Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods

BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions.ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.     

Design of a tripartite network for the prediction of drug targets

Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.     

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets.     

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.     

色谱技术在药物-血浆蛋白相互作用研究中的应用进展

小分子药物进入人体血液循环系统后与人血清白蛋白(HSA)、α₁ -酸性糖蛋白(AGP)等血浆蛋白存在广泛的相互作用,这些相互作用深刻影响药物在体内的分布及其与靶标蛋白的结合,进而影响药物效应的发挥。深入探究药物与血浆蛋白间的相互作用对于候选药物的成药性优化、新药研发、联合用药的风险评控等意义重大。而发展高效、灵敏、准确的分析检测方法是开展药物-血浆蛋白相互作用研究的关键。近年来,色谱技术由于其高通量、高分离性能、高灵敏度等特点在该领域得到了广泛的应用,包括测定血浆蛋白翻译后修饰对药物结合的影响,多种药物的竞争性结合等。其中,高效亲和色谱(HPAC)和毛细管电泳(CE)应用最为广泛,能够通过多种分析方法获取结合常数、结合位点数、解离速率常数等相互作用信息。该文着重综述了HPAC和CE在药物-血浆蛋白相互作用研究中的常用策略及最新研究进展,包括HPAC中常用的前沿色谱法、竞争洗脱法、超快亲和提取法、峰值分析法和峰衰减分析法,以及CE中常用的亲和毛细管电泳法(ACE)和毛细管电泳前沿分析法(CE-FA)等。最后,该文还对当前色谱方法存在的不足进行了总结,并对色谱技术在药物-血浆蛋白相互作用研究领域的应用前景和发展方向进行了展望。     

CABIN: collective analysis of biological interaction networks

The importance of understanding biological interaction networks has fueled the development of numerous interaction data generation techniques, databases and prediction tools. However, not all prediction tools and databases predict interactions with one hundred percent accuracy. Generation of high-confidence interaction networks formulates the first step towards deciphering unknown protein functions, determining protein complexes and inventing drugs. The CABIN: Collective Analysis of Biological Interaction Networks software is an exploratory data analysis tool that enables analysis and integration of interactions evidence obtained from multiple sources, thereby increasing the confidence of computational predictions as well as validating experimental observations. CABIN has been written in Java and is available as a plugin for Cytoscape--an open source network visualization tool.     

Drug effect prediction by polypharmacology-based interaction profiling

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation.     

Modification of the thermal unfolding pathways of myoglobin upon drug interaction in different aqueous media

In this work, we have analyzed the influence of two structurally related phenothiazine drugs, promazine and triflupromazine hydrochlorides, when bound to myoglobin, a model protein, and how the drug concentration and solution conditions may affect the denaturation process of this protein. In this manner, we derive the thermodynamic quantities of the unfolding process by using a spectroscopic technique such as UV-vis spectroscopy at different drugs concentrations and at pH 2.5, 5.5, and 9.0. To do this, a thermodynamic model was used which included experimental data corresponding to the pre- and post-transition into the observable transition. It has been found that both drugs play a destabilizing role for the protein, at least at low concentrations. In addition, at acidic pH and higher drug concentrations, a stabilizing effect can be observed, which may be related to the formation of some type of protein refolding, subsequent aggregation, or both. The reason for this behavior has been suggested to be the different protein conformations at acidic pH, the increase of solvent-exposed hydrophobic and hydrophilic residues after denaturation and/or binding, and the different strength of drug-protein interactions when changing the solution conditions. For this reason, thermodynamic quantities such as Gibbs energies, DeltaG, and entropies of unfolding, DeltaS(m), increase as the solution pH increases provided that additional solvent-exposed hydrophobic residues are present, which were previously buried at room temperature. Moreover, the larger binding affinity at pH 9.0 due to enhanced electrostatic interactions between protein and drug molecules (drug and protein differ in their net electrical charge) additionally collaborates to this residue exposition to solvent as a consequence of the alteration of protein conformation as due to drug binding. Comparison of thermodynamic data between promazine and triflupromazine hydrochlorides also shows that drug-protein affinity and hydrophobicity also affect the thermodynamic denaturation parameters.     

Simple and Rapid Hollow Fiber Liquid Phase Microextraction Followed by High Performance Liquid Chromatography Method for Determination of Drug-protein Binding

A method was established using hollow fiber-liquid phase microextraction(HF-LPME) followed by high performance liquid chromatography(HPLC) to determine the concentration of the free(unbound) drug in the solution of the drug and protein. Measurements of drug-protein binding ratios and free drug concentrations were then analyzed with the Klotz equation to determine the equilibrium binding constant and number of binding sites for drug-protein interaction. The optimized method allows one to perform the efficient extraction and separation of free drug from protein-bound drug, protein, and other interfering substances. This approach was used to characterize the binding of the anticholinergic drugs atropine sulfate and scopolamine hydrobromide to proteins in human plasma and bovine serum albumin(BSA). The results demonstrate the utility of HF-LPME method for measuring free drug concentrations in protein-drug mixtures and determining the protein binding parameters of a pharmacologically important class of drugs.     

