Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays

Development and photo-properties and intracellular behavior of visible-light-responsive molecule localizing to organelles of living cell

Visible-light-responsive azobenzene derivative in which a functional group having cell membrane permeability and a fluorophore were bonded was synthesized. This compound localized to the hydrophobic part in the lipid membrane of the liposome, and when the light corresponding to the transition absorption of azobenzene was irradiated, morphological change of the liposome was observed. When this compound was loaded into living cells, this molecule localized to the lysosome and when irradiated with light of the same wavelength caused cell death. These observed changes are thought to be due to photoisomerization of azobenzene derivatives.

Graphical abstract

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

RoFDT: Identification of Drug–Target Interactions from Protein Sequence and Drug Molecular Structure Using Rotation Forest

As the basis for screening drug candidates, the identification of drug–target interactions (DTIs) plays a crucial role in the innovative drugs research. However, due to the inherent constraints of small-scale and time-consuming wet experiments, DTI recognition is usually difficult to carry out. In the present study, we developed a computational approach called RoFDT to predict DTIs by combining feature-weighted Rotation Forest (FwRF) with a protein sequence. In particular, we first encode protein sequences as numerical matrices by Position-Specific Score Matrix (PSSM), then extract their features utilize Pseudo Position-Specific Score Matrix (PsePSSM) and combine them with drug structure information-molecular fingerprints and finally feed them into the FwRF classifier and validate the performance of RoFDT on Enzyme, GPCR, Ion Channel and Nuclear Receptor datasets. In the above dataset, RoFDT achieved 91.68%, 84.72%, 88.11% and 78.33% accuracy, respectively. RoFDT shows excellent performance in comparison with support vector machine models and previous superior approaches. Furthermore, 7 of the top 10 DTIs with RoFDT estimate scores were proven by the relevant database. These results demonstrate that RoFDT can be employed to a powerful predictive approach for DTIs to provide theoretical support for innovative drug discovery.

Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving

Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI.

Synthesis and Toxicological Effect of Some New Pyrrole Derivatives as Prospective Insecticidal Agents against the Cotton Leafworm, Spodoptera littoralis (Boisduval)

Herein, a series of biologically active pyrrole derivatives, namely 2-[(3-cyano-5-aryl-1H-pyrrol-2-yl)thio]acetic acids 2a-c, 2-[(2-hydroxyethyl)-thio]-5-aryl-1H-pyrrole-3-carbonitriles 3a-c, and 2-[(2-amino-ethyl)thio]-5-aryl-1H-pyrrole-3-carbonitriles 4a-c, 2,2'-disulfanediylbis(5-aryl-1H-pyrrole-3-carbonitriles) 5a-c, 2-((3-cyano-5-aryl-1H-pyrrol-2-yl)thio)acetates 6a-c, 2-[(3-cyano-5-phenyl-1H-pyrrol-2-yl)thio]acetohydrazides 7a-c, and 2-{2-[(3-cyano-5-aryl-1H-pyrrol-2-yl)thio]acetyl}-N-phenyl-hydrazinecarbothioamides 8a-c, as insecticidal agents, were synthesized via adaptable, smoothly accessible 2-(2-oxo-2-arylylethyl)malononitriles 1a-c. The structures were proved using infrared (IR), nuclear magnetic resonance (NMR), and mass spectrum (MS) techniques. Under laboratory conditions, the toxicological characteristics were tested towards Spodoptera littoralis, cotton leafworm insect type. In respect to the LC₅₀ values, compounds 6a, 7a, 8c, and 3c possess the highest insecticidal bioefficacy, with values of 0.5707, 0.1306, 0.9442, and 5.883 ppm, respectively. The study paves the way towards discovering new materials for potential use as insecticidal active agents.

