the active site with a close-packed array of carbon atoms. The use of in silico drug design has led to the discovery of indinavir, the HIV protease inhibitor (34), and the identification of haloperidol as a lead compound in a structure-based design study for nonpeptide inhibitors of HIV (35). Structure-based approach that has shown promise in recent years is to use computational methods to find putative binding proteins for a given compound from either genomic or protein databases, and to subsequently use experimental procedures to validate the computational result (36). One such computational approach, which is the reverse of docking a set of ligands into a given target, is to dock a compound with a known biological activity into the binding sites of all the three-dimensional (3D) structures in a given protein database. Protein ‘hits’ identified in this manner can then serve as potential candidates for experimental validation. Accordingly, this approach is referred to as reverse docking (or inverse docking) (37).
COMPUTATIONAL TOOLS USED IN CADD
The software that is available for computer-aided drug design and development originates from different sources. These include commercial companies, academic institutions, open-source software or in-house development. Each of these sources has its pros and cons, and the appropriate choice varies for institutions that use the software. These software packages also differ in terms of cost, functionality and efficacy (38), and automation (39).
The software that is available for computer-aided drug design and development originates from different sources. These include commercial companies, academic institutions, open-source software or in-house development. Each of these sources has its pros and cons, and the appropriate choice varies for institutions that use the software. These software packages also differ in terms of cost, functionality and efficacy (38), and automation (39).
VISUALIZATION
To view the optimized ligand or chemical compound and target molecule, over viewing tools are used. They are Rasmol, VMD, Molscript, Raster 3D. Rasmol is a computer program written for molecular graphics visualization intended and used primarily for the depiction and exploration of biological macromolecule structures.
To view the optimized ligand or chemical compound and target molecule, over viewing tools are used. They are Rasmol, VMD, Molscript, Raster 3D. Rasmol is a computer program written for molecular graphics visualization intended and used primarily for the depiction and exploration of biological macromolecule structures.
HOMOLOGY MODELING
Molecular modeling (40) is a science of representing molecular structures numerically and simulating their behaviors with the equations of quantum and classical physics. Most drug targets are proteins so it is important to know their 3D structures in detail. It estimated that the human body has five lacks to one million proteins, but the 3D structure is known for only a small fraction of these. Homology modeling is used to predict the 3D structure of proteins (41). Homology modeling is nothing but similarity searching for drug analogs. It starts with promising drug molecule. There are two computational tools for similarity searching and sequence alignment such as BLAST, FASTA and for multiple sequence alignments ClastalW ClastalX.
Molecular modeling (40) is a science of representing molecular structures numerically and simulating their behaviors with the equations of quantum and classical physics. Most drug targets are proteins so it is important to know their 3D structures in detail. It estimated that the human body has five lacks to one million proteins, but the 3D structure is known for only a small fraction of these. Homology modeling is used to predict the 3D structure of proteins (41). Homology modeling is nothing but similarity searching for drug analogs. It starts with promising drug molecule. There are two computational tools for similarity searching and sequence alignment such as BLAST, FASTA and for multiple sequence alignments ClastalW ClastalX.
HOMOLOGY MODELING PROGRAMS
There are two Homology Modeling Programs. They are Swiss Model, Modeller
Swiss Model makes it quick and easy to submit a target sequence and get back an automatically generate a comparative model, provided an empirical structure with >30% sequence identity exist to use as a template. Modeller is used for homology or comparative modeling of protein 3D structure. The user provides an alignment of a sequence to be modeled with known related structures and modeler automatically calculates a model containing all non hydrogen atoms.
There are two Homology Modeling Programs. They are Swiss Model, Modeller
Swiss Model makes it quick and easy to submit a target sequence and get back an automatically generate a comparative model, provided an empirical structure with >30% sequence identity exist to use as a template. Modeller is used for homology or comparative modeling of protein 3D structure. The user provides an alignment of a sequence to be modeled with known related structures and modeler automatically calculates a model containing all non hydrogen atoms.
MOLECULAR DYNAMICS
Molecular dynamics (42), (43) is a study of movement of molecule. Every molecule has its own frequency of vibration. It can oscillate position one to two through zero, where the molecule has high potential energy at one and two position and least at zero position.
