Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery.

Aggregation has been posing a great challenge in drug discovery. Current computational approaches aiming to filter out aggregated molecules based on their similarity to known aggregators, such as Aggregator Advisor, have low prediction accuracy, and therefore development of reliable in silico models to detect aggregators is highly desirable. In this study, we built a data set consisting of 12 119 aggregators and 24 172 drugs or drug candidates and then developed a group of classification models based on the combination of two ensemble learning approaches and five types of molecular representations. The best model yielded an accuracy of 0.950 and an area under the curve (AUC) value of 0.987 for the training set, and an accuracy of 0.937 and an AUC of 0.976 for the test set. The best model also gave reliable predictions to the external validation set with 5681 aggregators since 80% of molecules were predicted to be aggregators with a prediction probability higher than 0.9. More importantly, we explored the relationship between colloidal aggregation and molecular features, and generalized a set of simple rules to detect aggregators. Molecular features, such as log D, the number of hydroxyl groups, the number of aromatic carbons attached to a hydrogen atom, and the number of sulfur atoms in aromatic heterocycles, would be helpful to distinguish aggregators from nonaggregators. A comparison with numerous existing druglikeness and aggregation filtering rules and models used in virtual screening verified the high reliability of the model and rules proposed in this study. We also used the model to screen several curated chemical databases, and almost 20% of molecules in the evaluated databases were predicted as aggregators, highlighting the potential high risk of aggregation in screening. Finally, we developed an online Web server of ChemAGG ( http://admet.scbdd.com/ChemAGG/index ), which offers a freely available tool to detect aggregators.

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences.

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine.

Traditional Chinese Medicine (TCM) has been widely used in the treatment of various diseases for millennia. In the modernization process of TCM, TCM ingredient databases are playing more and more important roles. However, most of the existing TCM ingredient databases do not provide simplification function for extracting key ingredients in each herb or formula, which hinders the research on the mechanism of actions of the ingredients in TCM databases. The lack of quality control and standardization of the data in most of these existing databases is also a prominent disadvantage. Therefore, we developed a Traditional Chinese Medicine Simplified Integrated Database (TCMSID) with high storage, high quality and standardization. The database includes 499 herbs registered in the Chinese pharmacopeia with 20,015 ingredients, 3270 targets as well as corresponding detailed information. TCMSID is not only a database of herbal ingredients, but also a TCM simplification platform. Key ingredients from TCM herbs are available to be screened out and regarded as representatives to explore the mechanism of TCM herbs by implementing multi-tool target prediction and multilevel network construction. TCMSID provides abundant data sources and analysis platforms for TCM simplification and drug discovery, which is expected to promote modernization and internationalization of TCM and enhance its international status in the future. TCMSID is freely available at https://tcm.scbdd.com .

A combinatorial target screening strategy for deorphaning macromolecular targets of natural product.

Natural products, as an ideal starting point for molecular design, play a pivotal role in drug discovery; however, ambiguous targets and mechanisms have limited their in-depth research and applications in a global dimension. In-silico target prediction methods have become an alternative to target identification experiments due to the high accuracy and speed, but most studies only use a single prediction method, which may reduce the accuracy and reliability of the prediction. Here, we firstly presented a combinatorial target screening strategy to facilitate multi-target screening of natural products considering the characteristics of diverse in-silico target prediction methods, which consists of ligand-based online approaches, consensus SAR modelling and target-specific re-scoring function modelling. To validate the practicability of the strategy, natural product neferine, a bisbenzylisoquinoline alkaloid isolated from the lotus seed, was taken as an example to illustrate the screening process and a series of corresponding experiments were implemented to explore the pharmacological mechanisms of neferine. The proposed computational method could be used for a complementary hypothesis generation and rapid analysis of potential targets of natural products.

The ups and downs of Poly(ADP-ribose) Polymerase-1 inhibitors in cancer therapy-Current progress and future direction.

Poly(ADP-ribose) Polymerase 1 (PARP1), one of the most investigated 18 membered PARP family enzymes, is involved in a variety of cellular functions including DNA damage repair, gene transcription and cell apoptosis. PARP1 can form a PARP1(ADP-ribose) polymers, then bind to the DNA damage gap to recruit DNA repair proteins, and repair the break to maintain genomic stability. PARP1 is highly expressed in tumor cells, so the inhibition of PARP1 can block DNA repair, promote tumor cell apoptosis, and exert antitumor activity. To date, four PARP1 inhibitors namely olaparib, rucaparib, niraparib and talazoparib, have been approved by Food and Drug Administration (FDA) for treating ovarian cancer and breast cancer with BRCA1/2 mutation. These drugs have showed super advantages over conventional chemotherapeutic drugs with low hematological toxicity and slowly developed drug resistance. In this article, we summarize and analyze the structure features of PARP1, the biological functions and antitumor mechanisms of PARP1 inhibitors. Importantly, we suggest that establishing a new structure-activity relationship of developed PARP1 inhibitors via substructural searching and the matched molecular pair analysis would accelerate the process in finding more potent and safer PARP1 inhibitors.

A multi-scale systems pharmacology approach uncovers the anti-cancer molecular mechanism of Ixabepilone.