Bridging-BPs: a novel approach to predict potential drug-target interactions based on a bridging heterogeneous graph and BPs2vec

Predicting drug-target interactions (DTIs) is a convenient strategy for drug discovery. Although various computational methods have been put forward in recent years, DTIs prediction is still a challenging task. In this paper, based on indirect prior information (we term them as mediators), we proposed a new model, called Bridging-BPs (bridging paths), for DTIs prediction. Specifically, we regarded linkage process between mediators and DTs (drugs and proteins) as 'bridging' and source (drug)-mediators-destination (protein) as bridging paths. By integrating various bridging paths, we constructed a bridging heterogeneous graph for DTIs. After that, an improved graph-embedding algorithm-BPs2vec-was designed to capture deep topological features underlying the bridging graph, thereby obtaining the low-dimensional node vector representations. Then, the vector representations were fed into a Random Forest classifier to train and score the probability, outputting the final classification results for potential DTIs. Under 5-fold cross validation, our method obtained AUPR of 88.97% and AUC of 88.63%, suggesting that Bridging-BPs could effectively mine the link relationships hidden in indirect prior information and it significantly improved the accuracy and robustness of DTIs prediction without direct prior information. Finally, we confirmed the practical prediction ability of Bridging-BPs by case studies.

Network-Based Approaches for Drug Repositioning

With deep learning creeping up into the ranks of big data, new models based on deep learning and massive data have made great leaps forward rapidly in the field of drug repositioning. However, there is no relevant review to summarize the transformations and development process of models and their data in the field of drug repositioning. Among all the computational methods, network-based methods play an extraordinary role. In view of these circumstances, understanding and comparing existing network-based computational methods applied in drug repositioning will help us recognize the cutting-edge technologies and offer valuable information for relevant researchers. Therefore, in this review, we present an interpretation of the series of important network-based methods applied in drug repositioning, together with their comparisons and development process.

Bio-instructive materials on-demand - combinatorial chemistry of peptoids, foldamers, and beyond

Combinatorial chemistry allows for the rapid synthesis of large compound libraries for high throughput screenings in biology, medicinal chemistry, or materials science. Especially compounds from a highly modular design are interesting for the proper investigation of structure-to-activity relationships. Permutations of building blocks result in many similar but unique compounds. The influence of certain structural features on the entire structure can then be monitored and serve as a starting point for the rational design of potent molecules for various applications. Peptoids, a highly diverse class of bioinspired oligomers, suit perfectly for combinatorial chemistry. Their straightforward synthesis on a solid support using repetitive reaction steps ensures easy handling and high throughput. Applying this modular approach, peptoids are readily accessible, and their interchangeable side-chains allow for various structures. Thus, peptoids can easily be tuned in their solubility, their spatial structure, and, consequently, their applicability in various fields of research. Since their discovery, peptoids have been applied as antimicrobial agents, artificial membranes, molecular transporters, and much more. Studying their three-dimensional structure, various foldamers with fascinating, unique properties were discovered. This non-comprehensive review will state the most interesting discoveries made over the past years and arouse curiosity about what may come.

Multimorbidity prediction using link prediction

Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity to develop a further particular disease in the future is one of the key challenges in multimorbidity research. In this paper we are using a network-based approach to analyze multimorbidity data and develop methods for predicting diseases that a patient is likely to develop. The multimorbidity data is represented using a temporal bipartite network whose nodes represent patients and diseases and a link between these nodes indicates that the patient has been diagnosed with the disease. Disease prediction then is reduced to a problem of predicting those missing links in the network that are likely to appear in the future. We develop a novel link prediction method for static bipartite network and validate the performance of the method on benchmark datasets. By using a probabilistic framework, we then report on the development of a method for predicting future links in the network, where links are labelled with a time-stamp. We apply the proposed method to three different multimorbidity datasets and report its performance measured by different performance metrics including AUC, Precision, Recall, and F-Score.

