Current Topics in Medicinal Chemistry - Volume 13, Issue 14, 2013

With the explosion of protein sequences generated in the postgenomic era, the gap between the number of attribute- known proteins and that of uncharacterized ones has become increasingly large. Knowing the key attributes of proteins is a shortcut for prioritizing drug targets and developing novel new drugs. Unfortunately, it is both time-consuming and costly to acquire these kinds of information by purely conducting biological experiments. Therefore, it is highly desired to develop various computational tools for fast and effectively classifying proteins according to their sequence information alone. The process of developing these high throughput tools is generally involved with the following procedures: (1) constructing benchmark datasets; (2) representing a protein sequence with a discrete numerical model; (3) developing or introducing a powerful algorithm or machine learning operator to conduct the prediction; (4) estimating the anticipated accuracy with a proper and objective test method; and (5) establishing a user-friendly web-server accessible to the public. This minireview is focused on the recent progresses in identifying the types of G-protein coupled receptors (GPCRs), subcellular localization of proteins, DNA-binding proteins and their binding sites. All these identification tools may provide very useful informations for in-depth study of drug metabolism.

Entropy measures are universal parameters useful to codify biologically-relevant information in many systems. In our previous work, (Gonzalez-Diaz, H., et al. Chem. Res. Toxicol. 2003, 16, 1318-1327), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs) to study the quantitative structure-toxicity relationships (QSTR) of drugs. In a second part, (Cruz-Monteagudo, M. et al. Chem. Res. Toxicol., 2008, 21 (3), 619-632), we extended 3D-MEDNEs to numerically encode toxicologically-relevant information present in Mass Spectra of the serum proteome. These works demonstrated that the idea behind classic drug QSTR models can be extended to solve more general problems in toxicological chemical research. For instance, there are not many reports of multi-target QSTR (mt-QSTR) models useful to predict multiplexed endpoints of drugs in a high number of cytotoxicity assays. In this work, we train and validate for the first time a QSTR model that correctly classifies 8,806 out of 9,001 (Accuracy = 91.1%) multiplexing assay endpoints of 7903 drugs (including both training and validation series). Each endpoint corresponds to one out of 1443 assays, 32 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). We have also determined experimentally, for the first time, the values of EC50 = 8.21 μg/mL and Cytotoxicity = 26.25 % for the antimicrobial / antiparasitic drug G1 on Balb/C mouse thymic macrophages using flow cytometry. In addition, we have used the new model to predict G1 endpoints in 1,283 assays finding a low average probability of p(1) = 0.50% in 152 cytotoxicity assays. Last, we have used the model to predict average probability of the interaction of G1 with different proteins in macrophages. Interestingly, the Macrophage colony-stimulating factor receptor, the Macrophage colony-stimulating factor 1 receptor, the Macrophage migration inhibitory factor, Macrophage scavenger receptor types I and II, and the Macrophage-stimulating protein receptor, have also very low average predicted probabilities of p(1) = 0.092, 0.038, 0.077, 0.026, 0.2, 0.106, respectively. Both experimental and theoretical results show a moderate thymic macrophage cytotoxicity of G1. The obtained results are significant because they complement the immunotoxicology studies of this important drug.

β2 agonists and anticholinergics are two major classes of bronchodilators which form first line of drugs recommended in symptomatic treatment of asthma and COPD. Combinational therapy involving β agonists and anticholinergics prove more effective in treating airway disease than use of either agent alone. In present investigation, binding modes of Muscarinic Antagonism and β 2 Agonism (MABA) conjugates designed by Lyn et al. were revealed on structural grounds adopting molecular docking techniques. The conjugates tether β 2 motif onto M3 motif which makes it a single molecule that acts as both β 2 agonist and antimuscarinic agent. Series of conjugates were docked against β 2 adrenergic receptor and modeled M3 muscarinic acetylcholine receptor and pharmacophoric features were identified. Upon screening the conjugates on the basis of receptor ligand free energy, hydrogen bonding and internal electrostatic interaction, conjugate 11 demonstrated superior interactions with the receptors compared to remaining conjugates in the series. While, in vitro results and in silico outcomes are in agreement to reveal that conjugate 11 to possess best pharmacological profile, binding modes obtained in docking can be utilized to design new conjugates with improved biological activity. A close study of receptor residues in binding site and atoms, groups and substructures of conjugates may be used to develop favourable secondary valence forces towards receptor-ligand interactions.