Molecular dynamics (42), (43) is a study of movement of molecule. Every molecule has its own frequency of vibration. It can oscillate position one to two through zero, where the molecule has high potential energy at one and two position and least at zero position.
ENERGY MINIMIZATION
It is also called energy optimization or geometry optimization; it is used to compute the equilibrium configuration of molecules and solids. By this technique we can only obtain a final state of system that corresponds to minimum of potential energy. In Energy minimization one can obtain a molecule with least energy state i.e. zero energy state. In this state molecule get equilibrium configuration. Energy minimization tools are GAMESS Ghemical PS13 TINKER Ghemical (44) can be used or PS13 For quantum mechanical calculations. If proteins are used, a program such as PyMol can be used to identify ligand binding pockets, together with the DeepView PDB viewer to investigate the amino acid sequences of the protein. To transfer files between programs, Open Babel might be useful or even required to interconvert the file formats.
It is also called energy optimization or geometry optimization; it is used to compute the equilibrium configuration of molecules and solids. By this technique we can only obtain a final state of system that corresponds to minimum of potential energy. In Energy minimization one can obtain a molecule with least energy state i.e. zero energy state. In this state molecule get equilibrium configuration. Energy minimization tools are GAMESS Ghemical PS13 TINKER Ghemical (44) can be used or PS13 For quantum mechanical calculations. If proteins are used, a program such as PyMol can be used to identify ligand binding pockets, together with the DeepView PDB viewer to investigate the amino acid sequences of the protein. To transfer files between programs, Open Babel might be useful or even required to interconvert the file formats.
DOCKING
In the field of molecular modeling docking is a method which predicts the preferred orientation of one molecule to second, when bound to each other to form a stable complex. Docking represents ligand binding to its receptor or target protein. Docking is used to identify and optimize drug candidates by examine & modeling molecular interactions between ligand and target macromolecules. In the docking multiple ligand conformations and orientations are generated & the most appropriate ones are selected. There are several docking tools are presently available they are ArgusDock DOCK FRED eHITS AutoDock FTDock (45). Scoring methods are used to rank the affinity of ligands to bind to the active site of a receptor (46). In virtual high throughput screening compounds are docked into an active site and then scored to determine which once morelikely to bind tightly to the target macromolecule.
In the field of molecular modeling docking is a method which predicts the preferred orientation of one molecule to second, when bound to each other to form a stable complex. Docking represents ligand binding to its receptor or target protein. Docking is used to identify and optimize drug candidates by examine & modeling molecular interactions between ligand and target macromolecules. In the docking multiple ligand conformations and orientations are generated & the most appropriate ones are selected. There are several docking tools are presently available they are ArgusDock DOCK FRED eHITS AutoDock FTDock (45). Scoring methods are used to rank the affinity of ligands to bind to the active site of a receptor (46). In virtual high throughput screening compounds are docked into an active site and then scored to determine which once morelikely to bind tightly to the target macromolecule.
QSAR DESCRIPTORS
A descriptor is a molecular property that QSAR can calculate. QSAR provides a wide variety of descriptors that you can use in determining new QSAR relationships. There is a limited number of datasets and little information regarding the training and validation used by previous researchers. Tetko et al. (47) suggested the use of SMILES or .sdf files on a website to promote the calculation of additional parameters by other drug discovery scientists. The self-organizing molecular field analysis (SoMFA) test set, which represents the steroid set used to construct the first comparative molecular field analysis (CoMFA), can be downloaded from the Richards group’s web site. This information facilitates a more-rapid evaluation of the SoMFA program.
A descriptor is a molecular property that QSAR can calculate. QSAR provides a wide variety of descriptors that you can use in determining new QSAR relationships. There is a limited number of datasets and little information regarding the training and validation used by previous researchers. Tetko et al. (47) suggested the use of SMILES or .sdf files on a website to promote the calculation of additional parameters by other drug discovery scientists. The self-organizing molecular field analysis (SoMFA) test set, which represents the steroid set used to construct the first comparative molecular field analysis (CoMFA), can be downloaded from the Richards group’s web site. This information facilitates a more-rapid evaluation of the SoMFA program.