Capturing structure-activity relationships from chemogenomic spaces

Modeling off-target effects is one major goal of chemical biology, particularly in its applications to drug discovery. Here, we describe a new approach that allows the extraction of structure-activity relationships from large chemogenomic spaces starting from a single chemical structure. Several public source databases, offering a vast amount of data on structure and activity for a large number of different targets, have been investigated for their usefulness in automated structure-activity relationships (SAR) extraction. SAR tables were constructed by assembling similar structures around each query structure that have an activity record for a particular target. Quantitative series enrichment analysis (QSEA) was applied to these SAR tables to identify trends and to transform these trends into topomer CoMFA models. Overall more than 1700 SAR tables with topomer CoMFA models have been obtained from the ChEMBL, PubChem, and ChemBank databases. These models were able to highlight the structural trends associated with various off-target effects of marketed drugs, including cases where other structural similarity metrics would not have detected an off-target effect. These results indicate the usefulness of the QSEA approach, particularly whenever applicable with public databases, in providing a new means, beyond a simple similarity between ligand structures, to capture SAR trends and thereby contribute to success in drug discovery.     

Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. 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 with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR methodology. They open the way to a host of new investigations on the diversity analysis and prediction of drug–target interactions.     

Drug–target interaction prediction by integrating multiview network data

Drug–target interaction (DTI) prediction is a challenging step in further drug repositioning, drug discovery and drug design. The advent of high-throughput technologies brings convenience to the development of DTI prediction methods. With the generation of a high number of data sets, many mathematical models and computational algorithms have been developed to identify the potential drug–target pairs. However, most existing methods are proposed based on the single view data. By integrating the drug and target data from different views, we aim to get more stable and accurate prediction results.In this paper, a multiview DTI prediction method based on clustering is proposed. We first introduce a model for single view drug–target data. The model is formulated as an optimization problem, which aims to identify the clusters in both drug similarity network and target protein similarity network, and at the same time make the clusters with more known DTIs be connected together. Then the model is extended to multiview network data by maximizing the consistency of the clusters in each view. An approximation method is proposed to solve the optimization problem. We apply the proposed algorithms to two views of data. Comparisons with some existing algorithms show that the multiview DTI prediction algorithm can produce more accurate predictions. For the considered data set, we finally predict 54 possible DTIs. From the similarity analysis of the drugs/targets, enrichment analysis of DTIs and genes in each cluster, it is shown that the predicted DTIs have a high possibility to be true.     

Drug-human serum albumin binding studied by capillary electrophoresis with electrochemiluminescence detection

A new technique for investigating drug-protein binding was developed employing capillary electrophoresis (CE) coupled with tris(2,2'-bipyridyl) ruthenium(II) [Ru(bpy)(3) (2+)] electrochemiluminescence (ECL) (CE-ECL) detection after equilibrium dialysis. Three basic drugs, namely pridinol, procyclidine and its analogue trihexyphenidyl, were successfully separated by capillary zone electrophoresis with end-column Ru(bpy)(3) (2+) ECL detection. The relative drug binding to human serum albumin (HSA) for each single drug as well as for the three drugs binding simultaneously was calculated. It was found that the three antiparkinsonian drugs compete for the same binding site on HSA. This work demonstrated that Ru(bpy)(3) (2+) CE-ECL can be a suitable technique for studying drug-protein binding.     

Epigenetic drug (XL019) JAK2 inhibitor increases mitochondrial function in brown adipocytes by upregulating mitochondrial uncoupling protein 1 (UCP1), screening of epigenetic drug libraries,cell viability,and in-silico studies

BackgroundAt present lacking of effective and safe anti-obesity drugs available leads to initiate obesity worldwide that promotes several diseases like cardiovascular diseases, liver diseases, and NASH. The development of new therapeutics is an emergency demand to cure obesity-related diseases. Mitochondrial uncoupling protein 1 (UCP1) gene could be a potential target to develop new drug moieties that can treat obesity-related diseases.MethodsWe used a GFP reporter cell line to screen epigenetic drug libraries to identify UCP1 regulators that could be effective drug candidates to treat obesity-related diseases. In this study, we employed an in-silico study that revealed drug-protein interaction and stability of drugs with protein.ResultsScreening epigenetic drug libraries, we identified XL019 significant TYK2, JAK2, and JAK3, inhibitors that can significantly promote UCP1 gene expression in brown adipocytes. Here, we found that XL019 plays a vital role to modulates mitochondrial function and could be beneficial against obesity. Further analysis shows that XL019 significantly improved mitochondrial ATP production and mitochondrial DNA copy number of adipocytes compared with the control group. The in-silico study demonstrated drug-protein interaction and binding side with UCP1 gene. Thus XL019 improves mitochondrial function that would be effective drug candidate to treat metabolic diseases and obesity-related diseases.ConclusionIn this study, we confirm the potential effect of the XL019 epigenetic drug that modulates mitochondrial function and in-silico study on drug-likeness, stability, and safety profile. Further investigation will reveal the new insight into the mechanism of action against obesity, metabolic diseases ( NASH, Fibrosis, cardiac diseases and so on), by modulation of the mitochondrial UCP1 gene and mitochondrial function.     

