Abstract: With tens of thousands of plant species on earth, we are endowed with an enormous wealth of medicinal remedies from Mother Nature. Natural products and their derivatives represent more than 50% of all the drugs in modern therapeutics. Because of the low success rate and huge capital investment need, the research and development of conventional drugs are very costly and difficult. Over the past few decades, researchers have focused on drug discovery from herbal medicines or botanical sources, an important group of complementary and alternative medicine (CAM) therapy. With a long history of herbal usage for the clinical management of a variety of diseases in indigenous cultures, the success rate of developing a new drug from herbal medicinal preparations should, in theory, be higher than that from chemical synthesis. While the endeavor for drug discovery from herbal medicines is "experience driven," the search for a therapeutically useful synthetic drug, like "looking for a needle in a haystack," is a daunting task. In this paper, we first illustrated various approaches of drug discovery from herbal medicines. Typical examples of successful drug discovery from botanical sources were given. In addition, problems in drug discovery from herbal medicines were described and possible solutions were proposed. The prospect of drug discovery from herbal medicines in the postgenomic era was made with the provision of future directions in this area of drug development.

Abstract: Computational prediction of interactions between drugs and their target proteins is of great importance for drug discovery and design. The difficulties of developing computational methods for the prediction of such potential interactions lie in the rarity of known drug-protein interactions and no experimentally verified negative drug-target interaction sample. Furthermore, target proteins need also to be predicted for some new drugs without any known target interaction information. In this paper, a semi-supervised learning method NetCBP is presented to address this problem by using labeled and unlabeled interaction information. Assuming coherent interactions between the drugs ranked by their relevance to a query drug, and the target proteins ranked by their relevance to the hidden target proteins of the query drug, we formulate a learning framework maximizing the rank coherence with respect to the known drug-target interactions. When applied to four classes of important drug-target interaction networks, our method improves previous methods in terms of cross-validation and some strongly predicted interactions are confirmed by the publicly accessible drug target databases, which indicates the usefulness of our method. Finally, a comprehensive prediction of drug-target interactions enables us to suggest many new potential drug-target interactions for further studies.

Abstract: Quantitative analysis of known drug-target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large--which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug-target pairs implicated in neurobiological disorders are overrepresented among de novo predictions.

Abstract: BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death and associates with multiple risk factors. Herb medicines have been used to treat CVD long ago in china and several natural products or derivatives (e.g., aspirin and reserpine) are most common drugs all over the world. The objective of this work was to construct a systematic database for drug discovery based on natural products separated from CVD-related medicinal herbs and to research on action mechanism of herb medicines. DESCRIPTION: The cardiovascular disease herbal database (CVDHD) was designed to be a comprehensive resource for virtual screening and drug discovery from natural products isolated from medicinal herbs for cardiovascular-related diseases. CVDHD comprises 35230 distinct molecules and their identification information (chemical name, CAS registry number, molecular formula, molecular weight, international chemical identifier (InChI) and SMILES), calculated molecular properties (AlogP, number of hydrogen bond acceptor and donors, etc.), docking results between all molecules and 2395 target proteins, cardiovascular-related diseases, pathways and clinical biomarkers. All 3D structures were optimized in the MMFF94 force field and can be freely accessed. CONCLUSIONS: CVDHD integrated medicinal herbs, natural products, CVD-related target proteins, docking results, diseases and clinical biomarkers. By using the methods of virtual screening and network pharmacology, CVDHD will provide a platform to streamline drug/lead discovery from natural products and explore the action mechanism of medicinal herbs. CVDHD is freely available at http://pkuxxj.pku.edu.cn/CVDHD.

Abstract: BACKGROUND: Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases. In this paper, we provide a link between ranking-based virtual screening and fragment-based data mining methods. The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings. This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods. RESULTS: In virtual screening experiments we show that our approach clearly improves enrichment factors with both applied variants of our approach: the extension of the structural similarity measure with background knowledge in the form of a hand-selected relevant substructure or the extension of the similarity measure with background knowledge derived with data mining methods. CONCLUSION: Our study shows that adding binding relevant background knowledge can lead to significantly improved similarity rankings in virtual screening and that even basic data mining approaches can lead to competitive results making hand-selection of the background knowledge less crucial. This is especially important in drug discovery and development projects where no receptor structure is available or more frequently no verified binding mode is known and mostly ligand based approaches can be applied to generate hit compounds.

Abstract: Design of multitarget drugs and polypharmacological compounds has become popular during the past decade. However, the main approach to design such compounds is to link two selective ligands via a flexible linker. Although such chimeric ligands often have reasonable potency in vitro, the in vivo efficacy is low due to high molecular weight, low ligand efficiency, and poor pharmacokinetic profile. We developed an unprecedented in silico approach for fragment-based design of multitarget ligands. It relies on superposition of the chemical spaces related to the affinity on single targets represented by self-organizing maps. We used this approach for screening of molecular fragments, which bind to the enzymes 5-lipoxygenase (5-LO) and soluble epoxide hydrolase (sEH). Using STD-NMR and activity-based assays, we were able to identify fragments binding to both targets. Furthermore, we were able to expand one of the fragments to a potent dual inhibitor bearing a reasonable molecular weight (MW = 446) and high affinity to both targets (IC50 of 0.03 muM toward 5-LO and 0.17 muM toward sEH).