CONCLUTION
The technological progress of CADD brought a paradigm change to both pharmas and research institutions: it was now possible to obtain appropriate hits within several weeks because of the contribution of CADD. Computational technologies have been entering into the functional genomics studies and target identification in particular. After the completion of the human genome and numerous pathogen genomes, efforts are underway to understand the role of gene products in biological pathways and human diseases and to exploit their functions for the sake of discovering new drug targets. Small and cell-permeable chemical ligands are used increasingly in genomic approaches to understand the global functions of genomes and proteomes. This approach is referred to as chemical biology. As such, reverse docking can be referred to as computational chemical biology, which has been proven to be an effective way in finding clues of new targets. On the contrary, the CADD techniques like virtual screening and library design can also be used to design small molecule probes for illuminating the molecular mechanisms underlying biological processes through altering or perturbing the functions of target proteins by inhibiting or activating their normal functions
The technological progress of CADD brought a paradigm change to both pharmas and research institutions: it was now possible to obtain appropriate hits within several weeks because of the contribution of CADD. Computational technologies have been entering into the functional genomics studies and target identification in particular. After the completion of the human genome and numerous pathogen genomes, efforts are underway to understand the role of gene products in biological pathways and human diseases and to exploit their functions for the sake of discovering new drug targets. Small and cell-permeable chemical ligands are used increasingly in genomic approaches to understand the global functions of genomes and proteomes. This approach is referred to as chemical biology. As such, reverse docking can be referred to as computational chemical biology, which has been proven to be an effective way in finding clues of new targets. On the contrary, the CADD techniques like virtual screening and library design can also be used to design small molecule probes for illuminating the molecular mechanisms underlying biological processes through altering or perturbing the functions of target proteins by inhibiting or activating their normal functions
REFERENCES
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2. Yun Tang et al. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Vol. 3, No. 3, 2006
3. Yun Tang et al. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Vol. 3, No. 3, 2006
4. Robert C Jackson et al. Update on computer-aided drug design, Current Opinion in Biotechnology , 6:646-651, 1995
5. Werner J.Geldenhuys et al. Optimizing the use of open-source software applications in drug discovery, Volume 11, Number 3/4 February 2006
6. Jorgensen, W.L. et al. The many roles of computation in drug discovery. Science 303, 1813- 1818, 2004
7. Cohen, M.S. et al. Structural bioinformatics-based design of selective, irreversible kinase inhibitors. Science 308, 1318–1321, 2005
8. Hajduk, P.J. et al. Predicting protein druggability. Drug Discovery Today 10, 1675–1682, 2005
9. Huang, C.M. et al. Proteomics reveals that proteins expressed during the early stage of Bacillus anthracis infection are potential targets for the development of vaccines and drugs. Genomics Proteomics Bioinformatics 2, 143–151, 2004
10. Mekenyan OG et al. A new development of the OASIS computer system for modeling molecular properties. Computer Chemistry, 18:173-187, 1994
11. Oprea TI et al. 3-D QSAR of HIV-1 protease inhlbltors, 2. Predictive power using limited exploration of alternate binding modes. Med Chem, 37:2206-2215, 1994
12. Tokarski JS et al. 3-D molecular shape analysis-QSAR of a series of cholecystokinin-A receptor antagonists. J Meal Chem, 37:3639-3654, 1994
13. Garcia-Lara, J. et al. Staphylococcus aureus: the search for novel targets. Drug Discovery. Today 10, 643–651, 2005