Pseudo Natural Products-Chemical Evolution of Natural Product Structure

Pseudo-natural products (PNPs) combine natural product (NP) fragments in novel arrangements not accessible by current biosynthesis pathways. As such they can be regarded as non-biogenic fusions of NP-derived fragments. They inherit key biological characteristics of the guiding natural product, such as chemical and physiological properties, yet define small molecule chemotypes with unprecedented or unexpected bioactivity. We iterate the design principles underpinning PNP scaffolds and highlight their syntheses and biological investigations. We provide a cheminformatic analysis of PNP collections assessing their molecular properties and shape diversity. We propose and discuss how the iterative analysis of NP structure, design, synthesis, and biological evaluation of PNPs can be regarded as a human-driven branch of the evolution of natural products, that is, a chemical evolution of natural product structure.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Switching the Regioselectivity Access to Pyrroles and Isoquinolines from Ketoxime Acetates and Ynals

A novel formal [3+2] and [4+2] annulation of ketoxime acetates and ynals for the synthesis of pyrroles and isoquinolines has been developed. By simply switching the catalyst and solvent, the reaction proceeds via two pathways. The reactions are achieved under mild conditions with broad substrate scope and excellent regioselectivity.

Re-engineering natural products to engage new biological targets

Covering: up to 2020 Natural products have a long history in drug discovery, with their inherent biological activity often tailored by medicinal chemists to arrive at the final drug product. This process is illustrated by numerous examples, including the conversion of epothilone to ixabepilone, erythromycin to azithromycin, and lovastatin to simvastatin. However, natural products are also fruitful starting points for the creation of complex and diverse compounds, especially those that are markedly different from the parent natural product and accordingly do not retain the biological activity of the parent. The resulting products have physiochemical properties that differ considerably when compared to traditional screening collections, thus affording an opportunity to discover novel biological activity. The synthesis of new structural frameworks from natural products thus yields value-added compounds, as demonstrated in the last several years with multiple biological discoveries emerging from these collections. This Highlight details a handful of these studies, describing new compounds derived from natural products that have biological activity and cellular targets different from those evoked/engaged by the parent. Such re-engineering of natural products offers the potential for discovering compounds with interesting and unexpected biological activity.

Integrating bioassay data for improved prediction of drug-target interaction

Identifying drug targets is one of the major tasks in drug discovery. As experimental identification of targets is rather challenging, development of computational methods is necessary for efficient identification of drug-target interaction. Traditional computational method, such as docking, is based solely on the chemical structure, which is not available for most of the targets. On the other hand, bioassay data might contain information helpful for prediction of drug-target interaction. In this study, a feature enrichment method integrating bioassay and chemical structure data was developed to predict drug-target interaction. Using a large-scale benchmark on the datasets, we demonstrated that the model adopting integrated fingerprint outperformed the one using chemical fingerprint. Influence of the false positive hits in bioassays and algorithm-related factors on the model performance were also investigated. The results suggested that prediction by using integrated fingerprint was robust to false positive hits, the choice of classifiers, and different random splits of the datasets.

Identification of potential drug-targets by combining evolutionary information extracted from frequency profiles and molecular topological structures

Identifying interactions among drug compounds and target proteins is the basis of drug research and plays a crucial role in drug discovery. However, determining drug-target interactions (DTIs) and potential protein-compound interactions by biological experiment-based method alone is a very complicated, expensive, and time-consuming process. Hence, there is an intense motivation to design in silico prediction methods to overcome these obstacles. In this work, we designed a novel in silico strategy to predict proteome-scale DTIs based on the assumption that DTI pairs can be expressed through the evolutionary information derived from frequency profiles and drugs' structural properties. To achieve this, drug molecules are encoded into the substructure fingerprints to represent certain fragments; target proteins are first converted into position-specific scoring matrix (PSSM) and then encoded as 2-dimensional principal component analysis (2DPCA) descriptors. In the prediction phase, the feature weighted rotation forest (RF) classifier is used to estimate whether drug and target interact with each other on four benchmark datasets, including Enzymes, Ion Channels, GPCRs, and Nuclear Receptors. The prediction accuracy of cross-validation on the four datasets is 95.40%, 88.82%, 85.67%, and 82.22%, respectively. In order to have a clearer assessment of the proposed approach, we compared it with the discrete cosine transform (DCT) descriptor model, support vector machine (SVM) classifier model, and existing excellent approaches, including DBSI, NetCBP, KBMF2K, SIMCOMP, and RFDT. The excellent results of the experiment indicated that the proposed approach can effectively improve the DTI prediction accuracy and can be used as a practical tool for the research and design of new drugs.