Abstract: The human signal-molecule-profiling database (HSMPD) is designed as a prospective medical database for translational bioinformatics (TBI). To explore the feasibility of low-cost database construction, we studied the roadmap of HSMPD. A HSMPD-oriented tool, called "signal-molecule-profiling (SMP) chip" was developed for data acquisition, which can be employed in the routine blood tests in hospitals; the results will be stored in the HSMPD system automatically. HSMPD system can provide data services for the TBI community, which generates a stable income to support the data acquisition. The small-scale experimental test was performed in the hospital to verify SMP chips and the demo HSMPD software. One hundred and eighty nine complete SMP records were collected, and the demo HSMPD system was also evaluated in the survey study on patients and doctors. The function of SMP chip was verified, whereas the demo HSMPD software needed to be improved. The survey study showed that patients would only accept free tests of SMP chips when they originally needed blood examinations. The study indicated that the construction of HSMPD relies on the self-motivated cooperation of the TBI community and the traditional healthcare system. The proposed roadmap potentially provides an executable solution to build the HSMPD without high costs.

Abstract: Bioinformatics includes a suite of methods, which are cheap, approachable, and many of which are easily accessible without any sort of specialized bioinformatic training. Yet, despite this, bioinformatic tools are under-utilized by immunologists. Herein, we review a representative set of publicly available, easy-to-use bioinformatic tools using our own research on an under-annotated human gene, SCARA3, as an example. SCARA3 shares an evolutionary relationship with the class A scavenger receptors, but preliminary research showed that it was divergent enough that its function remained unclear. In our quest for more information about this gene - did it share gene sequence similarities to other scavenger receptors? Did it contain conserved protein domains? Where was it expressed in the human body? - we discovered the power and informative potential of publicly available bioinformatic tools designed for the novice in mind, which allowed us to hypothesize on the regulation, structure, and function of this protein. We argue that these tools are largely applicable to many facets of immunology research.

Abstract: MOTIVATION: Within Flux Balance Analysis, the investigation of complex subtasks, such as finding the optimal perturbation of the network or finding an optimal combination of drugs, often requires to set up a bilevel optimization problem. In order to keep the linearity and convexity of these nested optimization problems, an ON/OFF description of the effect of the perturbation (i.e. Boolean variable) is normally used. This restriction may not be realistic when one wants, for instance, to describe the partial inhibition of a reaction induced by a drug. RESULTS: In this paper we present a formulation of the bilevel optimization which overcomes the oversimplified ON/OFF modeling while preserving the linear nature of the problem. A case study is considered: the search of the best multi-drug treatment which modulates an objective reaction and has the minimal perturbation on the whole network. The drug inhibition is described and modulated through a convex combination of a fixed number of Boolean variables. The results obtained from the application of the algorithm to the core metabolism of E.coli highlight the possibility of finding a broader spectrum of drug combinations compared to a simple ON/OFF modeling. CONCLUSIONS: The method we have presented is capable of treating partial inhibition inside a bilevel optimization, without loosing the linearity property, and with reasonable computational performances also on large metabolic networks. The more fine-graded representation of the perturbation allows to enlarge the repertoire of synergistic combination of drugs for tasks such as selective perturbation of cellular metabolism. This may encourage the use of the approach also for other cases in which a more realistic modeling is required.

Abstract: Predicting enzymatic behavior in silico is an integral part of our efforts to understand biology. Hundreds of millions of compounds lie in targeted in silico libraries waiting for their metabolic potential to be discovered. In silico "enzymes" capable of accurately determining whether compounds can inhibit or react is often the missing piece in this endeavor. This problem has now been solved for the cytosolic sulfotransferases (SULTs). SULTs regulate the bioactivities of thousands of compounds--endogenous metabolites, drugs and other xenobiotics--by transferring the sulfuryl moiety (SO3) from 3'-phosphoadenosine 5'-phosphosulfate to the hydroxyls and primary amines of these acceptors. SULT1A1 and 2A1 catalyze the majority of sulfation that occurs during human Phase II metabolism. Here, recent insights into the structure and dynamics of SULT binding and reactivity are incorporated into in silico models of 1A1 and 2A1 that are used to identify substrates and inhibitors in a structurally diverse set of 1,455 high value compounds: the FDA-approved small molecule drugs. The SULT1A1 models predict 76 substrates. Of these, 53 were known substrates. Of the remaining 23, 21 were tested, and all were sulfated. The SULT2A1 models predict 22 substrates, 14 of which are known substrates. Of the remaining 8, 4 were tested, and all are substrates. The models proved to be 100% accurate in identifying substrates and made no false predictions at Kd thresholds of 100 muM. In total, 23 "new" drug substrates were identified, and new linkages to drug inhibitors are predicted. It now appears to be possible to accurately predict Phase II sulfonation in silico.