14. Dutta, A. et al. In silico identification of potential therapeutic target in the human pathogen Helicobacter pylori. In Silico Biol. 6, 0005, 2006
15. Horie-Inoue, K. et al. Identification of novel steroid target genes through the combination of bioinformatics and functional analysis of hormone response elements. Biochem. Biophys. Res. Commun. 339, 99–106, 2006
16. Renate Griffith et al. Combining structure-based drug design and pharmacophores Journal of Molecular Graphics and Modelling, vol 23, 439–446, 2005
17. A. Vedani et al. Quasi-atomistic receptor surface models—a bridge between 3-D QSAR and receptor modelling, J. Am. Chem. Soc. 120, 4471–4477, 1998
18. R. Griffith et al. Docking-derived pharmacophores from models of receptor-ligand complexes, in: O.F. Gu¨ner (Ed.), Pharmacophore Perception, Development and Use in Drug Design, International University Line, La Jolla, California, pp. 385–408, 2000
19. B.E. Thomas IV et al. Pharmacophore-based molecular docking, in: O.F. Gu¨ner (Ed.), Pharmacophore Perception, Development and Use in Drug Design, International University Line, La Jolla, California, pp. 353–367, 2000
20. H.A. Carlson et al. Developing a dynamic pharmacophore model for HIV-1 integrase, J. Med. Chem. vol 43, 2100–2114, 2000
21. K.J. Schleifer et al. Pharmacophore and pseudoreceptor modelling of class Ib antiarrhythmic and local anaestetic lidocaine analogues, Pharmazie, vol 53, 596–602, 1998
22. P.A. Greenidge et al. Pharmacophores incorporating numerous excluded volumes defined by X-ray crystallographic structure in three-dimensional database searching—application to the thyroid hormone receptor, J. Med. Chem. vol 41, 2503–2512, 1998
23. Soma Mandal et al. Rational drug design European Journal of Pharmacology 625, 90–100, 2009
24. Peer RE Mittl et al. Opportunities for structure-based design of protease directed drugs, Current Opinion in Structural Biology, 16:769–775, 2006
25. Spurlino JC et al. A Structure of mature truncated human fibroblast collagenase. Proteins, 19:98-109, 1994
26. Lovejoy B et al. Structure of the catalytic domain of fibroblast collagenase complexed with an inhibitor. Science, 263:375-377, 1994
27. Lovejoy B, et al. Crystal structures of recombinant 19 kDa human fibroblast collagenase complexed to itself. Biochemistry, 33:8207-8217, 1994
28. Bode W et al. The X-ray crystal structure of the catalytic domainoOf human neutrophil collagenase inhibited by a substrate analogue reveals the essentials for catalysis and specificity. EMBO J, 13:1263-1269, 1994
29. Browner MF et al. Matrilysin-inhibitor complexes: common themes among metalloproteases. Biochemistry, 34:6602-6610, 1995
30. McGrath ME et al. Preliminary crystallographic studies of the ligand-binding domain of the thyroid hormone receptor complexed with triiodothyrnnine. J Mol Biol, 237:236-239, 1994
31. Niwas S et al. Structure-based design of inhibitors of purine nncleoside phosphorylase. 5. 9- Deazahypoxanthines. J Med Chem, 37:2477-2480, 1994
32. Verlinde CLMJ et al. Structure-based drug design: progress, results and challenges. Structure, 2:577-587, 1994
33. Gehlhaar DK et al. De novo design of enzyme inhibitors by Monte Carlo ligand generation. J Med Chem, 38:466-472, 1995
34. Wlodawer, A et al. Rational approach to AIDS drug design through structural biology. Annu. Rev. Med. 53, 595–614, 2002
35. DesJarlais, R.L. et al. Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease. Proc. Natl. Acad. Sci.U. S. A. 87, 6644–6648, 1990
36. Rockey, W.M et al. Rapid computational identification of the targets of protein kinase inhibitors. J. Med. Chem. 48, 4138–4152, 2005
37. Chen, Y.Z et al. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins vol 43, 217–226, 2001
38. Marchand-Geneste, N. et al. e-Quantum chemistry free resources. SAR QSAR Environ. Res. 15, 43–54, 2004
39. Carpy, A.J. et al. e-Molecular shapes and properties. SAR QSAR Environ. Res.14, 329–337, 2003
40. Bing Wu et al. In silico predication of nuclear hormone receptors for organic pollutants by homology modeling and molecular docking Toxicology Letters 191, 69–73, 2009
