Current Computer - Aided Drug Design - Volume 7, Issue 4, 2011

In our days, there has been an explosion on the use of Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems in principle. We see QSPR model as a function that predict the properties of the system (drug, protein, RNA, proteome, diseasome) using parameters that numerically describe the structure of the system. In particular Topological Indices (TIs) are numerical parameters that quantify the structure of Graphs and Complex Networks used to represent the molecular, social, technological, and/or bio-systems. Using TIs as inputs we can find different types of QSPR-like models that fit to more specific situations, for instance Quantitative Structure-Activity Relationships (QSAR), Quantitative Structure- Toxicity Relationships (QSTR) or Quantitative Structure-Reactivity Relationships (QSRR), to cite a few examples. In all these cases, we can find models that use the TIs of the system as input to predict the properties of this system (output), see the recent book edited by Gonzalez-Diaz and Munteanu in 2010 [1]. In a recent, preliminary review in the field published in Proteomics in 2008 Gonzalez-Diaz et al. discussed the use of these methods but only from the point of view of proteins [2]. Next we extended the discussion to a number of authors editing special issues on Current Topics in Medicinal Chemistry in 2008 [3-12], Current Proteomics in December 2009 [13-19], Current Drug Metabolism [20-28] and Current Pharmaceutical Design in 2010 [29-37], and Current Bioinformatics in 2011 [38-47]. Taking all the previous aspects into consideration we decided to guest-edit the present hot-topic issue for Current Computer- Aided Drug Design. The main aim of the issue is to review and discuss new trends in the use of QSAR/QSPR-like methods, networks theory, and TIs in CADD. In the first paper, Jayadeepa et al. [48] (from Bangalore, INDIA) focused their attention on CADD study of the drug target 5-α-reductase (5αR), which is important in prostate cancer. In the second paper, Speck-Planche and Cordeiro [49] (REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, Portugal) focus on CADD approach for the design of novel anti-HIV drugs. In the third paper, Garcia, Fall, and Gomez [50] (from University of Vigo, Spain) deal with DNA polymerases, essential enzymes for DNA replication, repair and recombination. In the next work, Dave et al. [51] (from Sardar Patel University, Gujarat; India) discuss CADD techniques for exploration of drug target in H1N1 strain is hemagglutinin. The following paper [52] gives a review of Synthesis, Biological assay, and QSAR studies of β- secretase inhibitors, important for Alzheimer's disease (AD). After that, a group of authors coordinated by Khan (from Genok - Center for Biosafety, Tromso, Norway and Ankara University, Turkey) presented a work [53] where they studied two alkaloids namely (+)-buxabenzamidienine and (+)-buxamidine from Buxus sempervirens, using bioassay-guided fractionation and isolation method. Next, Concu (from University of Cagliari, Italy) and Shen (from Center for Systems Biology, Soochow University, China) et al. [54] reviewed CADD studies on Collagen, the most abundant protein of the whole human body, and its instability being related to a number of important diseases like osteogenesis imperfect, Ehlers-Danlos Syndrome, Collagenopathy. In the following paper, Chis et al. [55] (a team of authors from Institute for Marine Research (IIM-CSIC), Vigo, Spain, and West University of Timisoara, Romania) considered models of interaction between the immune system and tumor cells. Next, another paper [56] by authors from the University of Santiago, Cuba, and University of Porto, Portugal focused CADD techniques applied to pesticides design. In the last work [57], we intend to offer a common background and withdrawn general conclusions to all the manuscripts presented in this special issue making emphasis on the generalization of QSAR/QSPR and Complex network tools inside/outside CADD. In so doing, we have made a review of more common types of complex networks involving drugs or their targets. We expect that the present issue may become an interesting resource for all those authors interested in classic methods and new trends in CADD. With this aim, the issue groups 10 papers with the opinion of more than 50 researchers. These authors come from known institutions of Asia (China, India, Turkey), America (US, Cuba), and the EU (Spain, Portugal, Romania, Italy, and Norway). I would like to express here my gratitude to all these authors for their kind contribution. I also acknowledge the kind attention and collaboration of both teams, the editorial board of the journal Current Computer-Aided Drug Design (CCADD) and Bentham Science Publishers; with special mention to Editor Prof. Subhash C. Basak and Publication Manager Ms. Madiha Rauf, respectively.....

