应用化学基因组信息预测小分子化合物的潜在生物靶标的理论方法

在后基因组时代,化学基因组技术在药物作用靶点的确认、小分子化合物对通路的作用,以及小分子先导化合物的识别等方面都有着广泛的应用,为新药研发提供了新的技术方法。本文主要介绍了当前几种基于化学基因组信息来预测小分子化合物潜在生物靶标的理论方法（包括化学相似性搜索方法、反向分子对接方法、数据挖掘方法以及生物活性谱图分析方法）,并分析了这些方法的优缺点以及应用前景。     

1-Substituted carbamoyl and thiocarbamoyl-4,5-dihydro-1H-pyrazoles as possible cytotoxic and antimicrobial agents

Two series of 1-substituted carbamoyl and thiocarbomoyl derivatives were prepared by either treating the corresponding pyrazole with the appropriate isocyanate and isothiocyanate respectively, or alternatively by condensing the appropriate diketone with the proper substituted semicarbazide or thiosemicarbazide. The structures of the prepared compounds were fully determined by analytical and spectral methods. Preliminary biological screening of the prepared compounds revealed significant antibacterial and cytotoxic activities for some compounds. Compounds 4a₂ and 4a₃ were found to be the most active against the human colon carcinoma HT29 (11.8 and 7.5?μg/mL, respectively) and human breast cancer MCF 7 (3.4 and 2.6?μg/mL, respectively) cell lines. The structure–activity relationship (SAR) and in silico drug relevant properties (HBD, HBA, tPSA, cLog P, molecular weight, % ABS, drug-likeness and drug score) further confirmed that the compounds are potential lead compounds for future drug discovery study.     

Structure based design of heat shock protein 90 inhibitors acting as anticancer agents

Structure based drug design (SBDD) was used to discover heat shock protein 90 (HSP90) inhibitors useful in the treatment of cancer. By using the crystal structure of HSP90-ligand complex (1uyi), a docking model was prepared and was validated by external dataset containing known HSP90 inhibitors. This validated model was then used to virtually screen commercial databases, selected hits of which were bought and sent for real biological evaluation. Further as an alternative method, pharmacophores were generated using crystal structure conformations of ligands in HSP90 complexes (1uyi and 2bz5) and where used for virtual screening. Both cases yielded several hits containing novel scaffolds, particularly compound KHSP8 showed an IC(50) value of 0.902 μM in case of colon cancer (HT29), which is comparable to doxorubicin (0.828 μM). These compounds were being now used as leads for constructing small molecular libraries to get compounds with favourable pharmacokinetics and drug like properties.     

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.     

Quantification of free ligand conformational preferences by NMR and their relationship to the bioactive conformation

Accurate unbound solution 3D-structures of ligands provide unique opportunities for medicinal chemistry and, in particular, a context to understand binding thermodynamics and kinetics. Previous methods of deriving these 3D-structures have had neither the accuracy nor resolution needed for drug design and have not yet realized their potential. Here, we describe and apply a NMR methodology to the aminoglycoside streptomycin that can accurately quantify accessible 3D-space and rank the occupancy of observed conformers to a resolution that enables medicinal chemistry understanding and design. Importantly, it is based upon conventional small molecule NMR techniques and can be performed in physiologically-relevant solvents. The methodology uses multiple datasets, an order of magnitude more experimental data than previous NMR approaches and a dynamic model during refinement, is independent of computational chemistry and avoids the problem of virtual conformations. The refined set of solution 3D-shapes for streptomycin can be grouped into two major families, of which the most populated is almost identical to the 30S ribosomal subunit bioactive shape. We therefore propose that accurate unbound ligand solution conformations may, in some cases, provide a subsidiary route to bioactive shape without crystallography. This experimental technique opens up new opportunities for drug design and more so when complemented with protein co-crystal structures, SAR data and pharmacophore modeling.     