It has been realized that FDA approved drugs may have more molecular targets than is commonly thought. Thus, to find the exact drug-target interactions (DTIs) is of great significance for exploring the new molecular mechanism of drugs. Here, we developed a multi-scale system pharmacology (MSSP) method for the large-scale prediction of DTIs. We used MSSP to integrate drug-related and target-related data from multiple levels, the network structural data formed by known drug-target relationships for predicting likely unknown DTIs. Prediction results revealed that Ixabepilone, an epothilone B analog for treating breast cancer patients, may target Bcl-2, an oncogene that contributes to tumor progression and therapy resistance by inhibiting apoptosis. Furthermore, we demonstrated that Ixabepilone could bind with Bcl-2 and decrease its protein expression in breast cancer cells. The down-regulation of Bcl-2 by Ixabepilone is resulted from promoting its degradation by affecting p-Bcl-2. We further found that Ixabepilone could induce autophagy by releasing Beclin1 from Beclin1/Bcl-2 complex. Inhibition of autophagy by knockdown of Beclin1 or pharmacological inhibitor augmented apoptosis, thus enhancing the antitumor efficacy of Ixabepilone against breast cancer cells in vitro and in vivo. In addition, Ixabepilone also decreases Bcl-2 protein expression and induces cytoprotective autophagy in human hepatic carcinoma and glioma cells. In conclusion, this study not only provides a feasible and alternative way exploring new molecular mechanisms of drugs by combing computation DTI prediction, but also reveals an effective strategy to reinforce the antitumor efficacy of Ixabepilone.

Leverage principle of retardation signal in titration of double protein via chip moving reaction boundary electrophoresis.

In the present work we address a simple, rapid and quantitative analytical method for detection of different proteins present in biological samples. For this, we proposed the model of titration of double protein (TDP) and its relevant leverage theory relied on the retardation signal of chip moving reaction boundary electrophoresis (MRBE). The leverage principle showed that the product of the first protein content and its absolute retardation signal is equal to that of the second protein content and its absolute one. To manifest the model, we achieved theoretical self-evidence for the demonstration of the leverage principle at first. Then relevant experiments were conducted on the TDP-MRBE chip. The results revealed that (i) there was a leverage principle of retardation signal within the TDP of two pure proteins, and (ii) a lever also existed within these two complex protein samples, evidently demonstrating the validity of TDP model and leverage theory in MRBE chip. It was also showed that the proposed technique could provide a rapid and simple quantitative analysis of two protein samples in a mixture. Finally, we successfully applied the developed technique for the quantification of soymilk in adulterated infant formula. The TDP-MRBE opens up a new window for the detection of adulteration ratio of the poor food (milk) in blended high quality one.

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features.

Drugtarget interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drugtarget interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drugtarget interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68 % and 84.72 % for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.

Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures.

The kinase family is one of the largest target families in the human genome. The family's key function in signal transduction for all organisms makes it a very attractive target class for the therapeutic interventions in many diseases states such as cancer, diabetes, inflammation and arthritis. A first step toward accelerating kinase drug discovery process is to fast identify whether a chemical and a kinase interact or not. Experimentally, these interactions can be identified by in vitro binding assay - an expensive and laborious procedure that is not applicable on a large scale. Therefore, there is an urgent need to develop statistically efficient approaches for identifying kinase-inhibitor interactions. For the first time, the quantitative binding affinities of kinase-inhibitor pairs are differentiated as a measurement to define if an inhibitor interacts with a kinase, and then a chemogenomics framework using an unbiased set of general integrated features (drug descriptors and protein descriptors) and random forest (RF) is employed to construct a predictive model which can accurately classify kinase-inhibitor pairs. Our results show that RF with integrated features gave prediction accuracy of 93.76%, sensitivity of 92.26%, and specificity of 95.27%, respectively. The results are superior to those by only considering two separated spaces (chemical space and protein space), demonstrating that these integrated features contribute cooperatively. Based on the constructed model, we provided a high confidence list of drug-target associations for subsequent experimental investigation guidance at a low false discovery rate.

Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach.

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.

[Effects of alternative furrow irrigation and nitrogen application rate on photosynthesis, growth, and yield of cucumber in solar greenhouse].

This paper studied the effects of alternative furrow irrigation and nitrogen (N) application rate (no N, optimal N, and conventional N) on the photosynthesis, growth characteristics, yield formation, and fruit quality of cucumber (Cucumis sativus) cultivar Jinyu No. 5 in a solar greenhouse in winter-spring growth season and autumn-winter season. Under alternative furrow irrigation, the net photosynthetic rate of upper, middle, eand lower leaves was appreciably lower and the transpiration rate decreased significantly, and the transient water use efficiency of upper and middle leaves improved, as compared with those under conventional irrigation. Stomatal factor was the limiting factor of photosynthesis under alternative furrow irrigation. The photosynthesis and transient water use efficiency of functional leaves under alternative furrow irrigation increased with increasing N application rate. Comparing with conventional irrigation, alternative furrow irrigation decreased leaf chlorophyll content and plant biomass, but increased root biomass, root/shoot ratio, and dry matter allocation in root and fruit. The economic output under alternative furrow irrigation was nearly the same as that under conventional irrigation, whereas the water use efficiency for economic yield increased significantly, suggesting the beneficial effects of alternative furrow irrigation on root development and fruit formation. With the increase of N application rate, the leaf chlorophyll content, chlorophyll a/b, specific leaf mass, plant biomass, economic yield, and fruit Vc and soluble sugar contents under alternative furrow irrigation increased, but no significant difference was observed between the treatments optimal N and conventional N. N application had little effects on the water use efficiency for economic yield. The economic yield and biomass production of the cucumber were significantly higher in winter-spring growth season than in autumn-winter growth season.