Taking into consideration the high importance of the drug target 5-α-reductase (5αR) in prostate cancer in this work we are going first to review previous works and discuss works related to the computer aided drug design of 5αR inhibitors. We report new results in the in silico screening of natural 5αR inhibitors. Traditionally, drugs were discovered by testing compounds synthesized in time consuming multi-step processes against a battery of in vivo biological screens. Promising compounds were then further studied in development, where their pharmacokinetic properties, metabolism and potential toxicity were investigated. Here we present a study on herbal lead compounds and their potential binding affinity to the effectors molecules of major disease like Prostate Cancer. Clinical studies demonstrate a positive correlation between the extent of 5αR type 2 (5αR2) and malignant progression of precancerous lesions in prostate. Therefore, identification of effective, well-tolerated 5αR inhibitors represents a rational chemo preventive strategy. This study has investigated the effects of naturally occurring non-protein compounds berberine and monocaffeyltartaric acid that inhibits 5αR type2. Our results reveal that these compounds use less energy to bind to 5αR and inhibit its activity. Their high ligand binding affinity to 5αR introduce the prospect for their use in chemopreventive applications; in addition they are freely available natural compounds that can be safely used to prevent prostate cancer.

Human immunodeficiency virus (HIV) is the responsible causal agent of acquired immunodeficiency syndrome (AIDS), a condition in humans in which the immune system begins to fail, allowing the entry of opportunistic infections. HIV infection in humans is considered pandemic by the World Health Organization (WHO). HIV needs to use a protein as a co-receptor to enter its target cells. Several chemokine receptors can in principle act as viral co-receptors, but the chemokine (C-C motif) receptor 5 (CCR5) is likely the most physiologically important co-receptor during natural infection. For this reason the development of new CCR5 inhibitors like anti-HIV agents, constitutes a challenge for the scientific community. The present review will focus on the current state of the design of novel anti-HIV drugs, and how the existing computer aided-drug design methodologies, have been effective in the search of new anti-HIV agents. In addition, a QSAR model based on substructural descirptors is presented as a rapid, rational and promising alternative for the discovery of anti-HIV agents through the inhibition of the CCR5.

DNA polymerases are essential enzymes for DNA replication, repair and recombination. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the DNA polymerase inhibitors. In this work, we revised different computational studies for a very large and heterogeneous series of DNA polymerase inhibitors. First, we reviewed QSAR methods with different compounds to find out the structural requirements for DNA polymerase inhibitory activity. Last, we report a new LDA analysis with the different molecular descriptors calculated with DRAGON software.

Recently WHO and NREVSS collaborating laboratories located in all 50 states, and Washington D.C reported that out of 3,588 specimens,164 were found positive for influenza type (i.e. 4.6%) and from these 164 specimens 162 (i.e. 98.8 %) were of influenza A H1N1 subtype. Comparative study of the past and current reports gives a general idea that the influenza activity deserves high attention from public health authorities in the U.S. In this connection, presently some groups are developing intensive computer-aided research in QSAR, Docking, Molecular Modeling and Drug Design, Sequence Analysis and Phylogenetic analysis of candidate compounds and/or targets; in order to advance in the treatment and/or prevention of this pandemic Flu. In this work, primarily we carry out a mini-review of the more important theoretical studies reported until now within this area, followed by the study of a specific type of target. Keeping in view the nature of this virus, we can conclude that there is always a need to find other target protein as inhibitor other than the existing one. So that this lethal pandemic flu can be treated and prevented further. Therefore, after Neuraminidase and M2 ion channels the surface protein that we can target in H1N1 strain is Hemagglutinins (HA). We use comparative modeling; which is one of the methods that can reliably generate a 3D model for HA protein. Multiple structures of this subtype of Influenza Virus are available at PDB, but we are focused on Influenza A (H1N1). Therefore, methodology of analysis mainly focuses on modeling the structure of this protein and, if possible, finding a probable active sites and inhibitors to it.

Alzheimer's disease (AD) is highly complex. While several pathologies characterize this disease, amyloid plaques, composed of the β-amyloid peptide, are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that β-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. The BACE-1 enzyme is essential for the generation of β-amyloid. BACE-1 knockout mice do not produce β-amyloid and are free from Alzheimer's associated pathologies, including neuronal loss and certain memory deficits. The fact that BACE-1 initiates the formation of β-amyloid, and the observation that BACE-1 levels are elevated in this disease provide direct and compelling reasons to develop therapies directed at BACE-1 inhibition, thus reducing β-amyloid and its associated toxicities. In this sense, quantitative structure-activity relationships (QSAR) could play an important role in studying these β-secretase inhibitors. QSAR models are necessary in order to guide the β-secretase synthesis. This work is aimed at reviewing different design and synthesis and computational studies for a very large and heterogeneous series of β-secretase inhibitors. First, we review design, synthesis, and Biological assay of β-secretase inhibitors. Next, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find out the structural requirements. Next, we review QSAR studies using the method of Linear Discriminant Analysis (LDA) in order to understand the essential structural requirement for receptor binding for β- secretase inhibitors.