Network-based prediction of drug-target interactions using an arbitrary-order proximity embedded deep forest

MOTIVATION

Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs.

RESULTS

In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol).

AVAILABILITY AND IMPLEMENTATION

Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF.

FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction

The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/.

Drug-target Interaction Prediction by Metapath2vec Node Embedding in Heterogeneous Network of Interactions

Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods is an important area of drug research, which brings about a broad prospect for fast and low-risk drug development. By accurate prediction of drugs and targets interactions scientists can scale-down huge experimental space and reduce the costs and help to faster drug development as well as predicting the side effects and potential function of new drugs. Many approaches have been taken by researchers to solve DTI problem and enhance the accuracy of methods. State-of-the-art approaches are based on various techniques, such as deep learning methods-like stacked auto-encoder-, matrix factorization, network inference, and ensemble methods. In this work, we have taken a new approach based on node embedding in a heterogeneous interaction network to obtain the representation of each node in the interaction network and then use a binary classifier such as logistic regression to solve this prominent problem in the pharmaceutical industry. Most introduced network-based methods use a homogeneous network of interactions as their input data whereas in the real word problem there exist other informative networks to help to enhance the prediction and by considering the homogeneous networks we lose some precious network information. Hence, in this work, we have tried to work on the heterogeneous network and have improved the accuracy of methods in comparison to baseline methods.

SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction

Drug discovery is an academical and commercial process of global importance. Accurate identification of drug-target interactions (DTIs) can significantly facilitate the drug discovery process. Compared to the costly, labor-intensive and time-consuming experimental methods, machine learning (ML) plays an ever-increasingly important role in effective, efficient and high-throughput identification of DTIs. However, upstream feature extraction methods require tremendous human resources and expert insights, which limits the application of ML approaches. Inspired by the unsupervised representation learning methods like Word2vec, we here proposed SPVec, a novel way to automatically represent raw data such as SMILES strings and protein sequences into continuous, information-rich and lower-dimensional vectors, so as to avoid the sparseness and bit collisions from the cumbersomely manually extracted features. Visualization of SPVec nicely illustrated that the similar compounds or proteins occupy similar vector space, which indicated that SPVec not only encodes compound substructures or protein sequences efficiently, but also implicitly reveals some important biophysical and biochemical patterns. Compared with manually-designed features like MACCS fingerprints and amino acid composition (AAC), SPVec showed better performance with several state-of-art machine learning classifiers such as Gradient Boosting Decision Tree, Random Forest and Deep Neural Network on BindingDB. The performance and robustness of SPVec were also confirmed on independent test sets obtained from DrugBank database. Also, based on the whole DrugBank dataset, we predicted the possibilities of all unlabeled DTIs, where two of the top five predicted novel DTIs were supported by external evidences. These results indicated that SPVec can provide an effective and efficient way to discover reliable DTIs, which would be beneficial for drug reprofiling.

Stereocontrolled transformations of cyclohexadienone derivatives to access stereochemically rich and natural product-inspired architectures

The last two decades or so have witnessed an upsurge in defining the art of designing complex natural products and nature-inspired molecules. Throughout these decades, fundamental insights into stereocontrolled, step-economic and atom-economical synthesis principles were achieved by the numerous synthetic accomplishments particularly in diversity-oriented synthesis (DOS). This has empowered the visualization of the third dimension in synthetic design and thus has resulted in a dramatic increase with today's diversity-oriented synthesis (DOS) at the forefront enabling access to diverse scaffolds with a high degree of stereochemical and skeletal complexity. To this end, a starting material-based approach is one of the powerful tools utilized in DOS that allows rapid access to molecular architectures with a high sp3 content. Skeletal and stereochemical diversity is often paramount for the selective modulation of the biological function of a complementary protein in the biological space. In this context, stereocontrolled transformation of cyclohexadienone scaffolds has positioned itself as a powerful platform for the rapid generation of stereochemically enriched and natural product-inspired compound collections. In this review, we cover multidirectional synthetic strategies that utilized cyclohexadienone derivatives as pluripotent building blocks en route for the construction of novel chemical space.