Mycobacteria represent a group of pathogens which cause serious diseases in mammals, including the lethal tuberculosis (Mycobacterium tuberculosis). Despite the mortality of this community-acquired and nosocomial disease mentioned above, other mycobacteria may cause similar infections, acting as dangerous opportunistic pathogens. Additionally, resistant strains belonging to Mycobacterium spp. have emerged. Thus, the design of novel antimycobacterial agents is a challenge for the scientific community. In this sense, chemoinformatics has played a vital role in drug discovery, helping to rationalize chemical synthesis, as well as the evaluation of pharmacological and ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles in both medicinal and pharmaceutical chemistry. Until now, there is no in silico methodology able to assess antimycobacterial activity and ADMET properties at the same time. This work introduces the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous prediction of antimycobacterial activities and ADMET profiles of drugs/chemicals under diverse experimental conditions. The mtk-QSBER model was constructed by using a large and heterogeneous dataset of compounds (more than 34600 cases), displaying accuracies higher than 90% in both, training and prediction sets. To illustrate the utility of the present model, several molecular fragments were selected and their contributions to different biological effects were calculated and analyzed. Also, many properties of the investigational drug TMC-207 were predicted. Results confirmed that, from one side, TMC-207 can be a promising antimycobacterial drug, and on the other hand, this study demonstrates that the present mtk-QSBER model can be used for virtual screening of safer antimycobacterial agents.

Nature is the world’s best chemist: Many naturally occurring compounds have very complicated structures that present great challenges to chemists wishing to determine their structures or replicate them. The plant derived herbal compounds have a long history of clinical use, better patient tolerance and acceptance. Their high ligand binding affinity to the target introduce the prospect of their use in chemo preventive applications; in addition they are freely available natural compounds that can be safely used to prevent various ailments. Plants became the basis of traditional medicine system throughout the world for thousands of years and continue to provide mankind with new remedies. Here, we present a research study on a medicinal plant, Graviola, a native of North America but rarely grown in India. It has a wide potent anticancerous agents coined as Acetogenins which play a key role towards many varieties of cancer, Acetogenins are potent inhibitors of NADH oxidase of the plasma membranes of cancer cells. Potent leads were taken for the study through literature survey, major types of cancer targets were identified, the natureceuticals and the cancer protein were subjected to docking analysis, further with the help of the dock score and other descriptor properties top ranked molecules were collected, commercial drug was also selected and identified as a Test compound for the study. Later, the phytochemicals were subjected to toxicity analysis. Those screened compounds were then considered for active site analysis and to find the best binding site for the study. R Programming library was used to find the best leads. Phytochemicals such as Anonaine, Friedelin, Isolaureline, Annonamine, Anomurine, Kaempferol, Asimilobine, Quercetin, Xylopine were clustered and the highly clustered compounds such as Annonamine , Kaempferol termed to be a potential lead for the study. Further study on experimental analysis may prove the potentiality of these compounds. In the experimental analysis, Graviola leaves were collected, and the extracted components were tested against the HeLa cell line and PC3 cell line. HeLa cells treated with 75 μg of a crude leaf extract of A. muricata showing 80% of cell inhibition. Further investigation of other experimental studies may confirm that these potential lead could make a great impact in which it could help to accelerate the pipeline of drug discovery.

Recent times have seen flooding of biological data into the scientific community. Due to increase in large amounts of data from genome and other sequencing projects become available, being diverted on to Insilco approach for data collection and prediction has become a priority also progresses in sequencing technologies have found an exponential function rise in the number of newly found enzymes. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. As new approaches are needed to determine the functions of the proteins these genes encode. The protein parameters that can be used for an enzyme/ non-enzyme classification includes features of sequences like amino acid composition, dipeptide composition, grand Average of hydropathicity (GRAVY), probability of being in alpha helix, probability of being in beta sheet Probability of being in a turn. We show how large-scale computational analysis can help to address this challenge by help of java and support vector machine library. In this paper, a recently developed machine learning algorithm referred to as the svm library Learning Machine is used to classify protein sequences with six main classes of enzyme data downloaded from a public domain database. Comparative studies on different type of kernel methods like 1.radial basis function, 2.polynomial available in SVM library. Results show that RBF method take less time in training and give more accurate result then other kernel methods to also less training time compared to other kernel methods. The classification accuracy of RBF is also higher than various methods in respect of available sequences data.

