Computer-aided Drug Discovery Research Articles

Toward a Molecular Understanding of the Interaction of Dual Specificity Phosphatases with Substrates: Insights from Structure-Based Modeling and High Throughput Screening

Dual-specificity phosphatases (DSPs) are important, but poorly understood, cell signaling enzymes that remove phosphate groups from tyrosine and serine/threonine residues on their substrate. Deregulation of DSPs has been implicated in cancer, obesity, diabetes, inflammation, and Alzheimer’s disease. Due to their biological and biomedical significance, DSPs have increasingly become the subject of drug discovery high-throughput screening (HTS) and focused compound library development efforts. Progress in identifying selective and potent DSP inhibitors has, however, been restricted by the lack of sufficient structural data on inhibitor-bound DSPs. The shallow, almost flat, substrate binding sites in DSPs have been a major factor in hampering the rational design and the experimental development of active site inhibitors. Recent experimental and virtual HTS studies, as well as advances in molecular modeling, provide new insights into the potential mechanisms for substrate recognition and binding by this important class of enzymes. We present herein an overview of the progress, along with a brief description of applications to two types of DSPs: Cdc25 and MAP kinase phosphatase (MKP) family members. In particular, we focus on combined computational and experimental efforts for designing Cdc25B and MKP-1 inhibitors and understanding their mechanisms of interactions with their target proteins. These studies emphasize the utility of developing computational models and methods that meet the two major challenges currently faced in structure-based in silico design of lead compounds: the conformational flexibility of the target protein and the entropic contribution to the selection and stabilization of particular bound conformers.

On the applicability of GPCR homology models to computer-aided drug discovery: a comparison between in silico and crystal structures of the beta2-adrenergic receptor.

The publication of the crystal structure of the beta2-adrenergic receptor (beta2-AR) proved that G protein-coupled receptors (GPCRs) share a structurally conserved rhodopsin-like 7TM core. Here, to probe to which extent realistic GPCR structures can be recreated through modeling, carazolol was docked at two rhodopsin-based homology models of the human beta 2-AR. The first featured a rhodopsin-like second extracellular loop, which interfered with ligand docking and with the orientation of several residues in the binding pocket. The second featured a second extracellular loop built completely de novo, which afforded a more accurate model of the binding pocket and a better docking of the ligand. Furthermore, incorporating available biochemical and computational data to the model by correcting the conformation of a single residue lining the binding pocket --Phe290(6.52)--, resulted in significantly improved docking poses. These results support the applicability of GPCR modeling to the design of site-directed mutagenesis experiments and to drug discovery.

Molecular Lipophilicity in Protein Modeling and Drug Design

Hydrophobic interactions play a key role in the folding and maintenance of the 3-dimensional structure of proteins, as well as in the binding of ligands (e.g. drugs) to protein targets. Therefore, quantitative assessment of spatial hydrophobic (lipophilic) properties of these molecules is indispensable for the development of efficient computational methods in drug design. One possible solution to the problem lies in application of a concept of the 3-dimensional molecular hydrophobicity potential (MHP). The formalism of MHP utilizes a set of atomic physicochemical parameters evaluated from octanol-water partition coefficients (log P) of numerous chemical compounds. It permits detailed assessment of the hydrophobic and/or hydrophilic properties of various parts of molecules and may be useful in analysis of protein-protein and protein-ligand interactions. This review surveys recent applications of MHP-based techniques to a number of biologically relevant tasks. Among them are: (i) Detailed assessment of hydrophobic/hydrophilic organization of proteins; (ii) Application of this data to the modeling of structure, dynamics, and function of globular and membrane proteins, membrane-active peptides, etc. (iii) Employment of the MHP-based criteria in docking simulations for ligands binding to receptors. It is demonstrated that the application of the MHP-based techniques in combination with other molecular modeling tools (e.g. Monte Carlo and molecular dynamics simulations, docking, etc.) permits significant improvement to the standard computational approaches, provides additional important insights into the intimate molecular mechanisms driving protein assembling in water and in biological membranes, and helps in the computer-aided drug discovery process.

Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach

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. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure–activity and quantitative structure–property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.

