Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification.

Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines

Similarity-based virtual screening using 2D fingerprints

This paper summarises recent work at the University of Sheffield on virtual screening methods that use 2D fingerprint measures of structural similarity. A detailed comparison of a large number of similarity coefficients demonstrates that the well-known Tanimoto coefficient remains the method of choice for the computation of fingerprint-based similarity, despite possessing some inherent biases related to the sizes of the molecules that are being sought. Group fusion involves combining the results of similarity searches based on multiple reference structures and a single similarity measure. We demonstrate the effectiveness of this approach to screening, and also describe an approximate form of group fusion, turbo similarity searching, that can be used when just a single reference structure is available

Virtual Screening, Molecular Docking and QSAR Studies in Drug Discovery and Development Programme

Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two basic approaches of computer-aided drug design (CADD) used in modern drug discovery and development programme. Virtual screening (or in silico screening) has been used in drug discovery program as a complementary tool to high throughput screening (HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained considerable interest in the pharmaceutical research as a productive and cost-effective technology in search for novel molecules of medicinal interest. Docking is also used for virtual screening of new ligands on the basis of biological structures for identification of hits and generation of leads or optimization (potency/ property) of leads in drug discovery program. Hence, docking is approach of SBDD which plays an important role in rational designing of new drug molecules. Quantitative structure-activity relationship (QSAR) is an important chemometric tool in computational drug design. It is a common practice of LBDD. The study of QSAR gives information related to structural features and/or physicochemical properties of structurally similar molecules to their biological activity. In this paper, a comprehensive review on several computational tools of SBDD and LBDD such as virtual screening, molecular docking and QSAR methods of and their applications in the drug discovery and development programme have been summarized. Keywords: Virtual screening, Molecular docking, QSAR, Drug discovery, Lead molecul

Enhancing the effectiveness of ligand-based virtual screening using data fusion

Data fusion is being increasingly used to combine the outputs of different types of sensor. This paper reviews the application of the approach to ligand-based virtual screening, where the sensors to be combined are functions that score molecules in a database on their likelihood of exhibiting some required biological activity. Much of the literature to date involves the combination of multiple similarity searches, although there is also increasing interest in the combination of multiple machine learning techniques. Both approaches are reviewed here, focusing on the extent to which fusion can improve the effectiveness of searching when compared with a single screening mechanism, and on the reasons that have been suggested for the observed performance enhancement

Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe

Frontiers in Computational Chemistry for Drug Discovery

Computational methods pervade almost all aspects of drug discovery [1-3]. Computer-assisted tools contribute to the decision-making process along the entire drug discovery pipeline, including the validation of suitable targets, high-throughput screening of molecular libraries, the optimization of lead compounds, and the balance between pharmacological potency and physico-chemical and pharmacokinetic properties. This tendency will be reinforced in the next few years due to the continued increases in computer power, and the elaboration of sophisticated algorithms to capture the physico-chemical principles that underlie the activity of drugs. This effort should enable drug discovery methodology to evolve from approximate to more rigorous methods. How should computational methods evolve to ameliorate the success of drug discovery? The answer to this question is related to the identification of the current limitations faced by computational algorithms to unveil the delicate balance between factors that determine both potency and ADMET (absorption, distribution, metabolism, excretion, and toxicology) properties of drug candidates

IN SILICO DOCKING ANALYSIS OF BIOACTIVE COMPOUNDS FROM CALOPHYLLUM INOPHYLLUM L. ETHANOL LEAF EXTRACT AGAINST EGFR PROTEIN

Objective: The objective of this study was to evaluate the effective new phytocomponents from Calophyllum inophyllum ethanol leaf extract against breast cancer target protein of Epidermal Growth Factor Receptor (EGFR) using in silico docking studies.Materials and Methods: The identification of compounds was done by GC-MS analysis. The in silico docking studies were carried out using Discovery Studio 4.0 software.Results: The GC-MS analysis of ethanol leaf extract revealed the presence of eleven compounds. The docking analysis have exhibited moderate to potent inhibition with a range of dock score 3 to 55. 2H-Benzo(cd) pyrene-2,6(1,H)-dione, 3,5,7,10-tetrahydroxy-compound showed the dock score of 55.427.Conclusion: The results revealed out that the compounds present in Calophyllum inophyllum can inhibit the EGFR protein. The plant possesses anticancer potential because of the various bioactive compounds presence which is mainly responsible for anticancer activity.Â

In silico evaluation of the antimicrobial potentials of soluble bioactive compounds derived from Weissella ciberia metabolites.

Weissella species are a group of lactic acid bacteria gaining rapid popularity as a result of discoveries centered on their biotechnological properties. In this study, an in silico approach was imbibed to investigate the antimicrobial potentials of metabolites of Weissella ciberia. Soluble compounds of W. ciberia were subjected to High Performance Liquid Chromatographic (HPLC) analysis and the inherent metabolites were identified. In order to evaluate their antimicrobial potentials against Escherichia coli and Shigella flexneri, the identified metabolites of W. ciberia were further subjected to geometry optimization of compound structures, ligand/receptor preparation, docking calculations and docking simulations. The HPLC identified metabolites from W. ciberia were atropoine, gallic acid, naringinin, caffeine, maleic acid, saponin and glutathione. The results of the in silico analysis showed binding affinities of the metabolites against the target microorganisms at a range of ˗4.6 to 10.7 Kcal/mol. Among metabolites, the highest binding affinity was observed in saponin against E. coli and S. flexneri at scores of ˗9.7 Kcal/mol and ˗10 Kcal/mol respectively. Binding affinities against E. coli and S. flexneri were also observed in naringinin at binding scores of ˗7.8 Kcal/mol and ˗8.5 Kcal/mol respectively. The scores obtained in this study predicts strong antimicrobial potentials that were comparable to those of conventional antibiotics such as ciprofloxacin and meropenem. Hence, the antimicrobial activities of metabolites of W. ciberia could be harnessed further for their potential in drug sensitivity against multiple-drug resistant pathogenic microbes. KEYWORDS: Bioactive, metabolites, fermentate, in silico and binding affinity. DOI: 10.7176/JNSR/14-12-06 Publication date:October 30th 202