41. Qian Yang et al. Modeling the binding modes of Kv1.5 potassium channel and blockers Journal of Molecular Graphics and Modelling 27, 178–187, 2008
42. Luis Fernando Saraiva Macedo Timmers et al. Molecularmodeling, dynamics and docking studies of PurineNucleoside Phosphorylase from Streptococcus pyogenes Biophysical Chemistry 142, 7–16, 2009
43. Tamara Frembgen-Kesner et al. Computational Sampling of a Cryptic Drug Binding Site in a Protein Receptor: Explicit Solvent Molecular Dynamics and Inhibitor Docking to p38 MAP Kinase J. Mol. Biol. 359, 202–214, 2006
44. Werner J.Geldenhuys et al. Optimizing the use of open-source software applications in drug discovery DDT Volume 11, Number 3/4 February 2006
45. Sandeep Sundriyal et al. New PPARc ligands based on barbituric acid: Virtual screening, synthesis and receptor binding studies Bioorganic & Medicinal Chemistry Letters 18, 4959–4962, 2008
46. Y.Z. Chen et al. Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand protein inverse docking approach Journal of Molecular Graphics and Modelling 20, 199–218, 2001
47. Tetko, I.V. et al. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. J. Chem. Inf. Comput. Sci. 42, 1136–1145, 2002
1. Baldi A et al. Computational Approaches for Drug Design and Discovery: An Over View, IP: 117.204.64.189 August 25, 2010
2. Yun Tang et al. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Vol. 3, No. 3, 2006
3. Yun Tang et al. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Vol. 3, No. 3, 2006
4. Robert C Jackson et al. Update on computer-aided drug design, Current Opinion in Biotechnology , 6:646-651, 1995
5. Werner J.Geldenhuys et al. Optimizing the use of open-source software applications in drug discovery, Volume 11, Number 3/4 February 2006
6. Jorgensen, W.L. et al. The many roles of computation in drug discovery. Science 303, 1813- 1818, 2004
7. Cohen, M.S. et al. Structural bioinformatics-based design of selective, irreversible kinase inhibitors. Science 308, 1318–1321, 2005
8. Hajduk, P.J. et al. Predicting protein druggability. Drug Discovery Today 10, 1675–1682, 2005
9. Huang, C.M. et al. Proteomics reveals that proteins expressed during the early stage of Bacillus anthracis infection are potential targets for the development of vaccines and drugs. Genomics Proteomics Bioinformatics 2, 143–151, 2004
10. Mekenyan OG et al. A new development of the OASIS computer system for modeling molecular properties. Computer Chemistry, 18:173-187, 1994
11. Oprea TI et al. 3-D QSAR of HIV-1 protease inhlbltors, 2. Predictive power using limited exploration of alternate binding modes. Med Chem, 37:2206-2215, 1994
12. Tokarski JS et al. 3-D molecular shape analysis-QSAR of a series of cholecystokinin-A receptor antagonists. J Meal Chem, 37:3639-3654, 1994
13. Garcia-Lara, J. et al. Staphylococcus aureus: the search for novel targets. Drug Discovery. Today 10, 643–651, 2005
14. Dutta, A. et al. In silico identification of potential therapeutic target in the human pathogen Helicobacter pylori. In Silico Biol. 6, 0005, 2006
15. Horie-Inoue, K. et al. Identification of novel steroid target genes through the combination of bioinformatics and functional analysis of hormone response elements. Biochem. Biophys. Res. Commun. 339, 99–106, 2006
16. Renate Griffith et al. Combining structure-based drug design and pharmacophores Journal of Molecular Graphics and Modelling, vol 23, 439–446, 2005
17. A. Vedani et al. Quasi-atomistic receptor surface models—a bridge between 3-D QSAR and receptor modelling, J. Am. Chem. Soc. 120, 4471–4477, 1998
18. R. Griffith et al. Docking-derived pharmacophores from models of receptor-ligand complexes, in: O.F. Gu¨ner (Ed.), Pharmacophore Perception, Development and Use in Drug Design, International University Line, La Jolla, California, pp. 385–408, 2000
19. B.E. Thomas IV et al. Pharmacophore-based molecular docking, in: O.F. Gu¨ner (Ed.), Pharmacophore Perception, Development and Use in Drug Design, International University Line, La Jolla, California, pp. 353–367, 2000