In this work, two alkaloids namely (+)-buxabenzamidienine (1) and (+)-buxamidine (2) were isolated from Buxus sempervirens, using bioassay-guided fractionation and isolation method. Their acetyl- (AChE) and butyrylcholinesterase (BChE) inhibitory activities were studied and the compounds were found to be quite selective inhibitors of AChE. IC50 values of compound 1 for electric eel AChE and horse BChE were 0.787 and 7.68 mM, respectively; while the corresponding IC50 of compound 2 were 1.70 and 549.98 mM, respectively. Theoretical (quantum mechanical, homology modelling and docking) calculations were performed in order to explain their interactions with different AChE (electric eel and human) and BChE (horse and human). The x-ray crystal structures of electric eel AChE, human AChE, human BChE and a model of horse BChE constructed by homology with human BChE were used for docking of compounds 1 and 2. Density functional theory (DFT) calculations of the compounds were performed at the B3LYP/6- 31G** level using the program Spartan™, and their HOMO and LUMO energy levels were calculated. Docking studies exhibited that compound 1 interacts with the acyl-binding pocket of the active site gorge of huAChE, and including several other hydrophobic interactions.

Collagen is the most abundant protein in the whole human body and its instability is involved in many important diseases, such as Osteogenesis imperfecta, Ehlers-Danlos syndrome, and collagenopathy. The stability of the collagen triple helix is strictly related to its amino acid sequence, especially the main Gly-X-Y motif. Many groups have used computational methods to investigate collagen's structure and the relationship between its stability and structure. In this study, we initially reviewed the most important computational methods that have been applied in this field. We then assembled data on a large number of collagen-like peptides to build the first Markov chain model for predicting the stability of the collagen at different temperatures, simply by analyzing the amino acid sequence. We used the literature to assemble a set of 102 peptides and their relative melting temperatures were determined experimentally, indicating a great variance with the main motif of the collagen. This dataset was then split in two classes, stable and unstable, according to their melting temperatures and the dataset was then used to build artificial neural network (ANN) models to predict collagen stability. We built models to predict stability at temperatures of 38°C, 35°C, 30°C, and 25°C degrees, and all models had an accuracy between 82% and 92%. Several cross-validation procedures were performed to validate the model. This method facilitates fast and accurate predictions of collagen stability at different temperatures.

Pesticides are chemicals with a great impact in the economy of any country. They are employed for the eradication of pests. Insects constitute one of these pests which are extremely difficult to control. With the passage of the time, insects have become resistant to pesticides, causing huge crop losses and diseases in humans. For this reason, there is an increasing need for the design of more potent insecticides. The present review is focused on the current state of the application of computational approaches as essential tools for the design of novel insecticidal agents. Also, a model based on a substructural approach is presented as a rational, efficient and promising alternative for the discovery of new insecticides.

Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures to large systems. We can cite for instance, drug-target protein interaction networks, drug policy legislation networks, or drug treatment in large geographical disease spreading networks. In any case, all these networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and edges (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent. The main reason for this success of TIs is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. Another important reason for the success of TIs is that using these parameters as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for different kind of problems in Computer-Aided Drug Design (CADD). Taking into account all the above-mentioned aspects, the present work is aimed at offering a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most common types of complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. Next, we use for the first time a Markov chain model to generalize Galvez TIs to higher order analogues coined here as the Markov-Galvez TIs of order k (MGk). Lastly, we illustrate the calculation of MGk values for different classes of networks found in drug research, nature, technology, and social-legal sciences.

We consider the deterministic model of interaction between the immune system and tumor cells including a memory function that reflects the influence of the past states, to simulate the time needed by the latter to develop a chemical and cell mediated response to the presence of the tumor. The memory function is called delay kernel. The results are compared with those from other papers, concluding that the memory function introduces new instabilities in the system leading to an uncontrollable growth of the tumor. If the coefficient of the memory function is used as a bifurcation parameter, it is found that Hopf bifurcation occurs for kernel. The direction and stability of the bifurcating periodic solutions are determined. The deterministic model with delay allows stochastic perturbation. Mean value and square mean value of the linearized model are analyzed for the variables of the stochastic model. Some numerical simulations for justifying the theoretical analysis are also given.