Chemical genomics: a systematic approach in biological research and drug discovery

The knowledge of complete sequences of different organisms is dramatically changing the landscape of biological research and pharmaceutical development. We are experiencing a transition from a trial-and-error approach in traditional biological research and natural product drug discovery to a systematic operation in genomics and target-specific drug design and selection. Small, cell-permeable and target-specific chemical ligands are particularly useful in systematic genomic approaches to study biological questions. On the other hand, genomic sequence information, comparative and structural genomics, when combined with the cutting edge technologies in synthetic chemistry and ligand screening/identification, provide a powerful way to produce target-specific and/or function-specific chemical ligands and drugs. Chemical genomics or chemogenomics is a new term that describes the development of target-specific chemical ligands and the use of such chemical ligands to globally study gene and protein functions. We anticipate that chemical genomics plays a critical role in the genomic age of biological research and drug discovery.     

Chemotography for multi-target SAR analysis in the context of biological pathways

The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists.     

Chance favors the prepared mind--from serendipity to rational drug design

Accidental discoveries always played an important role in science, especially in the search for new drugs. Several examples of serendipitous findings, leading to therapeutically useful drugs, are presented and discussed. Captopril, an antihypertensive Angiotensin-converting enzyme inhibitor, was the first drug that could be derived from a structural model of a protein. Dorzolamide, a Carboanhydrase inhibitor for the treatment of glaucoma, and the HIV protease inhibitors Saquinavir, Indinavir, Ritonavir, and Nelfinavir are further examples of therapeutically used drugs from structure-based design. More enzyme inhibitors, e.g. the anti-influenza drugs Zanamivir and GS 4104, are in clinical development. In the absence of a protein 3D structure, the 3D structures of certain ligands may be used for rational design. This approach is exemplified by the design of specifically acting integrin receptor antagonists. In the last years, combinatorial and computational approaches became important methods for rational drug design. SAR by NMR searches for low-affinity ligands that bind to proximal subsites of an enzyme; linkage with an appropriate tether produces nanomolar inhibitors. The de novo design program LUDI and the docking program FlexX are tools for the computer-aided design of protein ligands. Work is in progress to combine such approaches to strategies for combinatorial drug design.     

Advances and applications of binding affinity prediction methods in drug discovery

Nowadays, the improvement of R&D productivity is the primary commitment in pharmaceutical research, both in big pharma and smaller biotech companies. To reduce costs, to speed up the discovery process and to increase the chance of success, advanced methods of rational drug design are very helpful, as demonstrated by several successful applications. Among these, computational methods able to predict the binding affinity of small molecules to specific biological targets are of special interest because they can accelerate the discovery of new hit compounds. Here we provide an overview of the most widely used methods in the field of binding affinity prediction, as well as of our own work in developing BEAR, an innovative methodology specifically devised to overtake some limitations in existing approaches. The BEAR method was successfully validated against different biological targets, and proved its efficacy in retrieving active compounds from virtual screening campaigns. The results obtained so far indicate that BEAR may become a leading tool in the drug discovery pipeline. We primarily discuss advantages and drawbacks of each technique and show relevant examples and applications in drug discovery.     

Rational design,synthesis, pharmacophore modeling,and docking studies for identification of novel potent DNA-PK inhibitors

Drugs of cancer based upon ionizing radiation or chemotherapeutic treatment may affect breaking of DNA double strand in cell. DNA-PK enzyme has emerged as an attractive target for drug discovery efforts toward DNA repair pathways. Hence, the search for potent and selective DNA-PK inhibitors has particularly considered state-of-the art and several series of inhibitors have been designed. In this article, a novel benchmark DNA-PK database of 43 compounds was built and described. Ligand-based approaches including pharmacophore and QSAR modeling were applied and novel models were introduced and analyzed for predicting activity test for DNA-PK drug candidates. Based upon the modeling results, we gave a report of synthesis of fifteen novel 2-((8-methyl-2-morpholino-4-oxo-4H-benzo[e][1,3]oxazin-7-yl)oxy)acetamide derivatives and in vitro evaluation for DNA-PK inhibitory and antiproliferative activities. These fifteen compounds overall are satisfied with Lipinski's rule of five. The biological testing of target compounds showed five promising active compounds 7c, 7d, 7f, 9e and 9f with micromolar DNA-PK activity range from 0.25 to 5 µM. In addition, SAR of the compounds activity was investigated and confirmed that the terminal aryl moiety was found to be quite crucial for DNA-PK activity. Moreover flexible docking simulation was done for the potent compounds into the putative binding site of the 3D homology model of DNA-PK enzyme and the probable interaction model between DNA-PK and the ligands was investigated and interpreted.     