Domino Transformations of Ene/Yne Tethered Salicylaldehyde Derivatives: Pluripotent Platforms for the Construction of High sp3 Content and Privileged Architectures

Diversity-oriented synthesis (DOS) has become a powerful synthetic tool that facilitates the construction of nature-inspired and privileged chemical space, particularly for sp³ -rich non-flat scaffolds, which are needed for phenotypic screening campaigns. These diverse compound collections led to the discovery of novel chemotypes that can modulate the protein function in underrepresented biological space. In this context, starting material-driven DOS is one of the most important tools used to build diverse compound libraries with rich stereochemical and scaffold diversity. To this end, ene/yne tethered salicylaldehyde derivatives have emerged as a pluripotent chemical platform, the products of which led to the construction of a privileged chemical space with significant biological activities. In this review, various domino transformations employing o-alkene/alkyne tethered aryl aldehyde/ketone platforms are described and discussed, with emphasis on the period from 2011 to date.

Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees

Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5–98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.

Synthetic ligands for PreQ1 riboswitches provide structural and mechanistic insights into targeting RNA tertiary structure

Riboswitches are naturally occurring RNA aptamers that regulate gene expression by binding to specific small molecules. Riboswitches control the expression of essential bacterial genes and are important models for RNA-small molecule recognition. Here, we report the discovery of a class of synthetic small molecules that bind to PreQ₁ riboswitch aptamers. These molecules bind specifically and reversibly to the aptamers with high affinity and induce a conformational change. Furthermore, the ligands modulate riboswitch activity through transcriptional termination despite no obvious chemical similarity to the cognate ligand. X-ray crystallographic studies reveal that the ligands share a binding site with the cognate ligand but make different contacts. Finally, alteration of the chemical structure of the ligand causes changes in the mode of RNA binding and affects regulatory function. Thus, target- and structure-based approaches can be used to identify and understand the mechanism of synthetic ligands that bind to and regulate complex, folded RNAs.

RNA sensors—Riboswitches—respond to the binding of small molecules ligands through structure modification. Here the authors identify synthetic small molecules that bind and regulate the activity of PreQ₁ Riboswitches despite having no obvious chemical similarity to the cognate ligand.

Rapid selection of aptamers based on protein microarray

We report a novel method for the efficient screening of aptamers from a complex ssDNA library based on a microarray chip, which was named Microarray-SELEX. In this method, the target protein (lactoferrin) and negative proteins (α-lactalbumin, β-lactoglobulin, bovine serum albumin, and casein) were each dotted and immobilized on a slide to form a protein microarray. Moreover, the library was added to this chip to interact with negative proteins, then with the target protein. The process of SELEX could be monitored on-line using a fluorescent microarray scanner and the whole process was performed in only six rounds. Finally, five aptamers (YFL-1, YFL-4, YFL-5, YFL-6 and YFL-7) were obtained, which showed good specificity towards lactoferrin in the presence of negative proteins. The equilibrium dissociation constants (K d) of the aptamers were in the nanomolar range. Briefly, Microarray-SELEX is a rapid, easy, sensitive and efficient method for screening aptamers.