The transport of the molecules inside cells is a very important topic, especially in Drug Metabolism. The experimental testing of the new proteins for the transporter molecular function is expensive and inefficient due to the large amount of new peptides. Therefore, there is a need for cheap and fast theoretical models to predict the transporter proteins. In the current work, the primary structure of a protein is represented as a molecular Star graph, characterized by a series of topological indices. The dataset was made up of 2,503 protein chains, out of which 413 have transporter molecular function and 2,090 have no transporter function. These indices were used as input to several classification techniques to find the best Quantitative Structure Activity Relationship (QSAR) model that can evaluate the transporter function of a new protein chain. Among several feature selection techniques, the Support Vector Machine Recursive Feature Elimination allows us to obtain a classification model based on 20 attributes with a true positive rate of 83% and a false positive rate of 16.7%.

Alzheimer's disease (AD) is the most common neurodegenerative disorder and the most prevalent cause of dementia with ageing. The etiology of this illness is rather complex and not completely known but deposits of aberrant β- amyloid protein, as well as τ-protein hyperphosphorylation, oxidative stress, dyshomeostasis of biometals and low levels of acetylcholine (ACh) seem to play a significant role. Such complex etiology of AD has encouraged active research in the development of multi-target drugs with two or more complementary biological activities, since they may represent an important advance in the treatment of this disease. Dual inhibitors combining anti-acetyl cholinesterase (AChE) and antimonoamine oxidase (MAO) activities in one molecular entity have been recently reported. Inhibition of AChE increases neurotransmission at cholinergic synapses and reduce temporally the cognitive deficit. AChE also participates in other functions related to neuronal development, differentiation, adhesion and β-amyloid protein processing. In addition MAOB inhibition retards further deterioration of cognitive functions. In this review relevant aspects about structure, mechanism and pharmacological effects of these dual inhibitors are reported.

Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as “the next GPCRs”. To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment.

In general perturbation methods starts with a known exact solution of a problem and add “small” variation terms in order to approach to a solution for a related problem without known exact solution. Perturbation theory has been widely used in almost all areas of science. Bhor's quantum model, Heisenberg's matrix mechanincs, Feyman diagrams, and Poincare's chaos model or “butterfly effect” in complex systems are examples of perturbation theories. On the other hand, the study of Quantitative Structure-Property Relationships (QSPR) in molecular complex systems is an ideal area for the application of perturbation theory. There are several problems with exact experimental solutions (new chemical reactions, physicochemical properties, drug activity and distribution, metabolic networks, etc.) in public databases like CHEMBL. However, in all these cases, we have an even larger list of related problems without known solutions. We need to know the change in all these properties after a perturbation of initial boundary conditions. It means, when we test large sets of similar, but different, compounds and/or chemical reactions under the slightly different conditions (temperature, time, solvents, enzymes, assays, protein targets, tissues, partition systems, organisms, etc.). However, to the best of our knowledge, there is no QSPR general-purpose perturbation theory to solve this problem. In this work, firstly we review general aspects and applications of both perturbation theory and QSPR models. Secondly, we formulate a general-purpose perturbation theory for multiple-boundary QSPR problems. Last, we develop three new QSPR-Perturbation theory models. The first model classify correctly >100,000 pairs of intra-molecular carbolithiations with 75-95% of Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp). The model predicts probabilities of variations in the yield and enantiomeric excess of reactions due to at least one perturbation in boundary conditions (solvent, temperature, temperature of addition, or time of reaction). The model also account for changes in chemical structure (connectivity structure and/or chirality paterns in substrate, product, electrophile agent, organolithium, and ligand of the asymmetric catalyst). The second model classifies more than 150,000 cases with 85-100% of Ac, Sn, and Sp. The data contains experimental shifts in up to 18 different pharmacological parameters determined in >3000 assays of ADMET (Absorption, Distribution, Metabolism, Elimination, and Toxicity) properties and/or interactions between 31723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model classifies more than 260,000 cases of perturbations in the self-aggregation of drugs and surfactants to form micelles with Ac, Sn, and Sp of 94-95%. The model predicts changes in 8 physicochemical and/or thermodynamics output parameters (critic micelle concentration, aggregation number, degree of ionization, surface area, enthalpy, free energy, entropy, heat capacity) of self-aggregation due to perturbations. The perturbations refers to changes in initial temperature, solvent, salt, salt concentration, solvent, and/or structure of the anion or cation of more than 150 different drugs and surfactants. QSPR-Perturbation Theory models may be useful for multi-objective optimization of organic synthesis, physicochemical properties, biological activity, metabolism, and distribution profiles towards the design of new drugs, surfactants, asymmetric ligands for catalysts, and other materials.