Progress in Computational Approach to Drug Development Against SARS

Since the outbreak of SARS (severe acute respiratory syndrome) in November 2002 in Southern China's Guangdong Province, considerable progress has been made in the development of drugs for SARS therapy. The present mini review is focused on the area of computer-aided drug discovery, i.e., the advances achieved mainly from the approaches of structural bioinformatics, pharmacophore modeling, molecular docking, peptide-cleavage site prediction, and other computational means. It is highlighted that the compounds C(28)H(34)O(4)N(7)Cl, C(21)H(36(O)5)N(6) and C(21)H(36)O(5)N(6) (Wei et al., Amino Acids, 2006, 31: 73-80), as well as KZ7088 (Chou et al. Biochem. Biophys. Res. Commun., 2003, 308: 148-151), a derivative of AG7088, might be the promising candidates for further investigation, and that the octapeptides ATLQAIAS and ATLQAENV, as well as AVLQSGFR, might be converted to effective inhibitors against the SARS enzyme. Meanwhile, how to modify these octapeptides based on the "distorted key" theory to make them become potent inhibitors is explicitly elucidated. Finally, a brief introduction is given for how to use computer-generated graphs to rapidly diagnose SARS coronavirus.

The Challenge of Predicting Drug Toxicity in silico

Poor pharmacokinetics, side effects and compound toxicity are frequent causes of late-stage failures in drug development. A safe in silico identification of adverse effects triggered by drugs and chemicals would be highly desirable as it not only bears economical potential but also spawns a variety of ecological benefits: sustainable resource management, reduction of animal models and possibly less risky clinical trials. In computer-aided drug discovery, both existing and hypothetical compounds may be studied; the methods are fast, reproducible, and typically based on human bioregulators, making the question of transferability obsolete. In the recent past, our laboratory contributed towards the development of in silico concepts (--> multi-dimensional QSAR) and validated a series of "virtual test kits" based on the oestrogen, androgen, thyroid, and aryl hydrocarbon receptor (endocrine disruption, receptor-mediated toxicity) as well as on the enzyme cytochrome P450 3A4 (metabolic transformations, drug-drug interactions). The test kits are based on the three-dimensional structure of their target protein (i.e. ER(alphabeta), AR, TR(alphabeta), CYP450) or a surrogate thereof (AhR) and were trained using a representative selection of 362 substances. Subsequent evaluation of 107 compounds different therefrom showed that binding affinities are predicted close to experimental uncertainty. These results suggest that our approach is suited for the in silico identification of adverse effects triggered by drugs and chemicals and encouraged us to compile an Internet Database for the virtual screening of drugs and chemicals for toxic effects.

Geometric modeling applications in rational drug design: a survey

Computer aided design methods are starting to gain acceptance in the field of life sciences and drug discovery, thus giving rise to a discipline coined Computer Aided Drug Discovery (CADD). This trend is accompanied by a larger interest in 3-dimensional methods. Traditionally for many applications chemical entities such as proteins and molecules have been regarded as either 2D graphs or sequences of letters (proteins and genome). This survey article will give an overview and historical perspective of the state of the art of geometric modeling as it relates to drug discovery.

Application of Validated QSAR Models of D1Dopaminergic Antagonists for Database Mining

Rigorously validated quantitative structure-activity relationship (QSAR) models have been developed for 48 antagonists of the dopamine D1 receptor and applied to mining chemical datasets to discover novel potential antagonists. Several QSAR methods have been employed, including comparative molecular field analysis (CoMFA), simulated annealing-partial least squares (SA-PLS), k-nearest neighbor (kNN), and support vector machines (SVM). With the exception of CoMFA, these approaches employed 2D topological descriptors generated with the MolConnZ software package (EduSoft, LLC. MolconnZ, version 4.05; http://www.eslc.vabiotech.com/ [4.05], 2003). The original dataset was split into training and test sets to allow for external validation of each training set model. The resulting models were characterized by cross-validated R2 (q2) for the training set and predictive R2 values for the test set of (q2/R2) 0.51/0.47 for CoMFA, 0.7/0.76 for kNN, R2 for the training and test sets of 0.74/0.71 for SVM, and training set fitness and test set R2 values of 0.68/0.63 for SA-PLS. Validated QSAR models with R2 > 0.7, (i.e., kNN and SVM) were used to mine three publicly available chemical databases: the National Cancer Institute (NCI) database of ca. 250,000 compounds, the Maybridge Database of ca. 56,000 compounds, and the ChemDiv Database of ca. 450,000 compounds. These searches resulted in only 54 consensus hits (i.e., predicted active by all models); five of them were previously characterized as dopamine D1 ligands, but were not present in the original dataset. A small fraction of the purported D1 ligands did not contain a catechol ring found in all known dopamine full agonist ligands, suggesting that they may be novel structural antagonist leads. This study illustrates that the combined application of predictive QSAR modeling and database mining may provide an important avenue for rational computer-aided drug discovery.