IspE inhibitors identified by a combination of in silico and in vitro high-throughput screening

CDP-ME kinase (IspE) contributes to the non-mevalonate or deoxy-xylulose phosphate (DOXP) pathway for isoprenoid precursor biosynthesis found in many species of bacteria and apicomplexan parasites. IspE has been shown to be essential by genetic methods and since it is absent from humans it constitutes a promising target for antimicrobial drug development. Using in silico screening directed against the substrate binding site and in vitro high-throughput screening directed against both, the substrate and co-factor binding sites, non-substrate-like IspE inhibitors have been discovered and structure-activity relationships were derived. The best inhibitors in each series have high ligand efficiencies and favourable physico-chemical properties rendering them promising starting points for drug discovery. Putative binding modes of the ligands were suggested which are consistent with established structure-activity relationships. The applied screening methods were complementary in discovering hit compounds, and a comparison of both approaches highlights their strengths and weaknesses. It is noteworthy that compounds identified by virtual screening methods provided the controls for the biochemical screens.     

Chemical biology of dynamic combinatorial libraries

Dynamic combinatorial chemistry (DCC) is a recently introduced supramolecular approach to generate libraries of chemical compounds based on reversible exchange processes. The building elements are spontaneously and reversibly assembled to virtually encompass all possible combinations, allowing for simple one-step generation of complex libraries. The method has been applied to a variety of combinatorial systems, ranging from synthetic models to materials science and drug discovery, and enables the establishment of adaptive processes due to the dynamic interchange of the library constituents and its evolution toward the best fit to the target. In particular, it has the potential to become a useful tool in the direct screening of ligands to a chosen receptor without extensive prior knowledge of the site structure, and several biological systems have been targeted. In the vast field of glycoscience, the concept may find special perspective in response to the highly complex nature of carbohydrate-protein interactions. This chapter summarises studies that have been performed using DCC in biological systems, with special emphasis on glycoscience.     

Large-scale integrated super-computing platform for next generation virtual drug discovery

Traditional drug discovery starts by experimentally screening chemical libraries to find hit compounds that bind to protein targets, modulating their activity. Subsequent rounds of iterative chemical derivitization and rescreening are conducted to enhance the potency, selectivity, and pharmacological properties of hit compounds. Although computational docking of ligands to targets has been used to augment the empirical discovery process, its historical effectiveness has been limited because of the poor correlation of ligand dock scores and experimentally determined binding constants. Recent progress in super-computing, coupled to theoretical insights, allows the calculation of the Gibbs free energy, and therefore accurate binding constants, for usually large ligand-receptor systems. This advance extends the potential of virtual drug discovery. A specific embodiment of the technology, integrating de novo, abstract fragment based drug design, sophisticated molecular simulation, and the ability to calculate thermodynamic binding constants with unprecedented accuracy, are discussed.     

Naturally occurring plant isoquinoline N-oxide alkaloids: Their pharmacological and SAR activities

The present review describes research on novel natural isoquinoline alkaloids and their N-oxides isolated from different plant species. More than 200 biological active compounds have shown confirmed antimicrobial, antibacterial, antitumor, and other activities. The structures, origins, and reported biological activities of a selection of isoquinoline N-oxides alkaloids are reviewed. With the computer program PASS some additional SAR (structure–activity relationship) activities are also predicted, which point toward new possible applications of these compounds. This review emphasizes the role of isoquinoline N-oxides alkaloids as an important source of leads for drug discovery.     