Scoring multiple features to predict drug disease associations using information fusion and aggregation

Prediction of drug–disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug–disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug–drug level and the drug–disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug–disease interactions. The method was validated against a well-established drug–disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.     

Integrated decision support for assessing chemical liabilities

Chemical liabilities, such as adverse effects and toxicity, have a major impact on today's drug discovery process. In silico prediction of chemical liabilities is an important approach which can reduce costs and animal testing by complementing or replacing in vitro and in vivo liability models. There is a lack of integrated, extensible decision support systems for chemical liability assessment which run quickly and have easily interpretable results. Here we present a method which integrates similarity searches, structural alerts, and QSAR models which all are available from the Bioclipse workbench. Emphasis has been placed on interpretation of results, and substructures which are important for predictions are highlighted in the original chemical structures. This allows for interactively changing chemical structures with instant visual feedback and can be used for hypothesis testing of single chemical structures as well as compound collections. The system has a clear separation between methods and data, and the extensible architecture enables straightforward extension via addition of more plugins (such as new data sets and computational models). We demonstrate our method on three important safety end points: mutagenicity, carcinogenicity, and aryl hydrocarbon receptor (AhR) activation. Bioclipse and the decision support implementation are free, open source, and available from http://www.bioclipse.net/decision-support .     

活性导向的化学探针在氨基酸反应活性表征中的应用进展

原创药物的研制得益于蛋白质新靶标的发现,而新靶标的发现依赖于高可信度、高通量的药物-蛋白质相互作用分析方法。蛋白质作为生命功能的执行者,其表达量、空间定位与结构差异直接影响药效的发挥。目前,超过85%的蛋白质尚被认为是无法成药的,主要原因是缺少药物分子靶向的空腔以及相应的反应活性位点。因此,基于蛋白质组学层次实现对氨基酸反应活性位点的表征成为原创共价靶向药物设计的关键,也是克服难以成药靶标蛋白问题的关键。近年来,质谱技术的飞速发展极大地推动了基于蛋白质组学技术的药物-靶蛋白相互作用研究。其中基于活性的蛋白质组分析(ABPP)策略是利用活性位点导向的化学探针分子在复杂样品中实现功能状态酶和药物靶标等蛋白质的检测。基于化学探针的开发和质谱定量技术的发展,ABPP技术在氨基酸反应活性表征研究中展现出重要的应用潜力,将助力于药物新靶标的发现和药物先导化合物的开发。ABPP策略主要基于蛋白质的活性特征进行富集,活性探针作为ABPP策略的核心,近年来取得了飞速进展。该文回顾了ABPP策略的发展历程,重点介绍基于广谱活性探针的ABPP技术在多种氨基酸反应活性筛选领域的研究进展,并对其在药物靶点发现中...     

Detecting drug promiscuity using gaussian ensemble screening

Polypharmacology describes the binding of a ligand to multiple protein targets (a promiscuous ligand) or multiple diverse ligands binding to a given target (a promiscuous target). Pharmaceutical companies are discovering increasing numbers of both promiscuous drugs and drug targets. Hence, polypharmacology is now recognized as an important aspect of drug design. Here, we describe a new and fast way to predict polypharmacological relationships between drug classes quantitatively, which we call Gaussian Ensemble Screening (GES). This approach represents a cluster of molecules with similar spherical harmonic surface shapes as a Gaussian distribution with respect to a selected center molecule. Calculating the Gaussian overlap between pairs of such clusters allows the similarity between drug classes to be calculated analytically without requiring thousands of bootstrap comparisons, as in current promiscuity prediction approaches. We find that such cluster similarity scores also follow a Gaussian distribution. Hence, a cluster similarity score may be transformed into a probability value, or "p-value", in order to quantify the relationships between drug classes. We present results obtained when using the GES approach to predict relationships between drug classes in a subset of the MDL Drug Data Report (MDDR) database. Our results indicate that GES is a useful way to study polypharmacology relationships, and it could provide a novel way to propose new targets for drug repositioning.