20. H.A. Carlson et al. Developing a dynamic pharmacophore model for HIV-1 integrase, J. Med. Chem. vol 43, 2100–2114, 2000
21. K.J. Schleifer et al. Pharmacophore and pseudoreceptor modelling of class Ib antiarrhythmic and local anaestetic lidocaine analogues, Pharmazie, vol 53, 596–602, 1998
22. P.A. Greenidge et al. Pharmacophores incorporating numerous excluded volumes defined by X-ray crystallographic structure in three-dimensional database searching—application to the thyroid hormone receptor, J. Med. Chem. vol 41, 2503–2512, 1998
23. Soma Mandal et al. Rational drug design European Journal of Pharmacology 625, 90–100, 2009
24. Peer RE Mittl et al. Opportunities for structure-based design of protease directed drugs, Current Opinion in Structural Biology, 16:769–775, 2006
25. Spurlino JC et al. A Structure of mature truncated human fibroblast collagenase. Proteins, 19:98-109, 1994
26. Lovejoy B et al. Structure of the catalytic domain of fibroblast collagenase complexed with an inhibitor. Science, 263:375-377, 1994
27. Lovejoy B, et al. Crystal structures of recombinant 19 kDa human fibroblast collagenase complexed to itself. Biochemistry, 33:8207-8217, 1994
28. Bode W et al. The X-ray crystal structure of the catalytic domainoOf human neutrophil collagenase inhibited by a substrate analogue reveals the essentials for catalysis and specificity. EMBO J, 13:1263-1269, 1994
29. Browner MF et al. Matrilysin-inhibitor complexes: common themes among metalloproteases. Biochemistry, 34:6602-6610, 1995
30. McGrath ME et al. Preliminary crystallographic studies of the ligand-binding domain of the thyroid hormone receptor complexed with triiodothyrnnine. J Mol Biol, 237:236-239, 1994
31. Niwas S et al. Structure-based design of inhibitors of purine nncleoside phosphorylase. 5. 9- Deazahypoxanthines. J Med Chem, 37:2477-2480, 1994
32. Verlinde CLMJ et al. Structure-based drug design: progress, results and challenges. Structure, 2:577-587, 1994
33. Gehlhaar DK et al. De novo design of enzyme inhibitors by Monte Carlo ligand generation. J Med Chem, 38:466-472, 1995
34. Wlodawer, A et al. Rational approach to AIDS drug design through structural biology. Annu. Rev. Med. 53, 595–614, 2002
35. DesJarlais, R.L. et al. Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease. Proc. Natl. Acad. Sci.U. S. A. 87, 6644–6648, 1990
36. Rockey, W.M et al. Rapid computational identification of the targets of protein kinase inhibitors. J. Med. Chem. 48, 4138–4152, 2005
37. Chen, Y.Z et al. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins vol 43, 217–226, 2001
38. Marchand-Geneste, N. et al. e-Quantum chemistry free resources. SAR QSAR Environ. Res. 15, 43–54, 2004
39. Carpy, A.J. et al. e-Molecular shapes and properties. SAR QSAR Environ. Res.14, 329–337, 2003
40. Bing Wu et al. In silico predication of nuclear hormone receptors for organic pollutants by homology modeling and molecular docking Toxicology Letters 191, 69–73, 2009
41. Qian Yang et al. Modeling the binding modes of Kv1.5 potassium channel and blockers Journal of Molecular Graphics and Modelling 27, 178–187, 2008
42. Luis Fernando Saraiva Macedo Timmers et al. Molecularmodeling, dynamics and docking studies of PurineNucleoside Phosphorylase from Streptococcus pyogenes Biophysical Chemistry 142, 7–16, 2009
43. Tamara Frembgen-Kesner et al. Computational Sampling of a Cryptic Drug Binding Site in a Protein Receptor: Explicit Solvent Molecular Dynamics and Inhibitor Docking to p38 MAP Kinase J. Mol. Biol. 359, 202–214, 2006
44. Werner J.Geldenhuys et al. Optimizing the use of open-source software applications in drug discovery DDT Volume 11, Number 3/4 February 2006
45. Sandeep Sundriyal et al. New PPARc ligands based on barbituric acid: Virtual screening, synthesis and receptor binding studies Bioorganic & Medicinal Chemistry Letters 18, 4959–4962, 2008
46. Y.Z. Chen et al. Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand protein inverse docking approach Journal of Molecular Graphics and Modelling 20, 199–218, 2001
47. Tetko, I.V. et al. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. J. Chem. Inf. Comput. Sci. 42, 1136–1145, 2002