Discovering COX-Inhibiting Constituents ofMorusRoot Bark: Activity-Guided versus Computer-Aided Methods

The aim of this study was to compare the efficiency of two well known approaches for the discovery of the bioactive principle/s in medicinal plants, namely the activity-guided isolation versus the computer-aided drug discovery by means of virtual screening (VS) techniques. Morus root bark of Morus sp. L. (Moraceae) was selected as application example for the discovery of compounds with anti-inflammatory activity. The two cyclooxygenase isoenzymes COX-1 and COX-2 were chosen as targets and the corresponding pharmacophore models were generated by our research. The activity-guided fractionation of the methanol extract of the root bark resulted in the isolation of nine compounds. Their structures were elucidated by mass spectrometry, 1- and 2-dimensional NMR experiments and identified as moracins B, M, the regioisomers O/P as a mixture, and sanggenons B, C, D, E and O. The COX-1 and COX-2 inhibiting activities of these compounds were established in an enzyme assay and compared with the predicted hits obtained from the VS. Sanggenons C, E, and O, that were tested the first time for an inhibitory effect on COX-1 and -2, showed IC50 values of 10-14 microM, and 40-50 microM, respectively. The results show that the COX activities obtained for the sanggenons are correctly predicted by the in silico filtering experiment. In the case of the isolated moracins, however, it failed because the COX inhibiting activities of moracins M and P/O were not retrieved by the VS. Structure-activity relationships of the isolated compounds are discussed as well as potential pitfalls and advantages of the applied strategies.

Computational Techniques for Diversity Analysis and Compound Classification

Molecular similarity and diversity analysis has played a significant role in computer-aided drug discovery for more than a decade. Compound classification methods have also become increasingly important for the design and organization of compound databases and in silico screening. Here we review these related methodologies and discuss selected applications.

Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example.

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.

Why are dengue virus serotypes so distantly related? Enhancement and limiting serotype similarity between dengue virus strains.

Dengue virus, the causative agent of dengue fever, has four major serotypes characterized by large genetic and immunological distances. We propose that the unusually large distances between the serotypes can be explained in the light of a process of antibody-dependent enhancement (ADE) leading to increased mortality. Antibody-dependent enhancement results from a new infection with a particular serotype in an individual with acquired immunity to a different serotype. Classical dengue fever causes negligible mortality, but ADE leads to the risk of developing the significantly more dangerous dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS). A mathematical model is presented that describes the epidemiological dynamics of two serotypes of a pathogen where there is the possibility of co-infection and reinfection by a different serotype, along with increased mortality as a result of enhancement. We show that if there is no or slightly increased mortality after reinfection (enhancement), serotypes with a small immunological distance can stably coexist. This suggests that a cloud of serotypes with minor serological differences will constitute the viral population. By contrast, if enhancement is sufficiently great, a substantial immunological distance is necessary for two serotypes to stably coexist in the population. Therefore, high mortality owing to enhancement leads to an evolutionarily stable viral community comprising a set of distantly separated serotypes.

Computer Aided Drug Design: Some Fundamental Aspects

Understanding the molecular basis of drug action and exploring the chemical interactions involved in the complex processes of drug delivery are among the most important goals of contemporary drug design. The major recent advances in the detailed, mechanistic interpretation of molecular interactions, the global and local shape analysis of electron density clouds making up the actual fuzzy bodies of molecules, novel similarity and complementary measures, the detailed and accurate computational visualization techniques of molecular level "Computational Microscopy", the advances in computer modeling of conformational processes and chemical reactions of drug molecules, the computer aided design of molecular templates fitting various receptor sites are among the powerful tools of computer aided drug discovery. In this contribution some of the latest advances are reviewed.

Holographic electron density shape theorem and its role in drug design and toxicological risk assessment.

Each complete, boundaryless molecular electron density is fully determined by any nonzero volume piece of the electron density cloud. This inherent feature of molecules, called the "holographic" property of molecular electron densities, provides a strong foundation for the local, quantum chemical shape analysis of various functional groups, pharmacophores, and other local molecular moieties. A proof is presented for the relevant molecular shape theorem, the "holographic electron density shape theorem", and the role of this theorem in quantum chemical, quantitative shape-activity relations (QShAR) is discussed. The quantum chemical methods of molecular shape analysis can be extended to ab initio quality electron densities of macromolecules, such as proteins, as well as to local molecular moieties, such as functional groups or pharmacophores, based on the transferability and additivity of local, fuzzy density fragments and the associated local density matrixes within the framework of the ADMA (Adjustable Density Matrix Assembler) approach. In addition to new results on chemical bonding and the development of macromolecular force methods, the new methodologies are also applicable to QShAR studies in computer-aided drug discovery and in toxicological risk assessment.

Computer-aided drug discovery and design

Computer-aided drug discovery and design