Multidirectional desymmetrization of pluripotent building block en route to diastereoselective synthesis of complex nature-inspired scaffolds

Octahydroindolo[2,3-a]quinolizine ring system forms the basic framework comprised of more than 2000 distinct family members of natural products. Despite the potential applications of this privileged substructure in drug discovery, efficient, atom-economic and modular strategies for its assembly, is underdeveloped. Here we show a one-step build/couple/pair strategy that uniquely allows access to diverse octahydroindolo[2,3-a]quinolizine scaffolds with more than three contiguous chiral centers and broad distribution of molecular shapes via desymmetrization of the oxidative-dearomatization products of phenols. The cascade demonstrates excellent diastereoselectivity, and the enantioselectivity exceeded 99% when amino acids are used as chiral reagents. Furthermore, two diastereoselective reactions for the synthesis of oxocanes and piperazinones, is reported. Phenotypic screening of the octahydroindolo[2,3-a]quinolizine library identifies small molecule probes that selectively suppress mitochondrial membrane potential, ATP contents and elevate the ROS contents in hepatoma cells (Hepa1-6) without altering the immunological activation or reprogramming of T- and B-cells, a promising approach to cancer therapy.

A Brief Overview of Two Major Strategies in Diversity-Oriented Synthesis: Build/Couple/Pair and Ring-Distortion

In the interdisciplinary research field of chemical biology and drug discovery, diversity-oriented synthesis (DOS) has become indispensable in the construction of novel small-molecule libraries rich in skeletal and stereochemical diversity. DOS aims to populate the unexplored chemical space with new potential bioactive molecules via forward synthetic analysis. Since the introduction of this concept by Schreiber, DOS has evolved along with many significant breakthroughs. It is therefore important to understand the key DOS strategies to build molecular diversity with maximized biological relevancy. Due to the length limitations of this mini review, we briefly discuss the recent DOS plans using build/couple/pair (B/C/P) and ring-distortion strategies for the synthesis of major biologically relevant target molecules like natural products and their related compounds, macrocycles, and privileged structures.

iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting

Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method found in the literature in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued as suitable metric for comparison for imbalanced datasets similar to the one studied here. Our reported results show the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is implemented and made publicly available at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/.

Divergent reactivity of α-azidochalcones with metal β-diketonates: tunable synthesis of substituted pyrroles and indoles

A divergent reactivity of α-azidochalcones with metal β-diketonates for the synthesis of substituted pyrroles and indoles is described.

The Expanding World of Small Molecule Microarrays

Speed and throughput are vital ingredients for discovery driven, "-omics" research. The small molecule microarray (SMM) succeeds at delivering phenomenal screening throughput and versatility. The concept at the heart of the technology is elegant, yet simple: by presenting large collections of molecules in high density on a flat surface, one is able to interrogate all possible interactions with desired targets, in just a single step. SMMs have become established as the choice platform for screening, lead discovery, and molecular characterization. This introduction describes the principles governing microarray construction and use, focusing on practical challenges faced when conducting SMM experiments. It will explain the key design considerations and lay the foundation for the chapters that follow. (An earlier version of this chapter appeared in Small Molecule Microarrays: Methods and Protocols, published in 2010.).

Temporally resolved direct delivery of second messengers into cells using nanostraws

Second messengers are biomolecules with the critical role of conveying information to intracellular targets. They are typically membrane-impermeable and only enter cells through tightly regulated transporters. Current methods for manipulating second messengers in cells require preparation of modified cell lines or significant disruptions in cell function, especially at the cell membrane. Here we demonstrate that 100 nm diameter 'nanostraws' penetrate the cell membrane to directly modulate second messenger concentrations within cells. Nanostraws are hollow vertical nanowires that provide a fluidic conduit into cells to allow time-resolved delivery of the signaling ion Ca(2+) without chemical permeabilization or genetic modification, minimizing cell perturbation. By integrating the nanostraw platform into a microfluidic device, we demonstrate coordinated delivery of Ca(2+) ions into hundreds of cells at the time scale of several seconds with the ability to deliver complex signal patterns, such as oscillations over time. The diffusive nature of nanostraw delivery gives the platform unique versatility, opening the possibility for time-resolved delivery of any freely diffusing molecules.

Drug-target interaction prediction: databases, web servers and computational models

Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.