Bioactive conformational generation of small molecules: A comparative analysis between force-field and multiple empirical criteria based methods

Background  

Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.     

Bioactivity landscape modeling: Chemoinformatic characterization of structure-activity relationships of compounds tested across multiple targets

Characterizing structure-activity relationships (SAR) of sets of compounds screened across different targets is crucial in several drug discovery endeavors. To this end, chemoinformatic approaches are emerging to characterize SARs using the concept of multi-target activity landscapes. Herein, we present the Structure multiple Activity Similarity (SmAS) maps and the Structure multiple Activity Landscape Index (SmALI) as general approaches to navigate through and quantify the most informative regions of multi-target activity landscapes. These methods are extensions of SAS maps and SALI metric used for single targets. To illustrate the use of these methods, SmAS maps and SmALI values were employed for characterizing the SAR of three benchmark sets of compounds screened with different target families. As a follow up of our work, we employed four 2D and 3D structure representations to obtain consensus models for each data set. For the three data sets, we identified pairs of compounds with high structure similarity but very different bioactivity profile across the corresponding targets of each family that is, multi-target activity cliffs. Also, we identified pairs of compounds with low structure similarity but similar bioactivity profile across the different targets that is, multi-target scaffold hops. The consensus SmAS maps and mean SmALI metric are complementary chemoinformatic tools to systematically describe multi-target activity landscapes.     

Physico-chemical and biological analysis of true combinatorial libraries

Combinatorial libraries offer new sources of compounds for the research of pharmacological agents such as receptor ligands, enzyme inhibitors or substrates and antibody-binding epitopes. The present review stresses the main roles played by both physico-chemical analysis, particularly when complex mixture of compounds are synthesized as libraries, and biological analysis from which active compounds are identified. After a brief discussion of semantic problems related to the designation of the product mixtures, the physico-chemical analysis of mixtures is reviewed with special emphasis on mass spectrometric techniques. These methods are able both to give a representative view of a library composition and to identify single critical compounds in large libraries. Then the biological screening of such combinatorial libraries is critically discussed with respect to the power and limitations of the methods used for the identification of the active components. Special attention is given to the complex process of library deconvolution. It is pointed out that while combinatorial techniques have evolved towards sophisticated high-tech methods, simple and robust biochemical tests should be used to deconvolute. From a large panel of published examples, a set of trends are identified which should help investigators to choose the most appropriate assay for the discovery of new entities.     

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

Conformational diversity of ligands bound to proteins

The phenomenon of molecular recognition, which underpins almost all biological processes, is dynamic, complex and subtle. Establishing an interaction between a pair of molecules involves mutual structural rearrangements guided by a highly convoluted energy landscape, the accurate mapping of which continues to elude us. Increased understanding of the degree to which the conformational space of a ligand is restricted upon binding may have important implications for docking studies, structure refinement and for function prediction methods based on geometrical comparisons of ligands or their binding sites. Here, we present an analysis of the conformational variability exhibited by three of the most ubiquitous biological ligands in nature, ATP, NAD and FAD. First, we demonstrate qualitatively that these ligands bind to proteins in widely varying conformations, including several cases in which parts of the molecule assume energetically unfavourable orientations. Next, by comparing the distribution of bound ligand shapes with the set of all possible molecular conformations, we provide a quantitative assessment of previous observations that ligands tend to unfold when binding to proteins. We show that, while extended forms of ligands are indeed common in ligand-protein structures, instances of ligands in almost maximally compact arrangements can also be found. Thirdly, we compare the conformational variation in two sets of ligand molecules, those bound to homologous proteins, and those bound to unrelated proteins. Although most superfamilies bind ligands in a fairly conserved manner, we find several cases in which significant variation in ligand configuration is observed.